Module astrapy.collection
Expand source code
# Copyright DataStax, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import asyncio
import json
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
import deprecation
from astrapy import __version__
from astrapy.api_options import CollectionAPIOptions
from astrapy.authentication import coerce_embedding_headers_provider
from astrapy.constants import (
DocumentType,
FilterType,
ProjectionType,
ReturnDocument,
SortType,
VectorType,
normalize_optional_projection,
)
from astrapy.core.db import AstraDBCollection, AsyncAstraDBCollection
from astrapy.core.defaults import DEFAULT_INSERT_NUM_DOCUMENTS
from astrapy.cursors import AsyncCursor, Cursor
from astrapy.database import AsyncDatabase, Database
from astrapy.exceptions import (
BulkWriteException,
CollectionNotFoundException,
CumulativeOperationException,
DataAPIFaultyResponseException,
DataAPIResponseException,
DeleteManyException,
InsertManyException,
MultiCallTimeoutManager,
TooManyDocumentsToCountException,
UpdateManyException,
base_timeout_info,
recast_method_async,
recast_method_sync,
)
from astrapy.info import CollectionInfo, CollectionOptions
from astrapy.meta import check_deprecated_vector_ize
from astrapy.results import (
BulkWriteResult,
DeleteResult,
InsertManyResult,
InsertOneResult,
UpdateResult,
)
if TYPE_CHECKING:
from astrapy.authentication import EmbeddingHeadersProvider
from astrapy.operations import AsyncBaseOperation, BaseOperation
logger = logging.getLogger(__name__)
DEFAULT_INSERT_MANY_CONCURRENCY = 20
DEFAULT_BULK_WRITE_CONCURRENCY = 10
def _prepare_update_info(statuses: List[Dict[str, Any]]) -> Dict[str, Any]:
reduced_status = {
"matchedCount": sum(
status["matchedCount"] for status in statuses if "matchedCount" in status
),
"modifiedCount": sum(
status["modifiedCount"] for status in statuses if "modifiedCount" in status
),
"upsertedId": [
status["upsertedId"] for status in statuses if "upsertedId" in status
],
}
if reduced_status["upsertedId"]:
if len(reduced_status["upsertedId"]) == 1:
ups_dict = {"upserted": reduced_status["upsertedId"][0]}
else:
ups_dict = {"upserteds": reduced_status["upsertedId"]}
else:
ups_dict = {}
return {
**{
"n": reduced_status["matchedCount"] + len(reduced_status["upsertedId"]),
"updatedExisting": reduced_status["modifiedCount"] > 0,
"ok": 1.0,
"nModified": reduced_status["modifiedCount"],
},
**ups_dict,
}
def _collate_vector_to_sort(
sort: Optional[SortType],
vector: Optional[VectorType],
vectorize: Optional[str],
) -> Optional[SortType]:
_vsort: Dict[str, Any]
if vector is None:
if vectorize is None:
return sort
else:
_vsort = {"$vectorize": vectorize}
if sort is None:
return _vsort
else:
raise ValueError(
"The `vectorize` and `sort` clauses are mutually exclusive."
)
else:
if vectorize is None:
_vsort = {"$vector": vector}
if sort is None:
return _vsort
else:
raise ValueError(
"The `vector` and `sort` clauses are mutually exclusive."
)
else:
raise ValueError(
"The `vector` and `vectorize` parameters cannot be passed at the same time."
)
def _is_vector_sort(sort: Optional[SortType]) -> bool:
if sort is None:
return False
else:
return "$vector" in sort or "$vectorize" in sort
def _collate_vector_to_document(
document0: DocumentType, vector: Optional[VectorType], vectorize: Optional[str]
) -> DocumentType:
if vector is None:
if vectorize is None:
return document0
else:
if "$vectorize" in document0:
raise ValueError(
"Cannot specify the `vectorize` separately for a document with "
"its '$vectorize' field already."
)
else:
return {
**document0,
**{"$vectorize": vectorize},
}
else:
if vectorize is None:
if "$vector" in document0:
raise ValueError(
"Cannot specify the `vector` separately for a document with "
"its '$vector' field already."
)
else:
return {
**document0,
**{"$vector": vector},
}
else:
raise ValueError(
"The `vector` and `vectorize` parameters cannot be passed at the same time."
)
def _collate_vectors_to_documents(
documents: Iterable[DocumentType],
vectors: Optional[Iterable[Optional[VectorType]]],
vectorize: Optional[Iterable[Optional[str]]],
) -> List[DocumentType]:
if vectors is None and vectorize is None:
return list(documents)
else:
_documents = list(documents)
_ndocs = len(_documents)
_vectors = list(vectors) if vectors else [None] * _ndocs
_vectorize = list(vectorize) if vectorize else [None] * _ndocs
if _ndocs != len(_vectors):
raise ValueError(
"The `documents` and `vectors` parameters must have the same length"
)
elif _ndocs != len(_vectorize):
raise ValueError(
"The `documents` and `vectorize` parameters must have the same length"
)
return [
_collate_vector_to_document(_doc, _vec, _vecize)
for _doc, _vec, _vecize in zip(_documents, _vectors, _vectorize)
]
class Collection:
"""
A Data API collection, the main object to interact with the Data API,
especially for DDL operations.
This class has a synchronous interface.
A Collection is spawned from a Database object, from which it inherits
the details on how to reach the API server (endpoint, authentication token).
Args:
database: a Database object, instantiated earlier. This represents
the database the collection belongs to.
name: the collection name. This parameter should match an existing
collection on the database.
namespace: this is the namespace to which the collection belongs.
If not specified, the database's working namespace is used.
api_options: An instance of `astrapy.api_options.CollectionAPIOptions`
providing the general settings for interacting with the Data API.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Examples:
>>> from astrapy import DataAPIClient, Collection
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_db = my_client.get_database(
... "https://01234567-....apps.astra.datastax.com"
... )
>>> my_coll_1 = Collection(database=my_db, name="my_collection")
>>> my_coll_2 = my_db.create_collection(
... "my_v_collection",
... dimension=3,
... metric="cosine",
... )
>>> my_coll_3a = my_db.get_collection("my_already_existing_collection")
>>> my_coll_3b = my_db.my_already_existing_collection
>>> my_coll_3c = my_db["my_already_existing_collection"]
Note:
creating an instance of Collection does not trigger actual creation
of the collection on the database. The latter should have been created
beforehand, e.g. through the `create_collection` method of a Database.
"""
def __init__(
self,
database: Database,
name: str,
*,
namespace: Optional[str] = None,
api_options: Optional[CollectionAPIOptions] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> None:
if api_options is None:
self.api_options = CollectionAPIOptions()
else:
self.api_options = api_options
additional_headers = self.api_options.embedding_api_key.get_headers()
self._astra_db_collection: AstraDBCollection = AstraDBCollection(
collection_name=name,
astra_db=database._astra_db,
namespace=namespace,
caller_name=caller_name,
caller_version=caller_version,
additional_headers=additional_headers,
)
# this comes after the above, lets AstraDBCollection resolve namespace
self._database = database._copy(
namespace=self._astra_db_collection.astra_db.namespace
)
def __repr__(self) -> str:
return (
f'{self.__class__.__name__}(name="{self.name}", '
f'namespace="{self.namespace}", database={self.database}, '
f"api_options={self.api_options})"
)
def __eq__(self, other: Any) -> bool:
if isinstance(other, Collection):
return all(
[
self._astra_db_collection == other._astra_db_collection,
self.api_options == other.api_options,
]
)
else:
return False
def __call__(self, *pargs: Any, **kwargs: Any) -> None:
raise TypeError(
f"'{self.__class__.__name__}' object is not callable. If you "
f"meant to call the '{self.name}' method on a "
f"'{self.database.__class__.__name__}' object "
"it is failing because no such method exists."
)
def _copy(
self,
*,
database: Optional[Database] = None,
name: Optional[str] = None,
namespace: Optional[str] = None,
api_options: Optional[CollectionAPIOptions] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> Collection:
return Collection(
database=database or self.database._copy(),
name=name or self.name,
namespace=namespace or self.namespace,
api_options=self.api_options.with_override(api_options),
caller_name=caller_name or self._astra_db_collection.caller_name,
caller_version=caller_version or self._astra_db_collection.caller_version,
)
def with_options(
self,
*,
name: Optional[str] = None,
embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None,
collection_max_time_ms: Optional[int] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> Collection:
"""
Create a clone of this collection with some changed attributes.
Args:
name: the name of the collection. This parameter is useful to
quickly spawn Collection instances each pointing to a different
collection existing in the same namespace.
embedding_api_key: optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`).
For some vectorize providers/models, if using header-based authentication,
specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider`
should be supplied.
collection_max_time_ms: a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
`find`, `delete_many`, `insert_many` and so on), it is strongly suggested
to provide a specific timeout as the default one likely wouldn't make
much sense.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Returns:
a new Collection instance.
Example:
>>> my_other_coll = my_coll.with_options(
... name="the_other_coll",
... caller_name="caller_identity",
... )
"""
_api_options = CollectionAPIOptions(
embedding_api_key=coerce_embedding_headers_provider(embedding_api_key),
max_time_ms=collection_max_time_ms,
)
return self._copy(
name=name,
api_options=_api_options,
caller_name=caller_name,
caller_version=caller_version,
)
def to_async(
self,
*,
database: Optional[AsyncDatabase] = None,
name: Optional[str] = None,
namespace: Optional[str] = None,
embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None,
collection_max_time_ms: Optional[int] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> AsyncCollection:
"""
Create an AsyncCollection from this one. Save for the arguments
explicitly provided as overrides, everything else is kept identical
to this collection in the copy (the database is converted into
an async object).
Args:
database: an AsyncDatabase object, instantiated earlier.
This represents the database the new collection belongs to.
name: the collection name. This parameter should match an existing
collection on the database.
namespace: this is the namespace to which the collection belongs.
If not specified, the database's working namespace is used.
embedding_api_key: optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`).
For some vectorize providers/models, if using header-based authentication,
specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider`
should be supplied.
collection_max_time_ms: a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
`find`, `delete_many`, `insert_many` and so on), it is strongly suggested
to provide a specific timeout as the default one likely wouldn't make
much sense.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Returns:
the new copy, an AsyncCollection instance.
Example:
>>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100))
77
"""
_api_options = CollectionAPIOptions(
embedding_api_key=coerce_embedding_headers_provider(embedding_api_key),
max_time_ms=collection_max_time_ms,
)
return AsyncCollection(
database=database or self.database.to_async(),
name=name or self.name,
namespace=namespace or self.namespace,
api_options=self.api_options.with_override(_api_options),
caller_name=caller_name or self._astra_db_collection.caller_name,
caller_version=caller_version or self._astra_db_collection.caller_version,
)
def set_caller(
self,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> None:
"""
Set a new identity for the application/framework on behalf of which
the Data API calls are performed (the "caller").
Args:
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Example:
>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
"""
logger.info(f"setting caller to {caller_name}/{caller_version}")
self._astra_db_collection.set_caller(
caller_name=caller_name,
caller_version=caller_version,
)
def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
"""
Get the collection options, i.e. its configuration as read from the database.
The method issues a request to the Data API each time is invoked,
without caching mechanisms: this ensures up-to-date information
for usages such as real-time collection validation by the application.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a CollectionOptions instance describing the collection.
(See also the database `list_collections` method.)
Example:
>>> my_coll.options()
CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
"""
logger.info(f"getting collections in search of '{self.name}'")
_max_time_ms = max_time_ms or self.api_options.max_time_ms
self_descriptors = [
coll_desc
for coll_desc in self.database.list_collections(max_time_ms=_max_time_ms)
if coll_desc.name == self.name
]
logger.info(f"finished getting collections in search of '{self.name}'")
if self_descriptors:
return self_descriptors[0].options # type: ignore[no-any-return]
else:
raise CollectionNotFoundException(
text=f"Collection {self.namespace}.{self.name} not found.",
namespace=self.namespace,
collection_name=self.name,
)
def info(self) -> CollectionInfo:
"""
Information on the collection (name, location, database), in the
form of a CollectionInfo object.
Not to be confused with the collection `options` method (related
to the collection internal configuration).
Example:
>>> my_coll.info().database_info.region
'eu-west-1'
>>> my_coll.info().full_name
'default_keyspace.my_v_collection'
Note:
the returned CollectionInfo wraps, among other things,
the database information: as such, calling this method
triggers the same-named method of a Database object (which, in turn,
performs a HTTP request to the DevOps API).
See the documentation for `Database.info()` for more details.
"""
return CollectionInfo(
database_info=self.database.info(),
namespace=self.namespace,
name=self.name,
full_name=self.full_name,
)
@property
def database(self) -> Database:
"""
a Database object, the database this collection belongs to.
Example:
>>> my_coll.database.name
'the_application_database'
"""
return self._database
@property
def namespace(self) -> str:
"""
The namespace this collection is in.
Example:
>>> my_coll.namespace
'default_keyspace'
"""
_namespace = self.database.namespace
if _namespace is None:
raise ValueError("The collection's DB is set with namespace=None")
return _namespace
@property
def name(self) -> str:
"""
The name of this collection.
Example:
>>> my_coll.name
'my_v_collection'
"""
# type hint added as for some reason the typechecker gets lost
return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type]
@property
def full_name(self) -> str:
"""
The fully-qualified collection name within the database,
in the form "namespace.collection_name".
Example:
>>> my_coll.full_name
'default_keyspace.my_v_collection'
"""
return f"{self.namespace}.{self.name}"
@recast_method_sync
def insert_one(
self,
document: DocumentType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
max_time_ms: Optional[int] = None,
) -> InsertOneResult:
"""
Insert a single document in the collection in an atomic operation.
Args:
document: the dictionary expressing the document to insert.
The `_id` field of the document can be left out, in which
case it will be created automatically.
vector: a vector (a list of numbers appropriate for the collection)
for the document. Passing this parameter is equivalent to
providing a `$vector` field within the document itself,
however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead.
vectorize: a string to be made into a vector, if such a service
is configured for the collection. Passing this parameter is
equivalent to providing a `$vectorize` field in the document itself,
however the two are mutually exclusive.
Moreover, this parameter cannot coexist with `vector`.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an InsertOneResult object.
Examples:
>>> my_coll.count_documents({}, upper_bound=10)
0
>>> my_coll.insert_one(
... {
... "age": 30,
... "name": "Smith",
... "food": ["pear", "peach"],
... "likes_fruit": True,
... },
... )
InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...')
>>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
InsertOneResult(raw_results=..., inserted_id='user-123')
>>> my_coll.count_documents({}, upper_bound=10)
2
>>> my_coll.insert_one({"tag": "v", "$vector": [10, 11]})
InsertOneResult(...)
Note:
If an `_id` is explicitly provided, which corresponds to a document
that exists already in the collection, an error is raised and
the insertion fails.
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="insert"
)
_document = _collate_vector_to_document(document, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"inserting one document in '{self.name}'")
io_response = self._astra_db_collection.insert_one(
_document,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished inserting one document in '{self.name}'")
if "insertedIds" in io_response.get("status", {}):
if io_response["status"]["insertedIds"]:
inserted_id = io_response["status"]["insertedIds"][0]
return InsertOneResult(
raw_results=[io_response],
inserted_id=inserted_id,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from insert_one API command.",
raw_response=io_response,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from insert_one API command.",
raw_response=io_response,
)
@recast_method_sync
def insert_many(
self,
documents: Iterable[DocumentType],
*,
vectors: Optional[Iterable[Optional[VectorType]]] = None,
vectorize: Optional[Iterable[Optional[str]]] = None,
ordered: bool = False,
chunk_size: Optional[int] = None,
concurrency: Optional[int] = None,
max_time_ms: Optional[int] = None,
) -> InsertManyResult:
"""
Insert a list of documents into the collection.
This is not an atomic operation.
Args:
documents: an iterable of dictionaries, each a document to insert.
Documents may specify their `_id` field or leave it out, in which
case it will be added automatically.
vectors: an optional list of vectors (as many vectors as the provided
documents) to associate to the documents when inserting.
Passing vectors this way is indeed equivalent to the "$vector" field
of the documents, however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead.
vectorize: an optional list of strings to be made into as many vectors
(one per document), if such a service is configured for the collection.
Passing this parameter is equivalent to providing a `$vectorize`
field in the documents themselves, however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead.
ordered: if False (default), the insertions can occur in arbitrary order
and possibly concurrently. If True, they are processed sequentially.
If there are no specific reasons against it, unordered insertions are to
be preferred as they complete much faster.
chunk_size: how many documents to include in a single API request.
Exceeding the server maximum allowed value results in an error.
Leave it unspecified (recommended) to use the system default.
concurrency: maximum number of concurrent requests to the API at
a given time. It cannot be more than one for ordered insertions.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
If many documents are being inserted, this method corresponds
to several HTTP requests: in such cases one may want to specify
a more tolerant timeout here.
Returns:
an InsertManyResult object.
Examples:
>>> my_coll.count_documents({}, upper_bound=10)
0
>>> my_coll.insert_many(
... [{"a": 10}, {"a": 5}, {"b": [True, False, False]}],
... ordered=True,
... )
InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...'])
>>> my_coll.count_documents({}, upper_bound=100)
3
>>> my_coll.insert_many(
... [{"seq": i} for i in range(50)],
... concurrency=5,
... )
InsertManyResult(raw_results=..., inserted_ids=[... ...])
>>> my_coll.count_documents({}, upper_bound=100)
53
>>> my_coll.insert_many(
... [
... {"tag": "a", "$vector": [1, 2]},
... {"tag": "b", "$vector": [3, 4]},
... ]
... )
InsertManyResult(...)
Note:
Unordered insertions are executed with some degree of concurrency,
so it is usually better to prefer this mode unless the order in the
document sequence is important.
Note:
A failure mode for this command is related to certain faulty documents
found among those to insert: a document may have the an `_id` already
present on the collection, or its vector dimension may not
match the collection setting.
For an ordered insertion, the method will raise an exception at
the first such faulty document -- nevertheless, all documents processed
until then will end up being written to the database.
For unordered insertions, if the error stems from faulty documents
the insertion proceeds until exhausting the input documents: then,
an exception is raised -- and all insertable documents will have been
written to the database, including those "after" the troublesome ones.
If, on the other hand, there are errors not related to individual
documents (such as a network connectivity error), the whole
`insert_many` operation will stop in mid-way, an exception will be raised,
and only a certain amount of the input documents will
have made their way to the database.
"""
check_deprecated_vector_ize(
vector=None, vectors=vectors, vectorize=vectorize, kind="insert"
)
if concurrency is None:
if ordered:
_concurrency = 1
else:
_concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
else:
_concurrency = concurrency
if _concurrency > 1 and ordered:
raise ValueError("Cannot run ordered insert_many concurrently.")
if chunk_size is None:
_chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS
else:
_chunk_size = chunk_size
_documents = _collate_vectors_to_documents(documents, vectors, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
raw_results: List[Dict[str, Any]] = []
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
if ordered:
options = {"ordered": True}
inserted_ids: List[Any] = []
for i in range(0, len(_documents), _chunk_size):
logger.info(f"inserting a chunk of documents in '{self.name}'")
chunk_response = self._astra_db_collection.insert_many(
documents=_documents[i : i + _chunk_size],
options=options,
partial_failures_allowed=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished inserting a chunk of documents in '{self.name}'")
# accumulate the results in this call
chunk_inserted_ids = (chunk_response.get("status") or {}).get(
"insertedIds", []
)
inserted_ids += chunk_inserted_ids
raw_results += [chunk_response]
# if errors, quit early
if chunk_response.get("errors", []):
partial_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
raise InsertManyException.from_response(
command=None,
raw_response=chunk_response,
partial_result=partial_result,
)
# return
full_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
logger.info(
f"finished inserting {len(_documents)} documents in '{self.name}'"
)
return full_result
else:
# unordered: concurrent or not, do all of them and parse the results
options = {"ordered": False}
if _concurrency > 1:
with ThreadPoolExecutor(max_workers=_concurrency) as executor:
def _chunk_insertor(
document_chunk: List[Dict[str, Any]]
) -> Dict[str, Any]:
logger.info(f"inserting a chunk of documents in '{self.name}'")
im_response = self._astra_db_collection.insert_many(
documents=document_chunk,
options=options,
partial_failures_allowed=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(
f"finished inserting a chunk of documents in '{self.name}'"
)
return im_response
raw_results = list(
executor.map(
_chunk_insertor,
(
_documents[i : i + _chunk_size]
for i in range(0, len(_documents), _chunk_size)
),
)
)
else:
for i in range(0, len(_documents), _chunk_size):
logger.info(f"inserting a chunk of documents in '{self.name}'")
raw_results.append(
self._astra_db_collection.insert_many(
_documents[i : i + _chunk_size],
options=options,
partial_failures_allowed=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
)
logger.info(
f"finished inserting a chunk of documents in '{self.name}'"
)
# recast raw_results
inserted_ids = [
inserted_id
for chunk_response in raw_results
for inserted_id in (chunk_response.get("status") or {}).get(
"insertedIds", []
)
]
# check-raise
if any(
[chunk_response.get("errors", []) for chunk_response in raw_results]
):
partial_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
raise InsertManyException.from_responses(
commands=[None for _ in raw_results],
raw_responses=raw_results,
partial_result=partial_result,
)
# return
full_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
logger.info(
f"finished inserting {len(_documents)} documents in '{self.name}'"
)
return full_result
def find(
self,
filter: Optional[FilterType] = None,
*,
projection: Optional[ProjectionType] = None,
skip: Optional[int] = None,
limit: Optional[int] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
include_similarity: Optional[bool] = None,
include_sort_vector: Optional[bool] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> Cursor:
"""
Find documents on the collection, matching a certain provided filter.
The method returns a Cursor that can then be iterated over. Depending
on the method call pattern, the iteration over all documents can reflect
collection mutations occurred since the `find` method was called, or not.
In cases where the cursor reflects mutations in real-time, it will iterate
over cursors in an approximate way (i.e. exhibiting occasional skipped
or duplicate documents). This happens when making use of the `sort`
option in a non-vector-search manner.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
skip: with this integer parameter, what would be the first `skip`
documents returned by the query are discarded, and the results
start from the (skip+1)-th document.
This parameter can be used only in conjunction with an explicit
`sort` criterion of the ascending/descending type (i.e. it cannot
be used when not sorting, nor with vector-based ANN search).
limit: this (integer) parameter sets a limit over how many documents
are returned. Once `limit` is reached (or the cursor is exhausted
for lack of matching documents), nothing more is returned.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search).
When running similarity search on a collection, no other sorting
criteria can be specified. Moreover, there is an upper bound
to the number of documents that can be returned. For details,
see the Note about upper bounds and the Data API documentation.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to `vector`,
provided such a service is configured for the collection,
and achieves the same effect.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
include_similarity: a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in each
returned document. Can only be used for vector ANN search, i.e.
when either `vector` is supplied or the `sort` parameter has the
shape {"$vector": ...}.
include_sort_vector: a boolean to request query vector used in this search.
If set to True (and if the invocation is a vector search), calling
the `get_sort_vector` method on the returned cursor will yield
the vector used for the ANN search.
sort: with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting, as well as
the one about upper bounds, for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for each single one
of the underlying HTTP requests used to fetch documents as the
cursor is iterated over.
If not passed, the collection-level setting is used instead.
Returns:
a Cursor object representing iterations over the matching documents
(see the Cursor object for how to use it. The simplest thing is to
run a for loop: `for document in collection.sort(...):`).
Examples:
>>> filter = {"seq": {"$exists": True}}
>>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5):
... print(doc["seq"])
...
37
35
10
36
27
>>> cursor1 = my_coll.find(
... {},
... limit=4,
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
>>> [doc["_id"] for doc in cursor1]
['97e85f81-...', '1581efe4-...', '...', '...']
>>> cursor2 = my_coll.find({}, limit=3)
>>> cursor2.distinct("seq")
[37, 35, 10]
>>> my_coll.insert_many([
... {"tag": "A", "$vector": [4, 5]},
... {"tag": "B", "$vector": [3, 4]},
... {"tag": "C", "$vector": [3, 2]},
... {"tag": "D", "$vector": [4, 1]},
... {"tag": "E", "$vector": [2, 5]},
... ])
>>> ann_tags = [
... document["tag"]
... for document in my_coll.find(
... {},
... sort={"$vector": [3, 3]},
... limit=3,
... )
... ]
>>> ann_tags
['A', 'B', 'C']
>>> # (assuming the collection has metric VectorMetric.COSINE)
>>> cursor = my_coll.find(
... sort={"$vector": [3, 3]},
... limit=3,
... include_sort_vector=True,
... )
>>> cursor.get_sort_vector()
[3.0, 3.0]
>>> matches = list(cursor)
>>> cursor.get_sort_vector()
[3.0, 3.0]
Note:
The following are example values for the `sort` parameter.
When no particular order is required:
sort={} # (default when parameter not provided)
When sorting by a certain value in ascending/descending order:
sort={"field": SortDocuments.ASCENDING}
sort={"field": SortDocuments.DESCENDING}
When sorting first by "field" and then by "subfield"
(while modern Python versions preserve the order of dictionaries,
it is suggested for clarity to employ a `collections.OrderedDict`
in these cases):
sort={
"field": SortDocuments.ASCENDING,
"subfield": SortDocuments.ASCENDING,
}
When running a vector similarity (ANN) search:
sort={"$vector": [0.4, 0.15, -0.5]}
Note:
Some combinations of arguments impose an implicit upper bound on the
number of documents that are returned by the Data API. More specifically:
(a) Vector ANN searches cannot return more than a number of documents
that at the time of writing is set to 1000 items.
(b) When using a sort criterion of the ascending/descending type,
the Data API will return a smaller number of documents, set to 20
at the time of writing, and stop there. The returned documents are
the top results across the whole collection according to the requested
criterion.
These provisions should be kept in mind even when subsequently running
a command such as `.distinct()` on a cursor.
Note:
When not specifying sorting criteria at all (by vector or otherwise),
the cursor can scroll through an arbitrary number of documents as
the Data API and the client periodically exchange new chunks of documents.
It should be noted that the behavior of the cursor in the case documents
have been added/removed after the `find` was started depends on database
internals and it is not guaranteed, nor excluded, that such "real-time"
changes in the data would be picked up by the cursor.
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
if include_similarity is not None and not _is_vector_sort(_sort):
raise ValueError(
"Cannot use `include_similarity` when not searching through `vector`."
)
return (
Cursor(
collection=self,
filter=filter,
projection=projection,
max_time_ms=_max_time_ms,
overall_max_time_ms=None,
)
.skip(skip)
.limit(limit)
.sort(_sort)
.include_similarity(include_similarity)
.include_sort_vector(include_sort_vector)
)
def find_one(
self,
filter: Optional[FilterType] = None,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
include_similarity: Optional[bool] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Run a search, returning the first document in the collection that matches
provided filters, if any is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search), extracting the most
similar document in the collection matching the filter.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
include_similarity: a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in the
returned document. Can only be used for vector ANN search, i.e.
when either `vector` is supplied or the `sort` parameter has the
shape {"$vector": ...}.
sort: with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary expressing the required document, otherwise None.
Examples:
>>> my_coll.find_one({})
{'_id': '68d1e515-...', 'seq': 37}
>>> my_coll.find_one({"seq": 10})
{'_id': 'd560e217-...', 'seq': 10}
>>> my_coll.find_one({"seq": 1011})
>>> # (returns None for no matches)
>>> my_coll.find_one({}, projection={"seq": False})
{'_id': '68d1e515-...'}
>>> my_coll.find_one(
... {},
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
{'_id': '97e85f81-...', 'seq': 69}
>>> my_coll.find_one({}, sort={"$vector": [1, 0]}, projection={"*": True})
{'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}
Note:
See the `find` method for more details on the accepted parameters
(whereas `skip` and `limit` are not valid parameters for `find_one`).
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
fo_cursor = self.find(
filter=filter,
projection=projection,
skip=None,
limit=1,
vector=vector,
vectorize=vectorize,
include_similarity=include_similarity,
sort=sort,
max_time_ms=_max_time_ms,
)
try:
document = fo_cursor.__next__()
return document # type: ignore[no-any-return]
except StopIteration:
return None
def distinct(
self,
key: str,
*,
filter: Optional[FilterType] = None,
max_time_ms: Optional[int] = None,
) -> List[Any]:
"""
Return a list of the unique values of `key` across the documents
in the collection that match the provided filter.
Args:
key: the name of the field whose value is inspected across documents.
Keys can use dot-notation to descend to deeper document levels.
Example of acceptable `key` values:
"field"
"field.subfield"
"field.3"
"field.3.subfield"
If lists are encountered and no numeric index is specified,
all items in the list are visited.
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`.
If not passed, the collection-level setting is used instead.
Returns:
a list of all different values for `key` found across the documents
that match the filter. The result list has no repeated items.
Example:
>>> my_coll.insert_many(
... [
... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
... ]
... )
InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...'])
>>> my_coll.distinct("name")
['Marco', 'Emma']
>>> my_coll.distinct("city")
['Helsinki']
>>> my_coll.distinct("food")
['apple', 'orange', {'likes_fruit': True, 'allergies': []}]
>>> my_coll.distinct("food.1")
['orange']
>>> my_coll.distinct("food.allergies")
[]
>>> my_coll.distinct("food.likes_fruit")
[True]
Note:
It must be kept in mind that `distinct` is a client-side operation,
which effectively browses all required documents using the logic
of the `find` method and collects the unique values found for `key`.
As such, there may be performance, latency and ultimately
billing implications if the amount of matching documents is large.
Note:
For details on the behaviour of "distinct" in conjunction with
real-time changes in the collection contents, see the
Note of the `find` command.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
f_cursor = Cursor(
collection=self,
filter=filter,
projection={key: True},
max_time_ms=None,
overall_max_time_ms=_max_time_ms,
)
return f_cursor.distinct(key) # type: ignore[no-any-return]
@recast_method_sync
def count_documents(
self,
filter: FilterType,
*,
upper_bound: int,
max_time_ms: Optional[int] = None,
) -> int:
"""
Count the documents in the collection matching the specified filter.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
upper_bound: a required ceiling on the result of the count operation.
If the actual number of documents exceeds this value,
an exception will be raised.
Furthermore, if the actual number of documents exceeds the maximum
count that the Data API can reach (regardless of upper_bound),
an exception will be raised.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
the exact count of matching documents.
Example:
>>> my_coll.insert_many([{"seq": i} for i in range(20)])
InsertManyResult(...)
>>> my_coll.count_documents({}, upper_bound=100)
20
>>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
4
>>> my_coll.count_documents({}, upper_bound=10)
Traceback (most recent call last):
... ...
astrapy.exceptions.TooManyDocumentsToCountException
Note:
Count operations are expensive: for this reason, the best practice
is to provide a reasonable `upper_bound` according to the caller
expectations. Moreover, indiscriminate usage of count operations
for sizeable amounts of documents (i.e. in the thousands and more)
is discouraged in favor of alternative application-specific solutions.
Keep in mind that the Data API has a hard upper limit on the amount
of documents it will count, and that an exception will be thrown
by this method if this limit is encountered.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info("calling count_documents")
cd_response = self._astra_db_collection.count_documents(
filter=filter,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info("finished calling count_documents")
if "count" in cd_response.get("status", {}):
count: int = cd_response["status"]["count"]
if cd_response["status"].get("moreData", False):
raise TooManyDocumentsToCountException(
text=f"Document count exceeds {count}, the maximum allowed by the server",
server_max_count_exceeded=True,
)
else:
if count > upper_bound:
raise TooManyDocumentsToCountException(
text="Document count exceeds required upper bound",
server_max_count_exceeded=False,
)
else:
return count
else:
raise DataAPIFaultyResponseException(
text="Faulty response from count_documents API command.",
raw_response=cd_response,
)
def estimated_document_count(
self,
*,
max_time_ms: Optional[int] = None,
) -> int:
"""
Query the API server for an estimate of the document count in the collection.
Contrary to `count_documents`, this method has no filtering parameters.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a server-provided estimate count of the documents in the collection.
Example:
>>> my_coll.estimated_document_count()
35700
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
ed_response = self.command(
{"estimatedDocumentCount": {}},
max_time_ms=_max_time_ms,
)
if "count" in ed_response.get("status", {}):
count: int = ed_response["status"]["count"]
return count
else:
raise DataAPIFaultyResponseException(
text="Faulty response from estimated_document_count API command.",
raw_response=ed_response,
)
@recast_method_sync
def find_one_and_replace(
self,
filter: FilterType,
replacement: DocumentType,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
return_document: str = ReturnDocument.BEFORE,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document on the collection and replace it entirely with a new one,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
replacement: the new document to write into the collection.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, `replacement` is inserted as a new document
if no matches are found on the collection. If False,
the operation silently does nothing in case of no matches.
return_document: a flag controlling what document is returned:
if set to `ReturnDocument.BEFORE`, or the string "before",
the document found on database is returned; if set to
`ReturnDocument.AFTER`, or the string "after", the new
document is returned. The default is "before".
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
A document (or a projection thereof, as required), either the one
before the replace operation or the one after that.
Alternatively, the method returns None to represent
that no matching document was found, or that no replacement
was inserted (depending on the `return_document` parameter).
Example:
>>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"})
InsertOneResult(...)
>>> my_coll.find_one_and_replace(
... {"_id": "rule1"},
... {"text": "some animals are more equal!"},
... )
{'_id': 'rule1', 'text': 'all animals are equal'}
>>> my_coll.find_one_and_replace(
... {"text": "some animals are more equal!"},
... {"text": "and the pigs are the rulers"},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'rule1', 'text': 'and the pigs are the rulers'}
>>> my_coll.find_one_and_replace(
... {"_id": "rule2"},
... {"text": "F=ma^2"},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
>>> # (returns None for no matches)
>>> my_coll.find_one_and_replace(
... {"_id": "rule2"},
... {"text": "F=ma"},
... upsert=True,
... return_document=astrapy.constants.ReturnDocument.AFTER,
... projection={"_id": False},
... )
{'text': 'F=ma'}
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"returnDocument": return_document,
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_replace on '{self.name}'")
fo_response = self._astra_db_collection.find_one_and_replace(
replacement=replacement,
filter=filter,
projection=normalize_optional_projection(projection),
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_replace on '{self.name}'")
if "document" in fo_response.get("data", {}):
ret_document = fo_response.get("data", {}).get("document")
if ret_document is None:
return None
else:
return ret_document # type: ignore[no-any-return]
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_replace API command.",
raw_response=fo_response,
)
@recast_method_sync
def replace_one(
self,
filter: FilterType,
replacement: DocumentType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Replace a single document on the collection with a new one,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
replacement: the new document to write into the collection.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, `replacement` is inserted as a new document
if no matches are found on the collection. If False,
the operation silently does nothing in case of no matches.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an UpdateResult object summarizing the outcome of the replace operation.
Example:
>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"})
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
>>> my_coll.find_one({"Buda": "Pest"})
{'_id': '8424905a-...', 'Buda': 'Pest'}
>>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True)
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'})
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"upsert": upsert,
}
logger.info(f"calling find_one_and_replace on '{self.name}'")
_max_time_ms = max_time_ms or self.api_options.max_time_ms
fo_response = self._astra_db_collection.find_one_and_replace(
replacement=replacement,
filter=filter,
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_replace on '{self.name}'")
if "document" in fo_response.get("data", {}):
fo_status = fo_response.get("status") or {}
_update_info = _prepare_update_info([fo_status])
return UpdateResult(
raw_results=[fo_response],
update_info=_update_info,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_replace API command.",
raw_response=fo_response,
)
@recast_method_sync
def find_one_and_update(
self,
filter: FilterType,
update: Dict[str, Any],
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
return_document: str = ReturnDocument.BEFORE,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document on the collection and update it as requested,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the document, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
return_document: a flag controlling what document is returned:
if set to `ReturnDocument.BEFORE`, or the string "before",
the document found on database is returned; if set to
`ReturnDocument.AFTER`, or the string "after", the new
document is returned. The default is "before".
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
A document (or a projection thereof, as required), either the one
before the replace operation or the one after that.
Alternatively, the method returns None to represent
that no matching document was found, or that no update
was applied (depending on the `return_document` parameter).
Example:
>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.find_one_and_update(
... {"Marco": {"$exists": True}},
... {"$set": {"title": "Mr."}},
... )
{'_id': 'a80106f2-...', 'Marco': 'Polo'}
>>> my_coll.find_one_and_update(
... {"title": "Mr."},
... {"$inc": {"rank": 3}},
... projection=["title", "rank"],
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3}
>>> my_coll.find_one_and_update(
... {"name": "Johnny"},
... {"$set": {"rank": 0}},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
>>> # (returns None for no matches)
>>> my_coll.find_one_and_update(
... {"name": "Johnny"},
... {"$set": {"rank": 0}},
... upsert=True,
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
{'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0}
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"returnDocument": return_document,
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_update on '{self.name}'")
fo_response = self._astra_db_collection.find_one_and_update(
update=update,
filter=filter,
projection=normalize_optional_projection(projection),
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_update on '{self.name}'")
if "document" in fo_response.get("data", {}):
ret_document = fo_response.get("data", {}).get("document")
if ret_document is None:
return None
else:
return ret_document # type: ignore[no-any-return]
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_update API command.",
raw_response=fo_response,
)
@recast_method_sync
def update_one(
self,
filter: FilterType,
update: Dict[str, Any],
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Update a single document on the collection as requested,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the document, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an UpdateResult object summarizing the outcome of the update operation.
Example:
>>> my_coll.insert_one({"Marco": "Polo"})
InsertOneResult(...)
>>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}})
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
>>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True)
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'})
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_update on '{self.name}'")
fo_response = self._astra_db_collection.find_one_and_update(
update=update,
sort=_sort,
filter=filter,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_update on '{self.name}'")
if "document" in fo_response.get("data", {}):
fo_status = fo_response.get("status") or {}
_update_info = _prepare_update_info([fo_status])
return UpdateResult(
raw_results=[fo_response],
update_info=_update_info,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_update API command.",
raw_response=fo_response,
)
@recast_method_sync
def update_many(
self,
filter: FilterType,
update: Dict[str, Any],
*,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Apply an update operations to all documents matching a condition,
optionally inserting one documents in absence of matches.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the documents, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
upsert: this parameter controls the behavior in absence of matches.
If True, a single new document (resulting from applying `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
if a large number of document updates is anticipated, it is suggested
to specify a larger timeout than in most other operations as the
update will span several HTTP calls to the API in sequence.
Returns:
an UpdateResult object summarizing the outcome of the update operation.
Example:
>>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
InsertManyResult(...)
>>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}})
UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2})
>>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}})
UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
>>> my_coll.update_many(
... {"c": "orange"},
... {"$set": {"is_also_fruit": True}},
... upsert=True,
... )
UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'})
Note:
Similarly to the case of `find` (see its docstring for more details),
running this command while, at the same time, another process is
inserting new documents which match the filter of the `update_many`
can result in an unpredictable fraction of these documents being updated.
In other words, it cannot be easily predicted whether a given
newly-inserted document will be picked up by the update_many command or not.
"""
api_options = {
"upsert": upsert,
}
page_state_options: Dict[str, str] = {}
um_responses: List[Dict[str, Any]] = []
um_statuses: List[Dict[str, Any]] = []
must_proceed = True
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"starting update_many on '{self.name}'")
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
while must_proceed:
options = {**api_options, **page_state_options}
logger.info(f"calling update_many on '{self.name}'")
this_um_response = self._astra_db_collection.update_many(
update=update,
filter=filter,
options=options,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished calling update_many on '{self.name}'")
this_um_status = this_um_response.get("status") or {}
#
# if errors, quit early
if this_um_response.get("errors", []):
partial_update_info = _prepare_update_info(um_statuses)
partial_result = UpdateResult(
raw_results=um_responses,
update_info=partial_update_info,
)
all_um_responses = um_responses + [this_um_response]
raise UpdateManyException.from_responses(
commands=[None for _ in all_um_responses],
raw_responses=all_um_responses,
partial_result=partial_result,
)
else:
if "status" not in this_um_response:
raise DataAPIFaultyResponseException(
text="Faulty response from update_many API command.",
raw_response=this_um_response,
)
um_responses.append(this_um_response)
um_statuses.append(this_um_status)
next_page_state = this_um_status.get("nextPageState")
if next_page_state is not None:
must_proceed = True
page_state_options = {"pageState": next_page_state}
else:
must_proceed = False
page_state_options = {}
update_info = _prepare_update_info(um_statuses)
logger.info(f"finished update_many on '{self.name}'")
return UpdateResult(
raw_results=um_responses,
update_info=update_info,
)
@recast_method_sync
def find_one_and_delete(
self,
filter: FilterType,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document in the collection and delete it. The deleted document,
however, is the return value of the method.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
This parameter cannot be used together with `sort`.
See the `find` method for more details on this parameter.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to `vector`,
provided such a service is configured for the collection,
and achieves the same effect.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
deleted one. See the `find` method for more on sorting.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
Either the document (or a projection thereof, as requested), or None
if no matches were found in the first place.
Example:
>>> my_coll.insert_many(
... [
... {"species": "swan", "class": "Aves"},
... {"species": "frog", "class": "Amphibia"},
... ],
... )
InsertManyResult(...)
>>> my_coll.find_one_and_delete(
... {"species": {"$ne": "frog"}},
... projection=["species"],
... )
{'_id': '5997fb48-...', 'species': 'swan'}
>>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}})
>>> # (returns None for no matches)
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_projection = normalize_optional_projection(projection)
logger.info(f"calling find_one_and_delete on '{self.name}'")
_max_time_ms = max_time_ms or self.api_options.max_time_ms
fo_response = self._astra_db_collection.find_one_and_delete(
sort=_sort,
filter=filter,
projection=_projection,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_delete on '{self.name}'")
if "document" in fo_response.get("data", {}):
document = fo_response["data"]["document"]
return document # type: ignore[no-any-return]
else:
deleted_count = fo_response.get("status", {}).get("deletedCount")
if deleted_count == 0:
return None
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_delete API command.",
raw_response=fo_response,
)
@recast_method_sync
def delete_one(
self,
filter: FilterType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> DeleteResult:
"""
Delete one document matching a provided filter.
This method never deletes more than a single document, regardless
of the number of matches to the provided filters.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
This parameter cannot be used together with `sort`.
See the `find` method for more details on this parameter.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to `vector`,
provided such a service is configured for the collection,
and achieves the same effect.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
deleted one. See the `find` method for more on sorting.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a DeleteResult object summarizing the outcome of the delete operation.
Example:
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_one({"seq": 1})
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0, 2]
>>> my_coll.delete_one(
... {"seq": {"$exists": True}},
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0]
>>> my_coll.delete_one({"seq": 2})
DeleteResult(raw_results=..., deleted_count=0)
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling delete_one_by_predicate on '{self.name}'")
do_response = self._astra_db_collection.delete_one_by_predicate(
filter=filter, timeout_info=base_timeout_info(_max_time_ms), sort=_sort
)
logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
if "deletedCount" in do_response.get("status", {}):
deleted_count = do_response["status"]["deletedCount"]
if deleted_count == -1:
return DeleteResult(
deleted_count=None,
raw_results=[do_response],
)
else:
# expected a non-negative integer:
return DeleteResult(
deleted_count=deleted_count,
raw_results=[do_response],
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from delete_one API command.",
raw_response=do_response,
)
@recast_method_sync
def delete_many(
self,
filter: FilterType,
*,
max_time_ms: Optional[int] = None,
) -> DeleteResult:
"""
Delete all documents matching a provided filter.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
Passing an empty filter, `{}`, completely erases all contents
of the collection.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
keep in mind that this method entails successive HTTP requests
to the API, depending on how many documents are to be deleted.
For this reason, in most cases it is suggested to relax the
timeout compared to other method calls.
Returns:
a DeleteResult object summarizing the outcome of the delete operation.
Example:
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_many({"seq": {"$lte": 1}})
DeleteResult(raw_results=..., deleted_count=2)
>>> my_coll.distinct("seq")
[2]
>>> my_coll.delete_many({"seq": {"$lte": 1}})
DeleteResult(raw_results=..., deleted_count=0)
Note:
This operation is in general not atomic. Depending on the amount
of matching documents, it can keep running (in a blocking way)
for a macroscopic time. In that case, new documents that are
meanwhile inserted (e.g. from another process/application) will be
deleted during the execution of this method call until the
collection is devoid of matches.
An exception is the `filter={}` case, whereby the operation is atomic.
"""
dm_responses: List[Dict[str, Any]] = []
deleted_count = 0
must_proceed = True
_max_time_ms = max_time_ms or self.api_options.max_time_ms
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
logger.info(f"starting delete_many on '{self.name}'")
while must_proceed:
logger.info(f"calling delete_many on '{self.name}'")
this_dm_response = self._astra_db_collection.delete_many(
filter=filter,
skip_error_check=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished calling delete_many on '{self.name}'")
# if errors, quit early
if this_dm_response.get("errors", []):
partial_result = DeleteResult(
deleted_count=deleted_count,
raw_results=dm_responses,
)
all_dm_responses = dm_responses + [this_dm_response]
raise DeleteManyException.from_responses(
commands=[None for _ in all_dm_responses],
raw_responses=all_dm_responses,
partial_result=partial_result,
)
else:
this_dc = this_dm_response.get("status", {}).get("deletedCount")
if this_dc is None:
raise DataAPIFaultyResponseException(
text="Faulty response from delete_many API command.",
raw_response=this_dm_response,
)
dm_responses.append(this_dm_response)
deleted_count += this_dc
must_proceed = this_dm_response.get("status", {}).get("moreData", False)
logger.info(f"finished delete_many on '{self.name}'")
return DeleteResult(
deleted_count=deleted_count,
raw_results=dm_responses,
)
@deprecation.deprecated( # type: ignore[misc]
deprecated_in="1.3.0",
removed_in="2.0.0",
current_version=__version__,
details="Use delete_many with filter={} instead.",
)
def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
"""
Delete all documents in a collection.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary of the form {"ok": 1} to signal successful deletion.
Example:
>>> my_coll.distinct("seq")
[2, 1, 0]
>>> my_coll.count_documents({}, upper_bound=100)
4
>>> my_coll.delete_all()
{'ok': 1}
>>> my_coll.count_documents({}, upper_bound=100)
0
Note:
Use with caution.
"""
dm_result = self.delete_many(filter={}, max_time_ms=max_time_ms)
if dm_result.deleted_count == -1:
return {"ok": 1}
else:
raise DataAPIFaultyResponseException(
text="Unexpected response from collection.delete_many({}).",
raw_response=None,
)
def bulk_write(
self,
requests: Iterable[BaseOperation],
*,
ordered: bool = False,
concurrency: Optional[int] = None,
max_time_ms: Optional[int] = None,
) -> BulkWriteResult:
"""
Execute an arbitrary amount of operations such as inserts, updates, deletes
either sequentially or concurrently.
This method does not execute atomically, i.e. individual operations are
each performed in the same way as the corresponding collection method,
and each one is a different and unrelated database mutation.
Args:
requests: an iterable over concrete subclasses of `BaseOperation`,
such as `InsertMany` or `ReplaceOne`. Each such object
represents an operation ready to be executed on a collection,
and is instantiated by passing the same parameters as one
would the corresponding collection method.
ordered: whether to launch the `requests` one after the other or
in arbitrary order, possibly in a concurrent fashion. For
performance reasons, False (default) should be preferred
when compatible with the needs of the application flow.
concurrency: maximum number of concurrent operations executing at
a given time. It cannot be more than one for ordered bulk writes.
max_time_ms: a timeout, in milliseconds, for the whole bulk write.
Remember that, if the method call times out, then there's no
guarantee about what portion of the bulk write has been received
and successfully executed by the Data API.
If not passed, the collection-level setting is used instead:
in most cases, however, one should pass a relaxed timeout
if longer sequences of operations are to be executed in bulk.
Returns:
A single BulkWriteResult summarizing the whole list of requested
operations. The keys in the map attributes of BulkWriteResult
(when present) are the integer indices of the corresponding operation
in the `requests` iterable.
Example:
>>> from astrapy.operations import InsertMany, ReplaceOne
>>> op1 = InsertMany([{"a": 1}, {"a": 2}])
>>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True)
>>> my_coll.bulk_write([op1, op2])
BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'})
>>> my_coll.count_documents({}, upper_bound=100)
3
>>> my_coll.distinct("replaced")
[True]
"""
# lazy importing here against circular-import error
from astrapy.operations import reduce_bulk_write_results
if concurrency is None:
if ordered:
_concurrency = 1
else:
_concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
else:
_concurrency = concurrency
if _concurrency > 1 and ordered:
raise ValueError("Cannot run ordered bulk_write concurrently.")
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"startng a bulk write on '{self.name}'")
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
if ordered:
bulk_write_results: List[BulkWriteResult] = []
for operation_i, operation in enumerate(requests):
try:
this_bw_result = operation.execute(
self,
index_in_bulk_write=operation_i,
bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
)
bulk_write_results.append(this_bw_result)
except CumulativeOperationException as exc:
partial_result = exc.partial_result
partial_bw_result = reduce_bulk_write_results(
bulk_write_results
+ [
partial_result.to_bulk_write_result(
index_in_bulk_write=operation_i
)
]
)
dar_exception = exc.data_api_response_exception()
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=[dar_exception],
)
except DataAPIResponseException as exc:
# the cumulative exceptions, with their
# partially-done-info, are handled above:
# here it's just one-shot d.a.r. exceptions
partial_bw_result = reduce_bulk_write_results(bulk_write_results)
dar_exception = exc.data_api_response_exception()
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=[dar_exception],
)
full_bw_result = reduce_bulk_write_results(bulk_write_results)
logger.info(f"finished a bulk write on '{self.name}'")
return full_bw_result
else:
def _execute_as_either(
operation: BaseOperation, operation_i: int
) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
try:
ex_result = operation.execute(
self,
index_in_bulk_write=operation_i,
bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
)
return (ex_result, None)
except DataAPIResponseException as exc:
return (None, exc)
with ThreadPoolExecutor(max_workers=_concurrency) as executor:
bulk_write_either_futures = [
executor.submit(
_execute_as_either,
operation,
operation_i,
)
for operation_i, operation in enumerate(requests)
]
bulk_write_either_results = [
bulk_write_either_future.result()
for bulk_write_either_future in bulk_write_either_futures
]
# regroup
bulk_write_successes = [
bwr for bwr, _ in bulk_write_either_results if bwr
]
bulk_write_failures = [
bwf for _, bwf in bulk_write_either_results if bwf
]
if bulk_write_failures:
# extract and cumulate
partial_results_from_failures = [
failure.partial_result.to_bulk_write_result(
index_in_bulk_write=operation_i
)
for failure in bulk_write_failures
if isinstance(failure, CumulativeOperationException)
]
partial_bw_result = reduce_bulk_write_results(
bulk_write_successes + partial_results_from_failures
)
# raise and recast the first exception
all_dar_exceptions = [
bw_failure.data_api_response_exception()
for bw_failure in bulk_write_failures
]
dar_exception = all_dar_exceptions[0]
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=all_dar_exceptions,
)
else:
logger.info(f"finished a bulk write on '{self.name}'")
return reduce_bulk_write_results(bulk_write_successes)
def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
"""
Drop the collection, i.e. delete it from the database along with
all the documents it contains.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Remember there is not guarantee that a request that has
timed out us not in fact honored.
Returns:
a dictionary of the form {"ok": 1} to signal successful deletion.
Example:
>>> my_coll.find_one({})
{'_id': '...', 'a': 100}
>>> my_coll.drop()
{'ok': 1}
>>> my_coll.find_one({})
Traceback (most recent call last):
... ...
astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection
Note:
Use with caution.
Note:
Once the method succeeds, methods on this object can still be invoked:
however, this hardly makes sense as the underlying actual collection
is no more.
It is responsibility of the developer to design a correct flow
which avoids using a deceased collection any further.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"dropping collection '{self.name}' (self)")
drop_result = self.database.drop_collection(self, max_time_ms=_max_time_ms)
logger.info(f"finished dropping collection '{self.name}' (self)")
return drop_result # type: ignore[no-any-return]
def command(
self,
body: Dict[str, Any],
*,
max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
"""
Send a POST request to the Data API for this collection with
an arbitrary, caller-provided payload.
Args:
body: a JSON-serializable dictionary, the payload of the request.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary with the response of the HTTP request.
Example:
>>> my_coll.command({"countDocuments": {}})
{'status': {'count': 123}}
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling command on '{self.name}'")
command_result = self.database.command(
body=body,
namespace=self.namespace,
collection_name=self.name,
max_time_ms=_max_time_ms,
)
logger.info(f"finished calling command on '{self.name}'")
return command_result # type: ignore[no-any-return]
class AsyncCollection:
"""
A Data API collection, the main object to interact with the Data API,
especially for DDL operations.
This class has a synchronous interface.
A Collection is spawned from a Database object, from which it inherits
the details on how to reach the API server (endpoint, authentication token).
Args:
database: a Database object, instantiated earlier. This represents
the database the collection belongs to.
name: the collection name. This parameter should match an existing
collection on the database.
namespace: this is the namespace to which the collection belongs.
If not specified, the database's working namespace is used.
api_options: An instance of `astrapy.api_options.CollectionAPIOptions`
providing the general settings for interacting with the Data API.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Examples:
>>> from astrapy import DataAPIClient, AsyncCollection
>>> my_client = astrapy.DataAPIClient("AstraCS:...")
>>> my_async_db = my_client.get_async_database(
... "https://01234567-....apps.astra.datastax.com"
... )
>>> my_async_coll_1 = AsyncCollection(database=my_async_db, name="my_collection")
>>> my_async coll_2 = asyncio.run(my_async_db.create_collection(
... "my_v_collection",
... dimension=3,
... metric="cosine",
... ))
>>> my_async_coll_3a = asyncio.run(my_async_db.get_collection(
... "my_already_existing_collection",
... ))
>>> my_async_coll_3b = my_async_db.my_already_existing_collection
>>> my_async_coll_3c = my_async_db["my_already_existing_collection"]
Note:
creating an instance of Collection does not trigger actual creation
of the collection on the database. The latter should have been created
beforehand, e.g. through the `create_collection` method of a Database.
"""
def __init__(
self,
database: AsyncDatabase,
name: str,
*,
namespace: Optional[str] = None,
api_options: Optional[CollectionAPIOptions] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> None:
if api_options is None:
self.api_options = CollectionAPIOptions()
else:
self.api_options = api_options
additional_headers = self.api_options.embedding_api_key.get_headers()
self._astra_db_collection: AsyncAstraDBCollection = AsyncAstraDBCollection(
collection_name=name,
astra_db=database._astra_db,
namespace=namespace,
caller_name=caller_name,
caller_version=caller_version,
additional_headers=additional_headers,
)
# this comes after the above, lets AstraDBCollection resolve namespace
self._database = database._copy(
namespace=self._astra_db_collection.astra_db.namespace
)
def __repr__(self) -> str:
return (
f'{self.__class__.__name__}(name="{self.name}", '
f'namespace="{self.namespace}", database={self.database}, '
f"api_options={self.api_options})"
)
def __eq__(self, other: Any) -> bool:
if isinstance(other, AsyncCollection):
return all(
[
self._astra_db_collection == other._astra_db_collection,
self.api_options == other.api_options,
]
)
else:
return False
def __call__(self, *pargs: Any, **kwargs: Any) -> None:
raise TypeError(
f"'{self.__class__.__name__}' object is not callable. If you "
f"meant to call the '{self.name}' method on a "
f"'{self.database.__class__.__name__}' object "
"it is failing because no such method exists."
)
def _copy(
self,
*,
database: Optional[AsyncDatabase] = None,
name: Optional[str] = None,
namespace: Optional[str] = None,
api_options: Optional[CollectionAPIOptions] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> AsyncCollection:
return AsyncCollection(
database=database or self.database._copy(),
name=name or self.name,
namespace=namespace or self.namespace,
api_options=self.api_options.with_override(api_options),
caller_name=caller_name or self._astra_db_collection.caller_name,
caller_version=caller_version or self._astra_db_collection.caller_version,
)
def with_options(
self,
*,
name: Optional[str] = None,
embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None,
collection_max_time_ms: Optional[int] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> AsyncCollection:
"""
Create a clone of this collection with some changed attributes.
Args:
name: the name of the collection. This parameter is useful to
quickly spawn AsyncCollection instances each pointing to a different
collection existing in the same namespace.
embedding_api_key: optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`).
For some vectorize providers/models, if using header-based authentication,
specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider`
should be supplied.
collection_max_time_ms: a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
`find`, `delete_many`, `insert_many` and so on), it is strongly suggested
to provide a specific timeout as the default one likely wouldn't make
much sense.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Returns:
a new AsyncCollection instance.
Example:
>>> my_other_async_coll = my_async_coll.with_options(
... name="the_other_coll",
... caller_name="caller_identity",
... )
"""
_api_options = CollectionAPIOptions(
embedding_api_key=coerce_embedding_headers_provider(embedding_api_key),
max_time_ms=collection_max_time_ms,
)
return self._copy(
name=name,
api_options=_api_options,
caller_name=caller_name,
caller_version=caller_version,
)
def to_sync(
self,
*,
database: Optional[Database] = None,
name: Optional[str] = None,
namespace: Optional[str] = None,
embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None,
collection_max_time_ms: Optional[int] = None,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> Collection:
"""
Create a Collection from this one. Save for the arguments
explicitly provided as overrides, everything else is kept identical
to this collection in the copy (the database is converted into
a sync object).
Args:
database: a Database object, instantiated earlier.
This represents the database the new collection belongs to.
name: the collection name. This parameter should match an existing
collection on the database.
namespace: this is the namespace to which the collection belongs.
If not specified, the database's working namespace is used.
embedding_api_key: optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`).
For some vectorize providers/models, if using header-based authentication,
specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider`
should be supplied.
collection_max_time_ms: a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
`find`, `delete_many`, `insert_many` and so on), it is strongly suggested
to provide a specific timeout as the default one likely wouldn't make
much sense.
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Returns:
the new copy, a Collection instance.
Example:
>>> my_async_coll.to_sync().count_documents({}, upper_bound=100)
77
"""
_api_options = CollectionAPIOptions(
embedding_api_key=coerce_embedding_headers_provider(embedding_api_key),
max_time_ms=collection_max_time_ms,
)
return Collection(
database=database or self.database.to_sync(),
name=name or self.name,
namespace=namespace or self.namespace,
api_options=self.api_options.with_override(_api_options),
caller_name=caller_name or self._astra_db_collection.caller_name,
caller_version=caller_version or self._astra_db_collection.caller_version,
)
def set_caller(
self,
caller_name: Optional[str] = None,
caller_version: Optional[str] = None,
) -> None:
"""
Set a new identity for the application/framework on behalf of which
the Data API calls are performed (the "caller").
Args:
caller_name: name of the application, or framework, on behalf of which
the Data API calls are performed. This ends up in the request user-agent.
caller_version: version of the caller.
Example:
>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")
"""
logger.info(f"setting caller to {caller_name}/{caller_version}")
self._astra_db_collection.set_caller(
caller_name=caller_name,
caller_version=caller_version,
)
async def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions:
"""
Get the collection options, i.e. its configuration as read from the database.
The method issues a request to the Data API each time is invoked,
without caching mechanisms: this ensures up-to-date information
for usages such as real-time collection validation by the application.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a CollectionOptions instance describing the collection.
(See also the database `list_collections` method.)
Example:
>>> asyncio.run(my_async_coll.options())
CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"getting collections in search of '{self.name}'")
self_descriptors = [
coll_desc
async for coll_desc in self.database.list_collections(
max_time_ms=_max_time_ms
)
if coll_desc.name == self.name
]
logger.info(f"finished getting collections in search of '{self.name}'")
if self_descriptors:
return self_descriptors[0].options # type: ignore[no-any-return]
else:
raise CollectionNotFoundException(
text=f"Collection {self.namespace}.{self.name} not found.",
namespace=self.namespace,
collection_name=self.name,
)
def info(self) -> CollectionInfo:
"""
Information on the collection (name, location, database), in the
form of a CollectionInfo object.
Not to be confused with the collection `options` method (related
to the collection internal configuration).
Example:
>>> my_async_coll.info().database_info.region
'us-east1'
>>> my_async_coll.info().full_name
'default_keyspace.my_v_collection'
Note:
the returned CollectionInfo wraps, among other things,
the database information: as such, calling this method
triggers the same-named method of a Database object (which, in turn,
performs a HTTP request to the DevOps API).
See the documentation for `Database.info()` for more details.
"""
return CollectionInfo(
database_info=self.database.info(),
namespace=self.namespace,
name=self.name,
full_name=self.full_name,
)
@property
def database(self) -> AsyncDatabase:
"""
a Database object, the database this collection belongs to.
Example:
>>> my_async_coll.database.name
'quicktest'
"""
return self._database
@property
def namespace(self) -> str:
"""
The namespace this collection is in.
Example:
>>> my_async_coll.database.namespace
'default_keyspace'
"""
_namespace = self.database.namespace
if _namespace is None:
raise ValueError("The collection's DB is set with namespace=None")
return _namespace
@property
def name(self) -> str:
"""
The name of this collection.
Example:
>>> my_async_coll.name
'my_v_collection'
"""
# type hint added as for some reason the typechecker gets lost
return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type]
@property
def full_name(self) -> str:
"""
The fully-qualified collection name within the database,
in the form "namespace.collection_name".
Example:
>>> my_async_coll.full_name
'default_keyspace.my_v_collection'
"""
return f"{self.namespace}.{self.name}"
@recast_method_async
async def insert_one(
self,
document: DocumentType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
max_time_ms: Optional[int] = None,
) -> InsertOneResult:
"""
Insert a single document in the collection in an atomic operation.
Args:
document: the dictionary expressing the document to insert.
The `_id` field of the document can be left out, in which
case it will be created automatically.
vector: a vector (a list of numbers appropriate for the collection)
for the document. Passing this parameter is equivalent to
providing a `$vector` field within the document itself,
however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead.
vectorize: a string to be made into a vector, if such a service
is configured for the collection. Passing this parameter is
equivalent to providing a `$vectorize` field in the document itself,
however the two are mutually exclusive.
Moreover, this parameter cannot coexist with `vector`.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an InsertOneResult object.
Example:
>>> async def write_and_count(acol: AsyncCollection) -> None:
... count0 = await acol.count_documents({}, upper_bound=10)
... print("count0", count0)
... await acol.insert_one(
... {
... "age": 30,
... "name": "Smith",
... "food": ["pear", "peach"],
... "likes_fruit": True,
... },
... )
... await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"})
... count1 = await acol.count_documents({}, upper_bound=10)
... print("count1", count1)
...
>>> asyncio.run(write_and_count(my_async_coll))
count0 0
count1 2
>>> asyncio.run(my_async_coll.insert_one({"tag": v", "$vector": [10, 11]}))
InsertOneResult(...)
Note:
If an `_id` is explicitly provided, which corresponds to a document
that exists already in the collection, an error is raised and
the insertion fails.
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="insert",
from_async_method=True,
)
_document = _collate_vector_to_document(document, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"inserting one document in '{self.name}'")
io_response = await self._astra_db_collection.insert_one(
_document,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished inserting one document in '{self.name}'")
if "insertedIds" in io_response.get("status", {}):
if io_response["status"]["insertedIds"]:
inserted_id = io_response["status"]["insertedIds"][0]
return InsertOneResult(
raw_results=[io_response],
inserted_id=inserted_id,
)
else:
raise ValueError(
"Could not complete a insert_one operation. "
f"(gotten '${json.dumps(io_response)}')"
)
else:
raise ValueError(
"Could not complete a insert_one operation. "
f"(gotten '${json.dumps(io_response)}')"
)
@recast_method_async
async def insert_many(
self,
documents: Iterable[DocumentType],
*,
vectors: Optional[Iterable[Optional[VectorType]]] = None,
vectorize: Optional[Iterable[Optional[str]]] = None,
ordered: bool = False,
chunk_size: Optional[int] = None,
concurrency: Optional[int] = None,
max_time_ms: Optional[int] = None,
) -> InsertManyResult:
"""
Insert a list of documents into the collection.
This is not an atomic operation.
Args:
documents: an iterable of dictionaries, each a document to insert.
Documents may specify their `_id` field or leave it out, in which
case it will be added automatically.
vectors: an optional list of vectors (as many vectors as the provided
documents) to associate to the documents when inserting.
Passing vectors this way is indeed equivalent to the "$vector" field
of the documents, however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead.
vectorize: an optional list of strings to be made into as many vectors
(one per document), if such a service is configured for the collection.
Passing this parameter is equivalent to providing a `$vectorize`
field in the documents themselves, however the two are mutually exclusive.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead.
ordered: if False (default), the insertions can occur in arbitrary order
and possibly concurrently. If True, they are processed sequentially.
If there are no specific reasons against it, unordered insertions are to
be preferred as they complete much faster.
chunk_size: how many documents to include in a single API request.
Exceeding the server maximum allowed value results in an error.
Leave it unspecified (recommended) to use the system default.
concurrency: maximum number of concurrent requests to the API at
a given time. It cannot be more than one for ordered insertions.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
If many documents are being inserted, this method corresponds
to several HTTP requests: in such cases one may want to specify
a more tolerant timeout here.
Returns:
an InsertManyResult object.
Examples:
>>> async def write_and_count(acol: AsyncCollection) -> None:
... count0 = await acol.count_documents({}, upper_bound=10)
... print("count0", count0)
... im_result1 = await acol.insert_many(
... [
... {"a": 10},
... {"a": 5},
... {"b": [True, False, False]},
... ],
... ordered=True,
... )
... print("inserted1", im_result1.inserted_ids)
... count1 = await acol.count_documents({}, upper_bound=100)
... print("count1", count1)
... await acol.insert_many(
... [{"seq": i} for i in range(50)],
... concurrency=5,
... )
... count2 = await acol.count_documents({}, upper_bound=100)
... print("count2", count2)
...
>>> asyncio.run(write_and_count(my_async_coll))
count0 0
inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...']
count1 3
count2 53
>>> asyncio.run(my_async_coll.insert_many(
... [
... {"tag": "a", "$vector": [1, 2]},
... {"tag": "b", "$vector": [3, 4]},
... ]
... ))
InsertManyResult(...)
Note:
Unordered insertions are executed with some degree of concurrency,
so it is usually better to prefer this mode unless the order in the
document sequence is important.
Note:
A failure mode for this command is related to certain faulty documents
found among those to insert: a document may have the an `_id` already
present on the collection, or its vector dimension may not
match the collection setting.
For an ordered insertion, the method will raise an exception at
the first such faulty document -- nevertheless, all documents processed
until then will end up being written to the database.
For unordered insertions, if the error stems from faulty documents
the insertion proceeds until exhausting the input documents: then,
an exception is raised -- and all insertable documents will have been
written to the database, including those "after" the troublesome ones.
If, on the other hand, there are errors not related to individual
documents (such as a network connectivity error), the whole
`insert_many` operation will stop in mid-way, an exception will be raised,
and only a certain amount of the input documents will
have made their way to the database.
"""
check_deprecated_vector_ize(
vector=None,
vectors=vectors,
vectorize=vectorize,
kind="insert",
from_async_method=True,
)
if concurrency is None:
if ordered:
_concurrency = 1
else:
_concurrency = DEFAULT_INSERT_MANY_CONCURRENCY
else:
_concurrency = concurrency
if _concurrency > 1 and ordered:
raise ValueError("Cannot run ordered insert_many concurrently.")
if chunk_size is None:
_chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS
else:
_chunk_size = chunk_size
_documents = _collate_vectors_to_documents(documents, vectors, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"inserting {len(_documents)} documents in '{self.name}'")
raw_results: List[Dict[str, Any]] = []
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
if ordered:
options = {"ordered": True}
inserted_ids: List[Any] = []
for i in range(0, len(_documents), _chunk_size):
logger.info(f"inserting a chunk of documents in '{self.name}'")
chunk_response = await self._astra_db_collection.insert_many(
documents=_documents[i : i + _chunk_size],
options=options,
partial_failures_allowed=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished inserting a chunk of documents in '{self.name}'")
# accumulate the results in this call
chunk_inserted_ids = (chunk_response.get("status") or {}).get(
"insertedIds", []
)
inserted_ids += chunk_inserted_ids
raw_results += [chunk_response]
# if errors, quit early
if chunk_response.get("errors", []):
partial_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
raise InsertManyException.from_response(
command=None,
raw_response=chunk_response,
partial_result=partial_result,
)
# return
full_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
logger.info(
f"finished inserting {len(_documents)} documents in '{self.name}'"
)
return full_result
else:
# unordered: concurrent or not, do all of them and parse the results
options = {"ordered": False}
sem = asyncio.Semaphore(_concurrency)
async def concurrent_insert_chunk(
document_chunk: List[DocumentType],
) -> Dict[str, Any]:
async with sem:
logger.info(f"inserting a chunk of documents in '{self.name}'")
im_response = await self._astra_db_collection.insert_many(
document_chunk,
options=options,
partial_failures_allowed=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(
f"finished inserting a chunk of documents in '{self.name}'"
)
return im_response
if _concurrency > 1:
tasks = [
asyncio.create_task(
concurrent_insert_chunk(_documents[i : i + _chunk_size])
)
for i in range(0, len(_documents), _chunk_size)
]
raw_results = await asyncio.gather(*tasks)
else:
raw_results = [
await concurrent_insert_chunk(_documents[i : i + _chunk_size])
for i in range(0, len(_documents), _chunk_size)
]
# recast raw_results
inserted_ids = [
inserted_id
for chunk_response in raw_results
for inserted_id in (chunk_response.get("status") or {}).get(
"insertedIds", []
)
]
# check-raise
if any(
[chunk_response.get("errors", []) for chunk_response in raw_results]
):
partial_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
raise InsertManyException.from_responses(
commands=[None for _ in raw_results],
raw_responses=raw_results,
partial_result=partial_result,
)
# return
full_result = InsertManyResult(
raw_results=raw_results,
inserted_ids=inserted_ids,
)
logger.info(
f"finished inserting {len(_documents)} documents in '{self.name}'"
)
return full_result
def find(
self,
filter: Optional[FilterType] = None,
*,
projection: Optional[ProjectionType] = None,
skip: Optional[int] = None,
limit: Optional[int] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
include_similarity: Optional[bool] = None,
include_sort_vector: Optional[bool] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> AsyncCursor:
"""
Find documents on the collection, matching a certain provided filter.
The method returns a Cursor that can then be iterated over. Depending
on the method call pattern, the iteration over all documents can reflect
collection mutations occurred since the `find` method was called, or not.
In cases where the cursor reflects mutations in real-time, it will iterate
over cursors in an approximate way (i.e. exhibiting occasional skipped
or duplicate documents). This happens when making use of the `sort`
option in a non-vector-search manner.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
skip: with this integer parameter, what would be the first `skip`
documents returned by the query are discarded, and the results
start from the (skip+1)-th document.
This parameter can be used only in conjunction with an explicit
`sort` criterion of the ascending/descending type (i.e. it cannot
be used when not sorting, nor with vector-based ANN search).
limit: this (integer) parameter sets a limit over how many documents
are returned. Once `limit` is reached (or the cursor is exhausted
for lack of matching documents), nothing more is returned.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search).
When running similarity search on a collection, no other sorting
criteria can be specified. Moreover, there is an upper bound
to the number of documents that can be returned. For details,
see the Note about upper bounds and the Data API documentation.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to `vector`,
provided such a service is configured for the collection,
and achieves the same effect.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
include_similarity: a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in each
returned document. Can only be used for vector ANN search, i.e.
when either `vector` is supplied or the `sort` parameter has the
shape {"$vector": ...}.
include_sort_vector: a boolean to request query vector used in this search.
If set to True (and if the invocation is a vector search), calling
the `get_sort_vector` method on the returned cursor will yield
the vector used for the ANN search.
sort: with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting, as well as
the one about upper bounds, for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for each single one
of the underlying HTTP requests used to fetch documents as the
cursor is iterated over.
If not passed, the collection-level setting is used instead.
Returns:
an AsyncCursor object representing iterations over the matching documents
(see the AsyncCursor object for how to use it. The simplest thing is to
run a for loop: `for document in collection.sort(...):`).
Examples:
>>> async def run_finds(acol: AsyncCollection) -> None:
... filter = {"seq": {"$exists": True}}
... print("find results 1:")
... async for doc in acol.find(filter, projection={"seq": True}, limit=5):
... print(doc["seq"])
... async_cursor1 = acol.find(
... {},
... limit=4,
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
... ids = [doc["_id"] async for doc in async_cursor1]
... print("find results 2:", ids)
... async_cursor2 = acol.find({}, limit=3)
... seqs = await async_cursor2.distinct("seq")
... print("distinct results 3:", seqs)
...
>>> asyncio.run(run_finds(my_async_coll))
find results 1:
48
35
7
11
13
find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...']
distinct results 3: [48, 35, 7]
>>> async def run_vector_finds(acol: AsyncCollection) -> None:
... await acol.insert_many([
... {"tag": "A", "$vector": [4, 5]},
... {"tag": "B", "$vector": [3, 4]},
... {"tag": "C", "$vector": [3, 2]},
... {"tag": "D", "$vector": [4, 1]},
... {"tag": "E", "$vector": [2, 5]},
... ])
... ann_tags = [
... document["tag"]
... async for document in acol.find(
... {},
... sort={"$vector": [3, 3]},
... limit=3,
... )
... ]
... return ann_tags
...
>>> asyncio.run(run_vector_finds(my_async_coll))
['A', 'B', 'C']
>>> # (assuming the collection has metric VectorMetric.COSINE)
>>> async_cursor = my_async_coll.find(
... sort={"$vector": [3, 3]},
... limit=3,
... include_sort_vector=True,
... )
>>> asyncio.run(async_cursor.get_sort_vector())
[3.0, 3.0]
>>> asyncio.run(async_cursor.__anext__())
{'_id': 'b13ce177-738e-47ec-bce1-77738ee7ec93', 'tag': 'A'}
>>> asyncio.run(async_cursor.get_sort_vector())
[3.0, 3.0]
Note:
The following are example values for the `sort` parameter.
When no particular order is required:
sort={}
When sorting by a certain value in ascending/descending order:
sort={"field": SortDocuments.ASCENDING}
sort={"field": SortDocuments.DESCENDING}
When sorting first by "field" and then by "subfield"
(while modern Python versions preserve the order of dictionaries,
it is suggested for clarity to employ a `collections.OrderedDict`
in these cases):
sort={
"field": SortDocuments.ASCENDING,
"subfield": SortDocuments.ASCENDING,
}
When running a vector similarity (ANN) search:
sort={"$vector": [0.4, 0.15, -0.5]}
Note:
Some combinations of arguments impose an implicit upper bound on the
number of documents that are returned by the Data API. More specifically:
(a) Vector ANN searches cannot return more than a number of documents
that at the time of writing is set to 1000 items.
(b) When using a sort criterion of the ascending/descending type,
the Data API will return a smaller number of documents, set to 20
at the time of writing, and stop there. The returned documents are
the top results across the whole collection according to the requested
criterion.
These provisions should be kept in mind even when subsequently running
a command such as `.distinct()` on a cursor.
Note:
When not specifying sorting criteria at all (by vector or otherwise),
the cursor can scroll through an arbitrary number of documents as
the Data API and the client periodically exchange new chunks of documents.
It should be noted that the behavior of the cursor in the case documents
have been added/removed after the `find` was started depends on database
internals and it is not guaranteed, nor excluded, that such "real-time"
changes in the data would be picked up by the cursor.
"""
check_deprecated_vector_ize(
vector=vector, vectors=None, vectorize=vectorize, kind="find"
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
if include_similarity is not None and not _is_vector_sort(_sort):
raise ValueError(
"Cannot use `include_similarity` when not searching through `vector`."
)
return (
AsyncCursor(
collection=self,
filter=filter,
projection=projection,
max_time_ms=_max_time_ms,
overall_max_time_ms=None,
)
.skip(skip)
.limit(limit)
.sort(_sort)
.include_similarity(include_similarity)
.include_sort_vector(include_sort_vector)
)
async def find_one(
self,
filter: Optional[FilterType] = None,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
include_similarity: Optional[bool] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Run a search, returning the first document in the collection that matches
provided filters, if any is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search), extracting the most
similar document in the collection matching the filter.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
include_similarity: a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in the
returned document. Can only be used for vector ANN search, i.e.
when either `vector` is supplied or the `sort` parameter has the
shape {"$vector": ...}.
sort: with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary expressing the required document, otherwise None.
Example:
>>> async def demo_find_one(acol: AsyncCollection) -> None:
.... print("Count:", await acol.count_documents({}, upper_bound=100))
... result0 = await acol.find_one({})
... print("result0", result0)
... result1 = await acol.find_one({"seq": 10})
... print("result1", result1)
... result2 = await acol.find_one({"seq": 1011})
... print("result2", result2)
... result3 = await acol.find_one({}, projection={"seq": False})
... print("result3", result3)
... result4 = await acol.find_one(
... {},
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
... print("result4", result4)
...
>>>
>>> asyncio.run(demo_find_one(my_async_coll))
Count: 50
result0 {'_id': '479c7ce8-...', 'seq': 48}
result1 {'_id': '93e992c4-...', 'seq': 10}
result2 None
result3 {'_id': '479c7ce8-...'}
result4 {'_id': 'd656cd9d-...', 'seq': 49}
>>> asyncio.run(my_async_coll.find_one(
... {},
... sort={"$vector": [1, 0]},
... projection={"*": True},
... ))
{'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}
Note:
See the `find` method for more details on the accepted parameters
(whereas `skip` and `limit` are not valid parameters for `find_one`).
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
fo_cursor = self.find(
filter=filter,
projection=projection,
skip=None,
limit=1,
vector=vector,
vectorize=vectorize,
include_similarity=include_similarity,
sort=sort,
max_time_ms=_max_time_ms,
)
try:
document = await fo_cursor.__anext__()
return document # type: ignore[no-any-return]
except StopAsyncIteration:
return None
async def distinct(
self,
key: str,
*,
filter: Optional[FilterType] = None,
max_time_ms: Optional[int] = None,
) -> List[Any]:
"""
Return a list of the unique values of `key` across the documents
in the collection that match the provided filter.
Args:
key: the name of the field whose value is inspected across documents.
Keys can use dot-notation to descend to deeper document levels.
Example of acceptable `key` values:
"field"
"field.subfield"
"field.3"
"field.3.subfield"
If lists are encountered and no numeric index is specified,
all items in the list are visited.
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`.
If not passed, the collection-level setting is used instead.
Returns:
a list of all different values for `key` found across the documents
that match the filter. The result list has no repeated items.
Example:
>>> async def run_distinct(acol: AsyncCollection) -> None:
... await acol.insert_many(
... [
... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"},
... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}},
... ]
... )
... distinct0 = await acol.distinct("name")
... print("distinct('name')", distinct0)
... distinct1 = await acol.distinct("city")
... print("distinct('city')", distinct1)
... distinct2 = await acol.distinct("food")
... print("distinct('food')", distinct2)
... distinct3 = await acol.distinct("food.1")
... print("distinct('food.1')", distinct3)
... distinct4 = await acol.distinct("food.allergies")
... print("distinct('food.allergies')", distinct4)
... distinct5 = await acol.distinct("food.likes_fruit")
... print("distinct('food.likes_fruit')", distinct5)
...
>>> asyncio.run(run_distinct(my_async_coll))
distinct('name') ['Emma', 'Marco']
distinct('city') ['Helsinki']
distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange']
distinct('food.1') ['orange']
distinct('food.allergies') []
distinct('food.likes_fruit') [True]
Note:
It must be kept in mind that `distinct` is a client-side operation,
which effectively browses all required documents using the logic
of the `find` method and collects the unique values found for `key`.
As such, there may be performance, latency and ultimately
billing implications if the amount of matching documents is large.
Note:
For details on the behaviour of "distinct" in conjunction with
real-time changes in the collection contents, see the
Note of the `find` command.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
f_cursor = AsyncCursor(
collection=self,
filter=filter,
projection={key: True},
max_time_ms=None,
overall_max_time_ms=_max_time_ms,
)
return await f_cursor.distinct(key) # type: ignore[no-any-return]
@recast_method_async
async def count_documents(
self,
filter: FilterType,
*,
upper_bound: int,
max_time_ms: Optional[int] = None,
) -> int:
"""
Count the documents in the collection matching the specified filter.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
upper_bound: a required ceiling on the result of the count operation.
If the actual number of documents exceeds this value,
an exception will be raised.
Furthermore, if the actual number of documents exceeds the maximum
count that the Data API can reach (regardless of upper_bound),
an exception will be raised.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
the exact count of matching documents.
Example:
>>> async def do_count_docs(acol: AsyncCollection) -> None:
... await acol.insert_many([{"seq": i} for i in range(20)])
... count0 = await acol.count_documents({}, upper_bound=100)
... print("count0", count0)
... count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100)
... print("count1", count1)
... count2 = await acol.count_documents({}, upper_bound=10)
... print("count2", count2)
...
>>> asyncio.run(do_count_docs(my_async_coll))
count0 20
count1 4
Traceback (most recent call last):
... ...
astrapy.exceptions.TooManyDocumentsToCountException
Note:
Count operations are expensive: for this reason, the best practice
is to provide a reasonable `upper_bound` according to the caller
expectations. Moreover, indiscriminate usage of count operations
for sizeable amounts of documents (i.e. in the thousands and more)
is discouraged in favor of alternative application-specific solutions.
Keep in mind that the Data API has a hard upper limit on the amount
of documents it will count, and that an exception will be thrown
by this method if this limit is encountered.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info("calling count_documents")
cd_response = await self._astra_db_collection.count_documents(
filter=filter,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info("finished calling count_documents")
if "count" in cd_response.get("status", {}):
count: int = cd_response["status"]["count"]
if cd_response["status"].get("moreData", False):
raise TooManyDocumentsToCountException(
text=f"Document count exceeds {count}, the maximum allowed by the server",
server_max_count_exceeded=True,
)
else:
if count > upper_bound:
raise TooManyDocumentsToCountException(
text="Document count exceeds required upper bound",
server_max_count_exceeded=False,
)
else:
return count
else:
raise DataAPIFaultyResponseException(
text="Faulty response from count_documents API command.",
raw_response=cd_response,
)
async def estimated_document_count(
self,
*,
max_time_ms: Optional[int] = None,
) -> int:
"""
Query the API server for an estimate of the document count in the collection.
Contrary to `count_documents`, this method has no filtering parameters.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a server-provided estimate count of the documents in the collection.
Example:
>>> asyncio.run(my_async_coll.estimated_document_count())
35700
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
ed_response = await self.command(
{"estimatedDocumentCount": {}},
max_time_ms=_max_time_ms,
)
if "count" in ed_response.get("status", {}):
count: int = ed_response["status"]["count"]
return count
else:
raise DataAPIFaultyResponseException(
text="Faulty response from estimated_document_count API command.",
raw_response=ed_response,
)
@recast_method_async
async def find_one_and_replace(
self,
filter: FilterType,
replacement: DocumentType,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
return_document: str = ReturnDocument.BEFORE,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document on the collection and replace it entirely with a new one,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
replacement: the new document to write into the collection.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, `replacement` is inserted as a new document
if no matches are found on the collection. If False,
the operation silently does nothing in case of no matches.
return_document: a flag controlling what document is returned:
if set to `ReturnDocument.BEFORE`, or the string "before",
the document found on database is returned; if set to
`ReturnDocument.AFTER`, or the string "after", the new
document is returned. The default is "before".
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
A document, either the one before the replace operation or the
one after that. Alternatively, the method returns None to represent
that no matching document was found, or that no replacement
was inserted (depending on the `return_document` parameter).
Example:
>>> async def do_find_one_and_replace(acol: AsyncCollection) -> None:
... await acol.insert_one({"_id": "rule1", "text": "all animals are equal"})
... result0 = await acol.find_one_and_replace(
... {"_id": "rule1"},
... {"text": "some animals are more equal!"},
... )
... print("result0", result0)
... result1 = await acol.find_one_and_replace(
... {"text": "some animals are more equal!"},
... {"text": "and the pigs are the rulers"},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
... print("result1", result1)
... result2 = await acol.find_one_and_replace(
... {"_id": "rule2"},
... {"text": "F=ma^2"},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
... print("result2", result2)
... result3 = await acol.find_one_and_replace(
... {"_id": "rule2"},
... {"text": "F=ma"},
... upsert=True,
... return_document=astrapy.constants.ReturnDocument.AFTER,
... projection={"_id": False},
... )
... print("result3", result3)
...
>>> asyncio.run(do_find_one_and_replace(my_async_coll))
result0 {'_id': 'rule1', 'text': 'all animals are equal'}
result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'}
result2 None
result3 {'text': 'F=ma'}
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"returnDocument": return_document,
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_replace on '{self.name}'")
fo_response = await self._astra_db_collection.find_one_and_replace(
replacement=replacement,
filter=filter,
projection=normalize_optional_projection(projection),
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_replace on '{self.name}'")
if "document" in fo_response.get("data", {}):
ret_document = fo_response.get("data", {}).get("document")
if ret_document is None:
return None
else:
return ret_document # type: ignore[no-any-return]
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_replace API command.",
raw_response=fo_response,
)
@recast_method_async
async def replace_one(
self,
filter: FilterType,
replacement: DocumentType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Replace a single document on the collection with a new one,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
replacement: the new document to write into the collection.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, `replacement` is inserted as a new document
if no matches are found on the collection. If False,
the operation silently does nothing in case of no matches.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an UpdateResult object summarizing the outcome of the replace operation.
Example:
>>> async def do_replace_one(acol: AsyncCollection) -> None:
... await acol.insert_one({"Marco": "Polo"})
... result0 = await acol.replace_one(
... {"Marco": {"$exists": True}},
... {"Buda": "Pest"},
... )
... print("result0.update_info", result0.update_info)
... doc1 = await acol.find_one({"Buda": "Pest"})
... print("doc1", doc1)
... result1 = await acol.replace_one(
... {"Mirco": {"$exists": True}},
... {"Oh": "yeah?"},
... )
... print("result1.update_info", result1.update_info)
... result2 = await acol.replace_one(
... {"Mirco": {"$exists": True}},
... {"Oh": "yeah?"},
... upsert=True,
... )
... print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_replace_one(my_async_coll))
result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}
doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'}
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'}
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_replace on '{self.name}'")
fo_response = await self._astra_db_collection.find_one_and_replace(
replacement=replacement,
filter=filter,
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_replace on '{self.name}'")
if "document" in fo_response.get("data", {}):
fo_status = fo_response.get("status") or {}
_update_info = _prepare_update_info([fo_status])
return UpdateResult(
raw_results=[fo_response],
update_info=_update_info,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_replace API command.",
raw_response=fo_response,
)
@recast_method_async
async def find_one_and_update(
self,
filter: FilterType,
update: Dict[str, Any],
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
return_document: str = ReturnDocument.BEFORE,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document on the collection and update it as requested,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the document, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
return_document: a flag controlling what document is returned:
if set to `ReturnDocument.BEFORE`, or the string "before",
the document found on database is returned; if set to
`ReturnDocument.AFTER`, or the string "after", the new
document is returned. The default is "before".
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
A document (or a projection thereof, as required), either the one
before the replace operation or the one after that.
Alternatively, the method returns None to represent
that no matching document was found, or that no update
was applied (depending on the `return_document` parameter).
Example:
>>> async def do_find_one_and_update(acol: AsyncCollection) -> None:
... await acol.insert_one({"Marco": "Polo"})
... result0 = await acol.find_one_and_update(
... {"Marco": {"$exists": True}},
... {"$set": {"title": "Mr."}},
... )
... print("result0", result0)
... result1 = await acol.find_one_and_update(
... {"title": "Mr."},
... {"$inc": {"rank": 3}},
... projection=["title", "rank"],
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
... print("result1", result1)
... result2 = await acol.find_one_and_update(
... {"name": "Johnny"},
... {"$set": {"rank": 0}},
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
... print("result2", result2)
... result3 = await acol.find_one_and_update(
... {"name": "Johnny"},
... {"$set": {"rank": 0}},
... upsert=True,
... return_document=astrapy.constants.ReturnDocument.AFTER,
... )
... print("result3", result3)
...
>>> asyncio.run(do_find_one_and_update(my_async_coll))
result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'}
result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3}
result2 None
result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0}
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"returnDocument": return_document,
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_update on '{self.name}'")
fo_response = await self._astra_db_collection.find_one_and_update(
update=update,
filter=filter,
projection=normalize_optional_projection(projection),
sort=_sort,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_update on '{self.name}'")
if "document" in fo_response.get("data", {}):
ret_document = fo_response.get("data", {}).get("document")
if ret_document is None:
return None
else:
return ret_document # type: ignore[no-any-return]
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_update API command.",
raw_response=fo_response,
)
@recast_method_async
async def update_one(
self,
filter: FilterType,
update: Dict[str, Any],
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Update a single document on the collection as requested,
optionally inserting a new one if no match is found.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the document, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
upsert: this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
an UpdateResult object summarizing the outcome of the update operation.
Example:
>>> async def do_update_one(acol: AsyncCollection) -> None:
... await acol.insert_one({"Marco": "Polo"})
... result0 = await acol.update_one(
... {"Marco": {"$exists": True}},
... {"$inc": {"rank": 3}},
... )
... print("result0.update_info", result0.update_info)
... result1 = await acol.update_one(
... {"Mirko": {"$exists": True}},
... {"$inc": {"rank": 3}},
... )
... print("result1.update_info", result1.update_info)
... result2 = await acol.update_one(
... {"Mirko": {"$exists": True}},
... {"$inc": {"rank": 3}},
... upsert=True,
... )
... print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_update_one(my_async_coll))
result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1})
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0})
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'}
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
options = {
"upsert": upsert,
}
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_update on '{self.name}'")
fo_response = await self._astra_db_collection.find_one_and_update(
update=update,
sort=_sort,
filter=filter,
options=options,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_update on '{self.name}'")
if "document" in fo_response.get("data", {}):
fo_status = fo_response.get("status") or {}
_update_info = _prepare_update_info([fo_status])
return UpdateResult(
raw_results=[fo_response],
update_info=_update_info,
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_update API command.",
raw_response=fo_response,
)
@recast_method_async
async def update_many(
self,
filter: FilterType,
update: Dict[str, Any],
*,
upsert: bool = False,
max_time_ms: Optional[int] = None,
) -> UpdateResult:
"""
Apply an update operations to all documents matching a condition,
optionally inserting one documents in absence of matches.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
update: the update prescription to apply to the documents, expressed
as a dictionary as per Data API syntax. Examples are:
{"$set": {"field": "value}}
{"$inc": {"counter": 10}}
{"$unset": {"field": ""}}
See the Data API documentation for the full syntax.
upsert: this parameter controls the behavior in absence of matches.
If True, a single new document (resulting from applying `update`
to an empty document) is inserted if no matches are found on
the collection. If False, the operation silently does nothing
in case of no matches.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
if a large number of document updates is anticipated, it is suggested
to specify a larger timeout than in most other operations as the
update will span several HTTP calls to the API in sequence.
Returns:
an UpdateResult object summarizing the outcome of the update operation.
Example:
>>> async def do_update_many(acol: AsyncCollection) -> None:
... await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}])
... result0 = await acol.update_many(
... {"c": {"$ne": "green"}},
... {"$set": {"nongreen": True}},
... )
... print("result0.update_info", result0.update_info)
... result1 = await acol.update_many(
... {"c": "orange"},
... {"$set": {"is_also_fruit": True}},
... )
... print("result1.update_info", result1.update_info)
... result2 = await acol.update_many(
... {"c": "orange"},
... {"$set": {"is_also_fruit": True}},
... upsert=True,
... )
... print("result2.update_info", result2.update_info)
...
>>> asyncio.run(do_update_many(my_async_coll))
result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}
result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}
result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'}
Note:
Similarly to the case of `find` (see its docstring for more details),
running this command while, at the same time, another process is
inserting new documents which match the filter of the `update_many`
can result in an unpredictable fraction of these documents being updated.
In other words, it cannot be easily predicted whether a given
newly-inserted document will be picked up by the update_many command or not.
"""
api_options = {
"upsert": upsert,
}
page_state_options: Dict[str, str] = {}
um_responses: List[Dict[str, Any]] = []
um_statuses: List[Dict[str, Any]] = []
must_proceed = True
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"starting update_many on '{self.name}'")
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
while must_proceed:
options = {**api_options, **page_state_options}
logger.info(f"calling update_many on '{self.name}'")
this_um_response = await self._astra_db_collection.update_many(
update=update,
filter=filter,
options=options,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished calling update_many on '{self.name}'")
this_um_status = this_um_response.get("status") or {}
#
# if errors, quit early
if this_um_response.get("errors", []):
partial_update_info = _prepare_update_info(um_statuses)
partial_result = UpdateResult(
raw_results=um_responses,
update_info=partial_update_info,
)
all_um_responses = um_responses + [this_um_response]
raise UpdateManyException.from_responses(
commands=[None for _ in all_um_responses],
raw_responses=all_um_responses,
partial_result=partial_result,
)
else:
if "status" not in this_um_response:
raise DataAPIFaultyResponseException(
text="Faulty response from update_many API command.",
raw_response=this_um_response,
)
um_responses.append(this_um_response)
um_statuses.append(this_um_status)
next_page_state = this_um_status.get("nextPageState")
if next_page_state is not None:
must_proceed = True
page_state_options = {"pageState": next_page_state}
else:
must_proceed = False
page_state_options = {}
update_info = _prepare_update_info(um_statuses)
logger.info(f"finished update_many on '{self.name}'")
return UpdateResult(
raw_results=um_responses,
update_info=update_info,
)
@recast_method_async
async def find_one_and_delete(
self,
filter: FilterType,
*,
projection: Optional[ProjectionType] = None,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> Union[DocumentType, None]:
"""
Find a document in the collection and delete it. The deleted document,
however, is the return value of the method.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
projection: it controls which parts of the document are returned.
It can be an allow-list: `{"f1": True, "f2": True}`,
or a deny-list: `{"fx": False, "fy": False}`, but not a mixture
(except for the `_id` and other special fields, which can be
associated to both True or False independently of the rest
of the specification).
The special star-projections `{"*": True}` and `{"*": False}`
have the effect of returning the whole document and `{}` respectively.
For lists in documents, slice directives can be passed to select
portions of the list: for instance, `{"array": {"$slice": 2}}`,
`{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or
`{"array": {"$slice": [-4, 2]}}`.
An iterable over strings will be treated implicitly as an allow-list.
The default projection (used if this parameter is not passed) does not
necessarily include "special" fields such as `$vector` or `$vectorize`.
See the Data API documentation for more on projections.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
Either the document (or a projection thereof, as requested), or None
if no matches were found in the first place.
Example:
>>> async def do_find_one_and_delete(acol: AsyncCollection) -> None:
... await acol.insert_many(
... [
... {"species": "swan", "class": "Aves"},
... {"species": "frog", "class": "Amphibia"},
... ],
... )
... delete_result0 = await acol.find_one_and_delete(
... {"species": {"$ne": "frog"}},
... projection=["species"],
... )
... print("delete_result0", delete_result0)
... delete_result1 = await acol.find_one_and_delete(
... {"species": {"$ne": "frog"}},
... )
... print("delete_result1", delete_result1)
...
>>> asyncio.run(do_find_one_and_delete(my_async_coll))
delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'}
delete_result1 None
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_projection = normalize_optional_projection(projection)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling find_one_and_delete on '{self.name}'")
fo_response = await self._astra_db_collection.find_one_and_delete(
sort=_sort,
filter=filter,
projection=_projection,
timeout_info=base_timeout_info(_max_time_ms),
)
logger.info(f"finished calling find_one_and_delete on '{self.name}'")
if "document" in fo_response.get("data", {}):
document = fo_response["data"]["document"]
return document # type: ignore[no-any-return]
else:
deleted_count = fo_response.get("status", {}).get("deletedCount")
if deleted_count == 0:
return None
else:
raise DataAPIFaultyResponseException(
text="Faulty response from find_one_and_delete API command.",
raw_response=fo_response,
)
@recast_method_async
async def delete_one(
self,
filter: FilterType,
*,
vector: Optional[VectorType] = None,
vectorize: Optional[str] = None,
sort: Optional[SortType] = None,
max_time_ms: Optional[int] = None,
) -> DeleteResult:
"""
Delete one document matching a provided filter.
This method never deletes more than a single document, regardless
of the number of matches to the provided filters.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
vector: a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
*DEPRECATED* (removal in 2.0). Use a `$vector` key in the
sort clause dict instead.
vectorize: a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
*DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the
sort clause dict instead.
sort: with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the `find` method for more on sorting.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in `sort`.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a DeleteResult object summarizing the outcome of the delete operation.
Example:
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
InsertManyResult(...)
>>> my_coll.delete_one({"seq": 1})
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0, 2]
>>> my_coll.delete_one(
... {"seq": {"$exists": True}},
... sort={"seq": astrapy.constants.SortDocuments.DESCENDING},
... )
DeleteResult(raw_results=..., deleted_count=1)
>>> my_coll.distinct("seq")
[0]
>>> my_coll.delete_one({"seq": 2})
DeleteResult(raw_results=..., deleted_count=0)
"""
check_deprecated_vector_ize(
vector=vector,
vectors=None,
vectorize=vectorize,
kind="find",
from_async_method=True,
)
_sort = _collate_vector_to_sort(sort, vector, vectorize)
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling delete_one_by_predicate on '{self.name}'")
do_response = await self._astra_db_collection.delete_one_by_predicate(
filter=filter,
timeout_info=base_timeout_info(_max_time_ms),
sort=_sort,
)
logger.info(f"finished calling delete_one_by_predicate on '{self.name}'")
if "deletedCount" in do_response.get("status", {}):
deleted_count = do_response["status"]["deletedCount"]
if deleted_count == -1:
return DeleteResult(
deleted_count=None,
raw_results=[do_response],
)
else:
# expected a non-negative integer:
return DeleteResult(
deleted_count=deleted_count,
raw_results=[do_response],
)
else:
raise DataAPIFaultyResponseException(
text="Faulty response from delete_one API command.",
raw_response=do_response,
)
@recast_method_async
async def delete_many(
self,
filter: FilterType,
*,
max_time_ms: Optional[int] = None,
) -> DeleteResult:
"""
Delete all documents matching a provided filter.
Args:
filter: a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
Passing an empty filter, `{}`, completely erases all contents
of the collection.
max_time_ms: a timeout, in milliseconds, for the operation.
If not passed, the collection-level setting is used instead:
keep in mind that this method entails successive HTTP requests
to the API, depending on how many documents are to be deleted.
For this reason, in most cases it is suggested to relax the
timeout compared to other method calls.
Returns:
a DeleteResult object summarizing the outcome of the delete operation.
Example:
>>> async def do_delete_many(acol: AsyncCollection) -> None:
... await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}])
... delete_result0 = await acol.delete_many({"seq": {"$lte": 1}})
... print("delete_result0.deleted_count", delete_result0.deleted_count)
... distinct1 = await acol.distinct("seq")
... print("distinct1", distinct1)
... delete_result2 = await acol.delete_many({"seq": {"$lte": 1}})
... print("delete_result2.deleted_count", delete_result2.deleted_count)
...
>>> asyncio.run(do_delete_many(my_async_coll))
delete_result0.deleted_count 2
distinct1 [2]
delete_result2.deleted_count 0
Note:
This operation is in general not atomic. Depending on the amount
of matching documents, it can keep running (in a blocking way)
for a macroscopic time. In that case, new documents that are
meanwhile inserted (e.g. from another process/application) will be
deleted during the execution of this method call until the
collection is devoid of matches.
An exception is the `filter={}` case, whereby the operation is atomic.
"""
dm_responses: List[Dict[str, Any]] = []
deleted_count = 0
must_proceed = True
_max_time_ms = max_time_ms or self.api_options.max_time_ms
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
logger.info(f"starting delete_many on '{self.name}'")
while must_proceed:
logger.info(f"calling delete_many on '{self.name}'")
this_dm_response = await self._astra_db_collection.delete_many(
filter=filter,
skip_error_check=True,
timeout_info=timeout_manager.remaining_timeout_info(),
)
logger.info(f"finished calling delete_many on '{self.name}'")
# if errors, quit early
if this_dm_response.get("errors", []):
partial_result = DeleteResult(
deleted_count=deleted_count,
raw_results=dm_responses,
)
all_dm_responses = dm_responses + [this_dm_response]
raise DeleteManyException.from_responses(
commands=[None for _ in all_dm_responses],
raw_responses=all_dm_responses,
partial_result=partial_result,
)
else:
this_dc = this_dm_response.get("status", {}).get("deletedCount")
if this_dc is None:
raise DataAPIFaultyResponseException(
text="Faulty response from delete_many API command.",
raw_response=this_dm_response,
)
dm_responses.append(this_dm_response)
deleted_count += this_dc
must_proceed = this_dm_response.get("status", {}).get("moreData", False)
logger.info(f"finished delete_many on '{self.name}'")
return DeleteResult(
deleted_count=deleted_count,
raw_results=dm_responses,
)
@deprecation.deprecated( # type: ignore[misc]
deprecated_in="1.3.0",
removed_in="2.0.0",
current_version=__version__,
details="Use delete_many with filter={} instead.",
)
async def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
"""
Delete all documents in a collection.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary of the form {"ok": 1} to signal successful deletion.
Example:
>>> async def do_delete_all(acol: AsyncCollection) -> None:
... distinct0 = await acol.distinct("seq")
... print("distinct0", distinct0)
... count1 = await acol.count_documents({}, upper_bound=100)
... print("count1", count1)
... delete_result2 = await acol.delete_all()
... print("delete_result2", delete_result2)
... count3 = await acol.count_documents({}, upper_bound=100)
... print("count3", count3)
...
>>> asyncio.run(do_delete_all(my_async_coll))
distinct0 [4, 2, 3, 0, 1]
count1 5
delete_result2 {'ok': 1}
count3 0
Note:
Use with caution.
"""
dm_result = await self.delete_many(filter={}, max_time_ms=max_time_ms)
if dm_result.deleted_count == -1:
return {"ok": 1}
else:
raise DataAPIFaultyResponseException(
text="Unexpected response from collection.delete_many({}).",
raw_response=None,
)
async def bulk_write(
self,
requests: Iterable[AsyncBaseOperation],
*,
ordered: bool = False,
concurrency: Optional[int] = None,
max_time_ms: Optional[int] = None,
) -> BulkWriteResult:
"""
Execute an arbitrary amount of operations such as inserts, updates, deletes
either sequentially or concurrently.
This method does not execute atomically, i.e. individual operations are
each performed in the same way as the corresponding collection method,
and each one is a different and unrelated database mutation.
Args:
requests: an iterable over concrete subclasses of `BaseOperation`,
such as `AsyncInsertMany` or `AsyncReplaceOne`. Each such object
represents an operation ready to be executed on a collection,
and is instantiated by passing the same parameters as one
would the corresponding collection method.
ordered: whether to launch the `requests` one after the other or
in arbitrary order, possibly in a concurrent fashion. For
performance reasons, False (default) should be preferred
when compatible with the needs of the application flow.
concurrency: maximum number of concurrent operations executing at
a given time. It cannot be more than one for ordered bulk writes.
max_time_ms: a timeout, in milliseconds, for the whole bulk write.
Remember that, if the method call times out, then there's no
guarantee about what portion of the bulk write has been received
and successfully executed by the Data API.
If not passed, the collection-level setting is used instead:
in most cases, however, one should pass a relaxed timeout
if longer sequences of operations are to be executed in bulk.
Returns:
A single BulkWriteResult summarizing the whole list of requested
operations. The keys in the map attributes of BulkWriteResult
(when present) are the integer indices of the corresponding operation
in the `requests` iterable.
Example:
>>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation
>>> from astrapy.results import BulkWriteResult
>>>
>>> async def do_bulk_write(
... acol: AsyncCollection,
... async_operations: List[AsyncOperation],
... ) -> BulkWriteResult:
... bw_result = await acol.bulk_write(async_operations)
... count0 = await acol.count_documents({}, upper_bound=100)
... print("count0", count0)
... distinct0 = await acol.distinct("replaced")
... print("distinct0", distinct0)
... return bw_result
...
>>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}])
>>> op2 = AsyncReplaceOne(
... {"z": 9},
... replacement={"z": 9, "replaced": True},
... upsert=True,
... )
>>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2]))
count0 3
distinct0 [True]
>>> print("result", result)
result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'})
"""
# lazy importing here against circular-import error
from astrapy.operations import reduce_bulk_write_results
if concurrency is None:
if ordered:
_concurrency = 1
else:
_concurrency = DEFAULT_BULK_WRITE_CONCURRENCY
else:
_concurrency = concurrency
if _concurrency > 1 and ordered:
raise ValueError("Cannot run ordered bulk_write concurrently.")
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"startng a bulk write on '{self.name}'")
timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms)
if ordered:
bulk_write_results: List[BulkWriteResult] = []
for operation_i, operation in enumerate(requests):
try:
this_bw_result = await operation.execute(
self,
index_in_bulk_write=operation_i,
bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
)
bulk_write_results.append(this_bw_result)
except CumulativeOperationException as exc:
partial_result = exc.partial_result
partial_bw_result = reduce_bulk_write_results(
bulk_write_results
+ [
partial_result.to_bulk_write_result(
index_in_bulk_write=operation_i
)
]
)
dar_exception = exc.data_api_response_exception()
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=[dar_exception],
)
except DataAPIResponseException as exc:
# the cumulative exceptions, with their
# partially-done-info, are handled above:
# here it's just one-shot d.a.r. exceptions
partial_bw_result = reduce_bulk_write_results(bulk_write_results)
dar_exception = exc.data_api_response_exception()
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=[dar_exception],
)
full_bw_result = reduce_bulk_write_results(bulk_write_results)
logger.info(f"finished a bulk write on '{self.name}'")
return full_bw_result
else:
sem = asyncio.Semaphore(_concurrency)
async def _concurrent_execute_as_either(
operation: AsyncBaseOperation, operation_i: int
) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]:
async with sem:
try:
ex_result = await operation.execute(
self,
index_in_bulk_write=operation_i,
bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(),
)
return (ex_result, None)
except DataAPIResponseException as exc:
return (None, exc)
tasks = [
asyncio.create_task(
_concurrent_execute_as_either(operation, operation_i)
)
for operation_i, operation in enumerate(requests)
]
bulk_write_either_results = await asyncio.gather(*tasks)
# regroup
bulk_write_successes = [bwr for bwr, _ in bulk_write_either_results if bwr]
bulk_write_failures = [bwf for _, bwf in bulk_write_either_results if bwf]
if bulk_write_failures:
# extract and cumulate
partial_results_from_failures = [
failure.partial_result.to_bulk_write_result(
index_in_bulk_write=operation_i
)
for failure in bulk_write_failures
if isinstance(failure, CumulativeOperationException)
]
partial_bw_result = reduce_bulk_write_results(
bulk_write_successes + partial_results_from_failures
)
# raise and recast the first exception
all_dar_exceptions = [
bw_failure.data_api_response_exception()
for bw_failure in bulk_write_failures
]
dar_exception = all_dar_exceptions[0]
raise BulkWriteException(
text=dar_exception.text,
error_descriptors=dar_exception.error_descriptors,
detailed_error_descriptors=dar_exception.detailed_error_descriptors,
partial_result=partial_bw_result,
exceptions=all_dar_exceptions,
)
else:
logger.info(f"finished a bulk write on '{self.name}'")
return reduce_bulk_write_results(bulk_write_successes)
async def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]:
"""
Drop the collection, i.e. delete it from the database along with
all the documents it contains.
Args:
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Remember there is not guarantee that a request that has
timed out us not in fact honored.
Returns:
a dictionary of the form {"ok": 1} to signal successful deletion.
Example:
>>> async def drop_and_check(acol: AsyncCollection) -> None:
... doc0 = await acol.find_one({})
... print("doc0", doc0)
... drop_result = await acol.drop()
... print("drop_result", drop_result)
... doc1 = await acol.find_one({})
...
>>> asyncio.run(drop_and_check(my_async_coll))
doc0 {'_id': '...', 'z': -10}
drop_result {'ok': 1}
Traceback (most recent call last):
... ...
astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection
Note:
Use with caution.
Note:
Once the method succeeds, methods on this object can still be invoked:
however, this hardly makes sense as the underlying actual collection
is no more.
It is responsibility of the developer to design a correct flow
which avoids using a deceased collection any further.
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"dropping collection '{self.name}' (self)")
drop_result = await self.database.drop_collection(
self, max_time_ms=_max_time_ms
)
logger.info(f"finished dropping collection '{self.name}' (self)")
return drop_result # type: ignore[no-any-return]
async def command(
self,
body: Dict[str, Any],
*,
max_time_ms: Optional[int] = None,
) -> Dict[str, Any]:
"""
Send a POST request to the Data API for this collection with
an arbitrary, caller-provided payload.
Args:
body: a JSON-serializable dictionary, the payload of the request.
max_time_ms: a timeout, in milliseconds, for the underlying HTTP request.
If not passed, the collection-level setting is used instead.
Returns:
a dictionary with the response of the HTTP request.
Example:
>>> asyncio.await(my_async_coll.command({"countDocuments": {}}))
{'status': {'count': 123}}
"""
_max_time_ms = max_time_ms or self.api_options.max_time_ms
logger.info(f"calling command on '{self.name}'")
command_result = await self.database.command(
body=body,
namespace=self.namespace,
collection_name=self.name,
max_time_ms=_max_time_ms,
)
logger.info(f"finished calling command on '{self.name}'")
return command_result # type: ignore[no-any-return]Classes
class AsyncCollection (database: AsyncDatabase, name: str, *, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None)-
A Data API collection, the main object to interact with the Data API, especially for DDL operations. This class has a synchronous interface.
A Collection is spawned from a Database object, from which it inherits the details on how to reach the API server (endpoint, authentication token).
Args
database- a Database object, instantiated earlier. This represents the database the collection belongs to.
name- the collection name. This parameter should match an existing collection on the database.
namespace- this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.
api_options- An instance of
astrapy.api_options.CollectionAPIOptionsproviding the general settings for interacting with the Data API. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Examples
>>> from astrapy import DataAPIClient, AsyncCollection >>> my_client = astrapy.DataAPIClient("AstraCS:...") >>> my_async_db = my_client.get_async_database( ... "https://01234567-....apps.astra.datastax.com" ... ) >>> my_async_coll_1 = AsyncCollection(database=my_async_db, name="my_collection") >>> my_async coll_2 = asyncio.run(my_async_db.create_collection( ... "my_v_collection", ... dimension=3, ... metric="cosine", ... )) >>> my_async_coll_3a = asyncio.run(my_async_db.get_collection( ... "my_already_existing_collection", ... )) >>> my_async_coll_3b = my_async_db.my_already_existing_collection >>> my_async_coll_3c = my_async_db["my_already_existing_collection"]Note
creating an instance of Collection does not trigger actual creation of the collection on the database. The latter should have been created beforehand, e.g. through the
create_collectionmethod of a Database.Expand source code
class AsyncCollection: """ A Data API collection, the main object to interact with the Data API, especially for DDL operations. This class has a synchronous interface. A Collection is spawned from a Database object, from which it inherits the details on how to reach the API server (endpoint, authentication token). Args: database: a Database object, instantiated earlier. This represents the database the collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. api_options: An instance of `astrapy.api_options.CollectionAPIOptions` providing the general settings for interacting with the Data API. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Examples: >>> from astrapy import DataAPIClient, AsyncCollection >>> my_client = astrapy.DataAPIClient("AstraCS:...") >>> my_async_db = my_client.get_async_database( ... "https://01234567-....apps.astra.datastax.com" ... ) >>> my_async_coll_1 = AsyncCollection(database=my_async_db, name="my_collection") >>> my_async coll_2 = asyncio.run(my_async_db.create_collection( ... "my_v_collection", ... dimension=3, ... metric="cosine", ... )) >>> my_async_coll_3a = asyncio.run(my_async_db.get_collection( ... "my_already_existing_collection", ... )) >>> my_async_coll_3b = my_async_db.my_already_existing_collection >>> my_async_coll_3c = my_async_db["my_already_existing_collection"] Note: creating an instance of Collection does not trigger actual creation of the collection on the database. The latter should have been created beforehand, e.g. through the `create_collection` method of a Database. """ def __init__( self, database: AsyncDatabase, name: str, *, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: if api_options is None: self.api_options = CollectionAPIOptions() else: self.api_options = api_options additional_headers = self.api_options.embedding_api_key.get_headers() self._astra_db_collection: AsyncAstraDBCollection = AsyncAstraDBCollection( collection_name=name, astra_db=database._astra_db, namespace=namespace, caller_name=caller_name, caller_version=caller_version, additional_headers=additional_headers, ) # this comes after the above, lets AstraDBCollection resolve namespace self._database = database._copy( namespace=self._astra_db_collection.astra_db.namespace ) def __repr__(self) -> str: return ( f'{self.__class__.__name__}(name="{self.name}", ' f'namespace="{self.namespace}", database={self.database}, ' f"api_options={self.api_options})" ) def __eq__(self, other: Any) -> bool: if isinstance(other, AsyncCollection): return all( [ self._astra_db_collection == other._astra_db_collection, self.api_options == other.api_options, ] ) else: return False def __call__(self, *pargs: Any, **kwargs: Any) -> None: raise TypeError( f"'{self.__class__.__name__}' object is not callable. If you " f"meant to call the '{self.name}' method on a " f"'{self.database.__class__.__name__}' object " "it is failing because no such method exists." ) def _copy( self, *, database: Optional[AsyncDatabase] = None, name: Optional[str] = None, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> AsyncCollection: return AsyncCollection( database=database or self.database._copy(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) def with_options( self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> AsyncCollection: """ Create a clone of this collection with some changed attributes. Args: name: the name of the collection. This parameter is useful to quickly spawn AsyncCollection instances each pointing to a different collection existing in the same namespace. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: a new AsyncCollection instance. Example: >>> my_other_async_coll = my_async_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... ) """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return self._copy( name=name, api_options=_api_options, caller_name=caller_name, caller_version=caller_version, ) def to_sync( self, *, database: Optional[Database] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> Collection: """ Create a Collection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into a sync object). Args: database: a Database object, instantiated earlier. This represents the database the new collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: the new copy, a Collection instance. Example: >>> my_async_coll.to_sync().count_documents({}, upper_bound=100) 77 """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return Collection( database=database or self.database.to_sync(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(_api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) def set_caller( self, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: """ Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller"). Args: caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Example: >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0") """ logger.info(f"setting caller to {caller_name}/{caller_version}") self._astra_db_collection.set_caller( caller_name=caller_name, caller_version=caller_version, ) async def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions: """ Get the collection options, i.e. its configuration as read from the database. The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a CollectionOptions instance describing the collection. (See also the database `list_collections` method.) Example: >>> asyncio.run(my_async_coll.options()) CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine')) """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"getting collections in search of '{self.name}'") self_descriptors = [ coll_desc async for coll_desc in self.database.list_collections( max_time_ms=_max_time_ms ) if coll_desc.name == self.name ] logger.info(f"finished getting collections in search of '{self.name}'") if self_descriptors: return self_descriptors[0].options # type: ignore[no-any-return] else: raise CollectionNotFoundException( text=f"Collection {self.namespace}.{self.name} not found.", namespace=self.namespace, collection_name=self.name, ) def info(self) -> CollectionInfo: """ Information on the collection (name, location, database), in the form of a CollectionInfo object. Not to be confused with the collection `options` method (related to the collection internal configuration). Example: >>> my_async_coll.info().database_info.region 'us-east1' >>> my_async_coll.info().full_name 'default_keyspace.my_v_collection' Note: the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for `Database.info()` for more details. """ return CollectionInfo( database_info=self.database.info(), namespace=self.namespace, name=self.name, full_name=self.full_name, ) @property def database(self) -> AsyncDatabase: """ a Database object, the database this collection belongs to. Example: >>> my_async_coll.database.name 'quicktest' """ return self._database @property def namespace(self) -> str: """ The namespace this collection is in. Example: >>> my_async_coll.database.namespace 'default_keyspace' """ _namespace = self.database.namespace if _namespace is None: raise ValueError("The collection's DB is set with namespace=None") return _namespace @property def name(self) -> str: """ The name of this collection. Example: >>> my_async_coll.name 'my_v_collection' """ # type hint added as for some reason the typechecker gets lost return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type] @property def full_name(self) -> str: """ The fully-qualified collection name within the database, in the form "namespace.collection_name". Example: >>> my_async_coll.full_name 'default_keyspace.my_v_collection' """ return f"{self.namespace}.{self.name}" @recast_method_async async def insert_one( self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None, ) -> InsertOneResult: """ Insert a single document in the collection in an atomic operation. Args: document: the dictionary expressing the document to insert. The `_id` field of the document can be left out, in which case it will be created automatically. vector: a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing a `$vector` field within the document itself, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead. vectorize: a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with `vector`. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an InsertOneResult object. Example: >>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... await acol.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) ... await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) ... count1 = await acol.count_documents({}, upper_bound=10) ... print("count1", count1) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 count1 2 >>> asyncio.run(my_async_coll.insert_one({"tag": v", "$vector": [10, 11]})) InsertOneResult(...) Note: If an `_id` is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="insert", from_async_method=True, ) _document = _collate_vector_to_document(document, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting one document in '{self.name}'") io_response = await self._astra_db_collection.insert_one( _document, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished inserting one document in '{self.name}'") if "insertedIds" in io_response.get("status", {}): if io_response["status"]["insertedIds"]: inserted_id = io_response["status"]["insertedIds"][0] return InsertOneResult( raw_results=[io_response], inserted_id=inserted_id, ) else: raise ValueError( "Could not complete a insert_one operation. " f"(gotten '${json.dumps(io_response)}')" ) else: raise ValueError( "Could not complete a insert_one operation. " f"(gotten '${json.dumps(io_response)}')" ) @recast_method_async async def insert_many( self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> InsertManyResult: """ Insert a list of documents into the collection. This is not an atomic operation. Args: documents: an iterable of dictionaries, each a document to insert. Documents may specify their `_id` field or leave it out, in which case it will be added automatically. vectors: an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead. vectorize: an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the documents themselves, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead. ordered: if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster. chunk_size: how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default. concurrency: maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here. Returns: an InsertManyResult object. Examples: >>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... im_result1 = await acol.insert_many( ... [ ... {"a": 10}, ... {"a": 5}, ... {"b": [True, False, False]}, ... ], ... ordered=True, ... ) ... print("inserted1", im_result1.inserted_ids) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... await acol.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) ... count2 = await acol.count_documents({}, upper_bound=100) ... print("count2", count2) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...'] count1 3 count2 53 >>> asyncio.run(my_async_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... )) InsertManyResult(...) Note: Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important. Note: A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an `_id` already present on the collection, or its vector dimension may not match the collection setting. For an ordered insertion, the method will raise an exception at the first such faulty document -- nevertheless, all documents processed until then will end up being written to the database. For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised -- and all insertable documents will have been written to the database, including those "after" the troublesome ones. If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole `insert_many` operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database. """ check_deprecated_vector_ize( vector=None, vectors=vectors, vectorize=vectorize, kind="insert", from_async_method=True, ) if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered insert_many concurrently.") if chunk_size is None: _chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS else: _chunk_size = chunk_size _documents = _collate_vectors_to_documents(documents, vectors, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting {len(_documents)} documents in '{self.name}'") raw_results: List[Dict[str, Any]] = [] timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: options = {"ordered": True} inserted_ids: List[Any] = [] for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") chunk_response = await self._astra_db_collection.insert_many( documents=_documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished inserting a chunk of documents in '{self.name}'") # accumulate the results in this call chunk_inserted_ids = (chunk_response.get("status") or {}).get( "insertedIds", [] ) inserted_ids += chunk_inserted_ids raw_results += [chunk_response] # if errors, quit early if chunk_response.get("errors", []): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_response( command=None, raw_response=chunk_response, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result else: # unordered: concurrent or not, do all of them and parse the results options = {"ordered": False} sem = asyncio.Semaphore(_concurrency) async def concurrent_insert_chunk( document_chunk: List[DocumentType], ) -> Dict[str, Any]: async with sem: logger.info(f"inserting a chunk of documents in '{self.name}'") im_response = await self._astra_db_collection.insert_many( document_chunk, options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) return im_response if _concurrency > 1: tasks = [ asyncio.create_task( concurrent_insert_chunk(_documents[i : i + _chunk_size]) ) for i in range(0, len(_documents), _chunk_size) ] raw_results = await asyncio.gather(*tasks) else: raw_results = [ await concurrent_insert_chunk(_documents[i : i + _chunk_size]) for i in range(0, len(_documents), _chunk_size) ] # recast raw_results inserted_ids = [ inserted_id for chunk_response in raw_results for inserted_id in (chunk_response.get("status") or {}).get( "insertedIds", [] ) ] # check-raise if any( [chunk_response.get("errors", []) for chunk_response in raw_results] ): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_responses( commands=[None for _ in raw_results], raw_responses=raw_results, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result def find( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> AsyncCursor: """ Find documents on the collection, matching a certain provided filter. The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the `find` method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the `sort` option in a non-vector-search manner. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. skip: with this integer parameter, what would be the first `skip` documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit `sort` criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit: this (integer) parameter sets a limit over how many documents are returned. Once `limit` is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. include_sort_vector: a boolean to request query vector used in this search. If set to True (and if the invocation is a vector search), calling the `get_sort_vector` method on the returned cursor will yield the vector used for the ANN search. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead. Returns: an AsyncCursor object representing iterations over the matching documents (see the AsyncCursor object for how to use it. The simplest thing is to run a for loop: `for document in collection.sort(...):`). Examples: >>> async def run_finds(acol: AsyncCollection) -> None: ... filter = {"seq": {"$exists": True}} ... print("find results 1:") ... async for doc in acol.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... async_cursor1 = acol.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... ids = [doc["_id"] async for doc in async_cursor1] ... print("find results 2:", ids) ... async_cursor2 = acol.find({}, limit=3) ... seqs = await async_cursor2.distinct("seq") ... print("distinct results 3:", seqs) ... >>> asyncio.run(run_finds(my_async_coll)) find results 1: 48 35 7 11 13 find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...'] distinct results 3: [48, 35, 7] >>> async def run_vector_finds(acol: AsyncCollection) -> None: ... await acol.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) ... ann_tags = [ ... document["tag"] ... async for document in acol.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] ... return ann_tags ... >>> asyncio.run(run_vector_finds(my_async_coll)) ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE) >>> async_cursor = my_async_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0] >>> asyncio.run(async_cursor.__anext__()) {'_id': 'b13ce177-738e-47ec-bce1-77738ee7ec93', 'tag': 'A'} >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0] Note: The following are example values for the `sort` parameter. When no particular order is required: sort={} When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a `collections.OrderedDict` in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]} Note: Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as `.distinct()` on a cursor. Note: When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the `find` was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms if include_similarity is not None and not _is_vector_sort(_sort): raise ValueError( "Cannot use `include_similarity` when not searching through `vector`." ) return ( AsyncCursor( collection=self, filter=filter, projection=projection, max_time_ms=_max_time_ms, overall_max_time_ms=None, ) .skip(skip) .limit(limit) .sort(_sort) .include_similarity(include_similarity) .include_sort_vector(include_sort_vector) ) async def find_one( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Run a search, returning the first document in the collection that matches provided filters, if any is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary expressing the required document, otherwise None. Example: >>> async def demo_find_one(acol: AsyncCollection) -> None: .... print("Count:", await acol.count_documents({}, upper_bound=100)) ... result0 = await acol.find_one({}) ... print("result0", result0) ... result1 = await acol.find_one({"seq": 10}) ... print("result1", result1) ... result2 = await acol.find_one({"seq": 1011}) ... print("result2", result2) ... result3 = await acol.find_one({}, projection={"seq": False}) ... print("result3", result3) ... result4 = await acol.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... print("result4", result4) ... >>> >>> asyncio.run(demo_find_one(my_async_coll)) Count: 50 result0 {'_id': '479c7ce8-...', 'seq': 48} result1 {'_id': '93e992c4-...', 'seq': 10} result2 None result3 {'_id': '479c7ce8-...'} result4 {'_id': 'd656cd9d-...', 'seq': 49} >>> asyncio.run(my_async_coll.find_one( ... {}, ... sort={"$vector": [1, 0]}, ... projection={"*": True}, ... )) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]} Note: See the `find` method for more details on the accepted parameters (whereas `skip` and `limit` are not valid parameters for `find_one`). """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_cursor = self.find( filter=filter, projection=projection, skip=None, limit=1, vector=vector, vectorize=vectorize, include_similarity=include_similarity, sort=sort, max_time_ms=_max_time_ms, ) try: document = await fo_cursor.__anext__() return document # type: ignore[no-any-return] except StopAsyncIteration: return None async def distinct( self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None, ) -> List[Any]: """ Return a list of the unique values of `key` across the documents in the collection that match the provided filter. Args: key: the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable `key` values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`. If not passed, the collection-level setting is used instead. Returns: a list of all different values for `key` found across the documents that match the filter. The result list has no repeated items. Example: >>> async def run_distinct(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) ... distinct0 = await acol.distinct("name") ... print("distinct('name')", distinct0) ... distinct1 = await acol.distinct("city") ... print("distinct('city')", distinct1) ... distinct2 = await acol.distinct("food") ... print("distinct('food')", distinct2) ... distinct3 = await acol.distinct("food.1") ... print("distinct('food.1')", distinct3) ... distinct4 = await acol.distinct("food.allergies") ... print("distinct('food.allergies')", distinct4) ... distinct5 = await acol.distinct("food.likes_fruit") ... print("distinct('food.likes_fruit')", distinct5) ... >>> asyncio.run(run_distinct(my_async_coll)) distinct('name') ['Emma', 'Marco'] distinct('city') ['Helsinki'] distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange'] distinct('food.1') ['orange'] distinct('food.allergies') [] distinct('food.likes_fruit') [True] Note: It must be kept in mind that `distinct` is a client-side operation, which effectively browses all required documents using the logic of the `find` method and collects the unique values found for `key`. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large. Note: For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the `find` command. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms f_cursor = AsyncCursor( collection=self, filter=filter, projection={key: True}, max_time_ms=None, overall_max_time_ms=_max_time_ms, ) return await f_cursor.distinct(key) # type: ignore[no-any-return] @recast_method_async async def count_documents( self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None, ) -> int: """ Count the documents in the collection matching the specified filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. upper_bound: a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: the exact count of matching documents. Example: >>> async def do_count_docs(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": i} for i in range(20)]) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100) ... print("count1", count1) ... count2 = await acol.count_documents({}, upper_bound=10) ... print("count2", count2) ... >>> asyncio.run(do_count_docs(my_async_coll)) count0 20 count1 4 Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountException Note: Count operations are expensive: for this reason, the best practice is to provide a reasonable `upper_bound` according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info("calling count_documents") cd_response = await self._astra_db_collection.count_documents( filter=filter, timeout_info=base_timeout_info(_max_time_ms), ) logger.info("finished calling count_documents") if "count" in cd_response.get("status", {}): count: int = cd_response["status"]["count"] if cd_response["status"].get("moreData", False): raise TooManyDocumentsToCountException( text=f"Document count exceeds {count}, the maximum allowed by the server", server_max_count_exceeded=True, ) else: if count > upper_bound: raise TooManyDocumentsToCountException( text="Document count exceeds required upper bound", server_max_count_exceeded=False, ) else: return count else: raise DataAPIFaultyResponseException( text="Faulty response from count_documents API command.", raw_response=cd_response, ) async def estimated_document_count( self, *, max_time_ms: Optional[int] = None, ) -> int: """ Query the API server for an estimate of the document count in the collection. Contrary to `count_documents`, this method has no filtering parameters. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a server-provided estimate count of the documents in the collection. Example: >>> asyncio.run(my_async_coll.estimated_document_count()) 35700 """ _max_time_ms = max_time_ms or self.api_options.max_time_ms ed_response = await self.command( {"estimatedDocumentCount": {}}, max_time_ms=_max_time_ms, ) if "count" in ed_response.get("status", {}): count: int = ed_response["status"]["count"] return count else: raise DataAPIFaultyResponseException( text="Faulty response from estimated_document_count API command.", raw_response=ed_response, ) @recast_method_async async def find_one_and_replace( self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document, either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the `return_document` parameter). Example: >>> async def do_find_one_and_replace(acol: AsyncCollection) -> None: ... await acol.insert_one({"_id": "rule1", "text": "all animals are equal"}) ... result0 = await acol.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_replace(my_async_coll)) result0 {'_id': 'rule1', 'text': 'all animals are equal'} result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'} result2 None result3 {'text': 'F=ma'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) @recast_method_async async def replace_one( self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Replace a single document on the collection with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the replace operation. Example: >>> async def do_replace_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.replace_one( ... {"Marco": {"$exists": True}}, ... {"Buda": "Pest"}, ... ) ... print("result0.update_info", result0.update_info) ... doc1 = await acol.find_one({"Buda": "Pest"}) ... print("doc1", doc1) ... result1 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_replace_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1} doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) @recast_method_async async def find_one_and_update( self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and update it as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the `return_document` parameter). Example: >>> async def do_find_one_and_update(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_update(my_async_coll)) result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'} result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3} result2 None result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_update( update=update, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) @recast_method_async async def update_one( self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Update a single document on the collection as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> async def do_update_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.update_one( ... {"Marco": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_update( update=update, sort=_sort, filter=filter, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) @recast_method_async async def update_many( self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. upsert: this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> async def do_update_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) ... result0 = await acol.update_many( ... {"c": {"$ne": "green"}}, ... {"$set": {"nongreen": True}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_many(my_async_coll)) result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'} Note: Similarly to the case of `find` (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the `update_many` can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not. """ api_options = { "upsert": upsert, } page_state_options: Dict[str, str] = {} um_responses: List[Dict[str, Any]] = [] um_statuses: List[Dict[str, Any]] = [] must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"starting update_many on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) while must_proceed: options = {**api_options, **page_state_options} logger.info(f"calling update_many on '{self.name}'") this_um_response = await self._astra_db_collection.update_many( update=update, filter=filter, options=options, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling update_many on '{self.name}'") this_um_status = this_um_response.get("status") or {} # # if errors, quit early if this_um_response.get("errors", []): partial_update_info = _prepare_update_info(um_statuses) partial_result = UpdateResult( raw_results=um_responses, update_info=partial_update_info, ) all_um_responses = um_responses + [this_um_response] raise UpdateManyException.from_responses( commands=[None for _ in all_um_responses], raw_responses=all_um_responses, partial_result=partial_result, ) else: if "status" not in this_um_response: raise DataAPIFaultyResponseException( text="Faulty response from update_many API command.", raw_response=this_um_response, ) um_responses.append(this_um_response) um_statuses.append(this_um_status) next_page_state = this_um_status.get("nextPageState") if next_page_state is not None: must_proceed = True page_state_options = {"pageState": next_page_state} else: must_proceed = False page_state_options = {} update_info = _prepare_update_info(um_statuses) logger.info(f"finished update_many on '{self.name}'") return UpdateResult( raw_results=um_responses, update_info=update_info, ) @recast_method_async async def find_one_and_delete( self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document in the collection and delete it. The deleted document, however, is the return value of the method. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: Either the document (or a projection thereof, as requested), or None if no matches were found in the first place. Example: >>> async def do_find_one_and_delete(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) ... delete_result0 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) ... print("delete_result0", delete_result0) ... delete_result1 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... ) ... print("delete_result1", delete_result1) ... >>> asyncio.run(do_find_one_and_delete(my_async_coll)) delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'} delete_result1 None """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _projection = normalize_optional_projection(projection) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_delete on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_delete( sort=_sort, filter=filter, projection=_projection, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_delete on '{self.name}'") if "document" in fo_response.get("data", {}): document = fo_response["data"]["document"] return document # type: ignore[no-any-return] else: deleted_count = fo_response.get("status", {}).get("deletedCount") if deleted_count == 0: return None else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_delete API command.", raw_response=fo_response, ) @recast_method_async async def delete_one( self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling delete_one_by_predicate on '{self.name}'") do_response = await self._astra_db_collection.delete_one_by_predicate( filter=filter, timeout_info=base_timeout_info(_max_time_ms), sort=_sort, ) logger.info(f"finished calling delete_one_by_predicate on '{self.name}'") if "deletedCount" in do_response.get("status", {}): deleted_count = do_response["status"]["deletedCount"] if deleted_count == -1: return DeleteResult( deleted_count=None, raw_results=[do_response], ) else: # expected a non-negative integer: return DeleteResult( deleted_count=deleted_count, raw_results=[do_response], ) else: raise DataAPIFaultyResponseException( text="Faulty response from delete_one API command.", raw_response=do_response, ) @recast_method_async async def delete_many( self, filter: FilterType, *, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete all documents matching a provided filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. Passing an empty filter, `{}`, completely erases all contents of the collection. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> async def do_delete_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) ... delete_result0 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result0.deleted_count", delete_result0.deleted_count) ... distinct1 = await acol.distinct("seq") ... print("distinct1", distinct1) ... delete_result2 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result2.deleted_count", delete_result2.deleted_count) ... >>> asyncio.run(do_delete_many(my_async_coll)) delete_result0.deleted_count 2 distinct1 [2] delete_result2.deleted_count 0 Note: This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the `filter={}` case, whereby the operation is atomic. """ dm_responses: List[Dict[str, Any]] = [] deleted_count = 0 must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) logger.info(f"starting delete_many on '{self.name}'") while must_proceed: logger.info(f"calling delete_many on '{self.name}'") this_dm_response = await self._astra_db_collection.delete_many( filter=filter, skip_error_check=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling delete_many on '{self.name}'") # if errors, quit early if this_dm_response.get("errors", []): partial_result = DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) all_dm_responses = dm_responses + [this_dm_response] raise DeleteManyException.from_responses( commands=[None for _ in all_dm_responses], raw_responses=all_dm_responses, partial_result=partial_result, ) else: this_dc = this_dm_response.get("status", {}).get("deletedCount") if this_dc is None: raise DataAPIFaultyResponseException( text="Faulty response from delete_many API command.", raw_response=this_dm_response, ) dm_responses.append(this_dm_response) deleted_count += this_dc must_proceed = this_dm_response.get("status", {}).get("moreData", False) logger.info(f"finished delete_many on '{self.name}'") return DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) @deprecation.deprecated( # type: ignore[misc] deprecated_in="1.3.0", removed_in="2.0.0", current_version=__version__, details="Use delete_many with filter={} instead.", ) async def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Delete all documents in a collection. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> async def do_delete_all(acol: AsyncCollection) -> None: ... distinct0 = await acol.distinct("seq") ... print("distinct0", distinct0) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... delete_result2 = await acol.delete_all() ... print("delete_result2", delete_result2) ... count3 = await acol.count_documents({}, upper_bound=100) ... print("count3", count3) ... >>> asyncio.run(do_delete_all(my_async_coll)) distinct0 [4, 2, 3, 0, 1] count1 5 delete_result2 {'ok': 1} count3 0 Note: Use with caution. """ dm_result = await self.delete_many(filter={}, max_time_ms=max_time_ms) if dm_result.deleted_count == -1: return {"ok": 1} else: raise DataAPIFaultyResponseException( text="Unexpected response from collection.delete_many({}).", raw_response=None, ) async def bulk_write( self, requests: Iterable[AsyncBaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> BulkWriteResult: """ Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently. This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation. Args: requests: an iterable over concrete subclasses of `BaseOperation`, such as `AsyncInsertMany` or `AsyncReplaceOne`. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered: whether to launch the `requests` one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency: maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes. max_time_ms: a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk. Returns: A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the `requests` iterable. Example: >>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation >>> from astrapy.results import BulkWriteResult >>> >>> async def do_bulk_write( ... acol: AsyncCollection, ... async_operations: List[AsyncOperation], ... ) -> BulkWriteResult: ... bw_result = await acol.bulk_write(async_operations) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... distinct0 = await acol.distinct("replaced") ... print("distinct0", distinct0) ... return bw_result ... >>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}]) >>> op2 = AsyncReplaceOne( ... {"z": 9}, ... replacement={"z": 9, "replaced": True}, ... upsert=True, ... ) >>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2])) count0 3 distinct0 [True] >>> print("result", result) result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'}) """ # lazy importing here against circular-import error from astrapy.operations import reduce_bulk_write_results if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered bulk_write concurrently.") _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"startng a bulk write on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: bulk_write_results: List[BulkWriteResult] = [] for operation_i, operation in enumerate(requests): try: this_bw_result = await operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) bulk_write_results.append(this_bw_result) except CumulativeOperationException as exc: partial_result = exc.partial_result partial_bw_result = reduce_bulk_write_results( bulk_write_results + [ partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) ] ) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) except DataAPIResponseException as exc: # the cumulative exceptions, with their # partially-done-info, are handled above: # here it's just one-shot d.a.r. exceptions partial_bw_result = reduce_bulk_write_results(bulk_write_results) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) full_bw_result = reduce_bulk_write_results(bulk_write_results) logger.info(f"finished a bulk write on '{self.name}'") return full_bw_result else: sem = asyncio.Semaphore(_concurrency) async def _concurrent_execute_as_either( operation: AsyncBaseOperation, operation_i: int ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]: async with sem: try: ex_result = await operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) return (ex_result, None) except DataAPIResponseException as exc: return (None, exc) tasks = [ asyncio.create_task( _concurrent_execute_as_either(operation, operation_i) ) for operation_i, operation in enumerate(requests) ] bulk_write_either_results = await asyncio.gather(*tasks) # regroup bulk_write_successes = [bwr for bwr, _ in bulk_write_either_results if bwr] bulk_write_failures = [bwf for _, bwf in bulk_write_either_results if bwf] if bulk_write_failures: # extract and cumulate partial_results_from_failures = [ failure.partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) for failure in bulk_write_failures if isinstance(failure, CumulativeOperationException) ] partial_bw_result = reduce_bulk_write_results( bulk_write_successes + partial_results_from_failures ) # raise and recast the first exception all_dar_exceptions = [ bw_failure.data_api_response_exception() for bw_failure in bulk_write_failures ] dar_exception = all_dar_exceptions[0] raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=all_dar_exceptions, ) else: logger.info(f"finished a bulk write on '{self.name}'") return reduce_bulk_write_results(bulk_write_successes) async def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Drop the collection, i.e. delete it from the database along with all the documents it contains. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> async def drop_and_check(acol: AsyncCollection) -> None: ... doc0 = await acol.find_one({}) ... print("doc0", doc0) ... drop_result = await acol.drop() ... print("drop_result", drop_result) ... doc1 = await acol.find_one({}) ... >>> asyncio.run(drop_and_check(my_async_coll)) doc0 {'_id': '...', 'z': -10} drop_result {'ok': 1} Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection Note: Use with caution. Note: Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"dropping collection '{self.name}' (self)") drop_result = await self.database.drop_collection( self, max_time_ms=_max_time_ms ) logger.info(f"finished dropping collection '{self.name}' (self)") return drop_result # type: ignore[no-any-return] async def command( self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None, ) -> Dict[str, Any]: """ Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload. Args: body: a JSON-serializable dictionary, the payload of the request. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary with the response of the HTTP request. Example: >>> asyncio.await(my_async_coll.command({"countDocuments": {}})) {'status': {'count': 123}} """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling command on '{self.name}'") command_result = await self.database.command( body=body, namespace=self.namespace, collection_name=self.name, max_time_ms=_max_time_ms, ) logger.info(f"finished calling command on '{self.name}'") return command_result # type: ignore[no-any-return]Instance variables
var database : AsyncDatabase-
a Database object, the database this collection belongs to.
Example
>>> my_async_coll.database.name 'quicktest'Expand source code
@property def database(self) -> AsyncDatabase: """ a Database object, the database this collection belongs to. Example: >>> my_async_coll.database.name 'quicktest' """ return self._database var full_name : str-
The fully-qualified collection name within the database, in the form "namespace.collection_name".
Example
>>> my_async_coll.full_name 'default_keyspace.my_v_collection'Expand source code
@property def full_name(self) -> str: """ The fully-qualified collection name within the database, in the form "namespace.collection_name". Example: >>> my_async_coll.full_name 'default_keyspace.my_v_collection' """ return f"{self.namespace}.{self.name}" var name : str-
The name of this collection.
Example
>>> my_async_coll.name 'my_v_collection'Expand source code
@property def name(self) -> str: """ The name of this collection. Example: >>> my_async_coll.name 'my_v_collection' """ # type hint added as for some reason the typechecker gets lost return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type] var namespace : str-
The namespace this collection is in.
Example
>>> my_async_coll.database.namespace 'default_keyspace'Expand source code
@property def namespace(self) -> str: """ The namespace this collection is in. Example: >>> my_async_coll.database.namespace 'default_keyspace' """ _namespace = self.database.namespace if _namespace is None: raise ValueError("The collection's DB is set with namespace=None") return _namespace
Methods
async def bulk_write(self, requests: Iterable[AsyncBaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> BulkWriteResult-
Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently.
This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation.
Args
requests- an iterable over concrete subclasses of
BaseOperation, such asAsyncInsertManyorAsyncReplaceOne. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered- whether to launch the
requestsone after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency- maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes.
max_time_ms- a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk.
Returns
A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the
requestsiterable.Example
>>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation >>> from astrapy.results import BulkWriteResult >>> >>> async def do_bulk_write( ... acol: AsyncCollection, ... async_operations: List[AsyncOperation], ... ) -> BulkWriteResult: ... bw_result = await acol.bulk_write(async_operations) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... distinct0 = await acol.distinct("replaced") ... print("distinct0", distinct0) ... return bw_result ... >>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}]) >>> op2 = AsyncReplaceOne( ... {"z": 9}, ... replacement={"z": 9, "replaced": True}, ... upsert=True, ... ) >>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2])) count0 3 distinct0 [True] >>> print("result", result) result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'})Expand source code
async def bulk_write( self, requests: Iterable[AsyncBaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> BulkWriteResult: """ Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently. This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation. Args: requests: an iterable over concrete subclasses of `BaseOperation`, such as `AsyncInsertMany` or `AsyncReplaceOne`. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered: whether to launch the `requests` one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency: maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes. max_time_ms: a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk. Returns: A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the `requests` iterable. Example: >>> from astrapy.operations import AsyncInsertMany, AsyncReplaceOne, AsyncOperation >>> from astrapy.results import BulkWriteResult >>> >>> async def do_bulk_write( ... acol: AsyncCollection, ... async_operations: List[AsyncOperation], ... ) -> BulkWriteResult: ... bw_result = await acol.bulk_write(async_operations) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... distinct0 = await acol.distinct("replaced") ... print("distinct0", distinct0) ... return bw_result ... >>> op1 = AsyncInsertMany([{"a": 1}, {"a": 2}]) >>> op2 = AsyncReplaceOne( ... {"z": 9}, ... replacement={"z": 9, "replaced": True}, ... upsert=True, ... ) >>> result = asyncio.run(do_bulk_write(my_async_coll, [op1, op2])) count0 3 distinct0 [True] >>> print("result", result) result BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: 'ccd0a800-...'}) """ # lazy importing here against circular-import error from astrapy.operations import reduce_bulk_write_results if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered bulk_write concurrently.") _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"startng a bulk write on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: bulk_write_results: List[BulkWriteResult] = [] for operation_i, operation in enumerate(requests): try: this_bw_result = await operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) bulk_write_results.append(this_bw_result) except CumulativeOperationException as exc: partial_result = exc.partial_result partial_bw_result = reduce_bulk_write_results( bulk_write_results + [ partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) ] ) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) except DataAPIResponseException as exc: # the cumulative exceptions, with their # partially-done-info, are handled above: # here it's just one-shot d.a.r. exceptions partial_bw_result = reduce_bulk_write_results(bulk_write_results) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) full_bw_result = reduce_bulk_write_results(bulk_write_results) logger.info(f"finished a bulk write on '{self.name}'") return full_bw_result else: sem = asyncio.Semaphore(_concurrency) async def _concurrent_execute_as_either( operation: AsyncBaseOperation, operation_i: int ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]: async with sem: try: ex_result = await operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) return (ex_result, None) except DataAPIResponseException as exc: return (None, exc) tasks = [ asyncio.create_task( _concurrent_execute_as_either(operation, operation_i) ) for operation_i, operation in enumerate(requests) ] bulk_write_either_results = await asyncio.gather(*tasks) # regroup bulk_write_successes = [bwr for bwr, _ in bulk_write_either_results if bwr] bulk_write_failures = [bwf for _, bwf in bulk_write_either_results if bwf] if bulk_write_failures: # extract and cumulate partial_results_from_failures = [ failure.partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) for failure in bulk_write_failures if isinstance(failure, CumulativeOperationException) ] partial_bw_result = reduce_bulk_write_results( bulk_write_successes + partial_results_from_failures ) # raise and recast the first exception all_dar_exceptions = [ bw_failure.data_api_response_exception() for bw_failure in bulk_write_failures ] dar_exception = all_dar_exceptions[0] raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=all_dar_exceptions, ) else: logger.info(f"finished a bulk write on '{self.name}'") return reduce_bulk_write_results(bulk_write_successes) async def command(self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload.
Args
body- a JSON-serializable dictionary, the payload of the request.
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary with the response of the HTTP request.
Example
>>> asyncio.await(my_async_coll.command({"countDocuments": {}})) {'status': {'count': 123}}Expand source code
async def command( self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None, ) -> Dict[str, Any]: """ Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload. Args: body: a JSON-serializable dictionary, the payload of the request. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary with the response of the HTTP request. Example: >>> asyncio.await(my_async_coll.command({"countDocuments": {}})) {'status': {'count': 123}} """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling command on '{self.name}'") command_result = await self.database.command( body=body, namespace=self.namespace, collection_name=self.name, max_time_ms=_max_time_ms, ) logger.info(f"finished calling command on '{self.name}'") return command_result # type: ignore[no-any-return] async def count_documents(self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None) ‑> int-
Count the documents in the collection matching the specified filter.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
upper_bound- a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised.
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
the exact count of matching documents.
Example
>>> async def do_count_docs(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": i} for i in range(20)]) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100) ... print("count1", count1) ... count2 = await acol.count_documents({}, upper_bound=10) ... print("count2", count2) ... >>> asyncio.run(do_count_docs(my_async_coll)) count0 20 count1 4 Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountExceptionNote
Count operations are expensive: for this reason, the best practice is to provide a reasonable
upper_boundaccording to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered.Expand source code
@recast_method_async async def count_documents( self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None, ) -> int: """ Count the documents in the collection matching the specified filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. upper_bound: a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: the exact count of matching documents. Example: >>> async def do_count_docs(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": i} for i in range(20)]) ... count0 = await acol.count_documents({}, upper_bound=100) ... print("count0", count0) ... count1 = await acol.count_documents({"seq":{"$gt": 15}}, upper_bound=100) ... print("count1", count1) ... count2 = await acol.count_documents({}, upper_bound=10) ... print("count2", count2) ... >>> asyncio.run(do_count_docs(my_async_coll)) count0 20 count1 4 Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountException Note: Count operations are expensive: for this reason, the best practice is to provide a reasonable `upper_bound` according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info("calling count_documents") cd_response = await self._astra_db_collection.count_documents( filter=filter, timeout_info=base_timeout_info(_max_time_ms), ) logger.info("finished calling count_documents") if "count" in cd_response.get("status", {}): count: int = cd_response["status"]["count"] if cd_response["status"].get("moreData", False): raise TooManyDocumentsToCountException( text=f"Document count exceeds {count}, the maximum allowed by the server", server_max_count_exceeded=True, ) else: if count > upper_bound: raise TooManyDocumentsToCountException( text="Document count exceeds required upper bound", server_max_count_exceeded=False, ) else: return count else: raise DataAPIFaultyResponseException( text="Faulty response from count_documents API command.", raw_response=cd_response, ) async def delete_all(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Delete all documents in a collection.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary of the form {"ok": 1} to signal successful deletion.
Example
>>> async def do_delete_all(acol: AsyncCollection) -> None: ... distinct0 = await acol.distinct("seq") ... print("distinct0", distinct0) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... delete_result2 = await acol.delete_all() ... print("delete_result2", delete_result2) ... count3 = await acol.count_documents({}, upper_bound=100) ... print("count3", count3) ... >>> asyncio.run(do_delete_all(my_async_coll)) distinct0 [4, 2, 3, 0, 1] count1 5 delete_result2 {'ok': 1} count3 0Note
Use with caution.
Deprecated since version: 1.3.0
This will be removed in 2.0.0. Use delete_many with filter={} instead.
Expand source code
@deprecation.deprecated( # type: ignore[misc] deprecated_in="1.3.0", removed_in="2.0.0", current_version=__version__, details="Use delete_many with filter={} instead.", ) async def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Delete all documents in a collection. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> async def do_delete_all(acol: AsyncCollection) -> None: ... distinct0 = await acol.distinct("seq") ... print("distinct0", distinct0) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... delete_result2 = await acol.delete_all() ... print("delete_result2", delete_result2) ... count3 = await acol.count_documents({}, upper_bound=100) ... print("count3", count3) ... >>> asyncio.run(do_delete_all(my_async_coll)) distinct0 [4, 2, 3, 0, 1] count1 5 delete_result2 {'ok': 1} count3 0 Note: Use with caution. """ dm_result = await self.delete_many(filter={}, max_time_ms=max_time_ms) if dm_result.deleted_count == -1: return {"ok": 1} else: raise DataAPIFaultyResponseException( text="Unexpected response from collection.delete_many({}).", raw_response=None, ) async def delete_many(self, filter: FilterType, *, max_time_ms: Optional[int] = None) ‑> DeleteResult-
Delete all documents matching a provided filter.
Args
filter- a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
Passing an empty filter,
{}, completely erases all contents of the collection. max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls.
Returns
a DeleteResult object summarizing the outcome of the delete operation.
Example
>>> async def do_delete_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) ... delete_result0 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result0.deleted_count", delete_result0.deleted_count) ... distinct1 = await acol.distinct("seq") ... print("distinct1", distinct1) ... delete_result2 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result2.deleted_count", delete_result2.deleted_count) ... >>> asyncio.run(do_delete_many(my_async_coll)) delete_result0.deleted_count 2 distinct1 [2] delete_result2.deleted_count 0Note
This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the
filter={}case, whereby the operation is atomic.Expand source code
@recast_method_async async def delete_many( self, filter: FilterType, *, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete all documents matching a provided filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. Passing an empty filter, `{}`, completely erases all contents of the collection. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> async def do_delete_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) ... delete_result0 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result0.deleted_count", delete_result0.deleted_count) ... distinct1 = await acol.distinct("seq") ... print("distinct1", distinct1) ... delete_result2 = await acol.delete_many({"seq": {"$lte": 1}}) ... print("delete_result2.deleted_count", delete_result2.deleted_count) ... >>> asyncio.run(do_delete_many(my_async_coll)) delete_result0.deleted_count 2 distinct1 [2] delete_result2.deleted_count 0 Note: This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the `filter={}` case, whereby the operation is atomic. """ dm_responses: List[Dict[str, Any]] = [] deleted_count = 0 must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) logger.info(f"starting delete_many on '{self.name}'") while must_proceed: logger.info(f"calling delete_many on '{self.name}'") this_dm_response = await self._astra_db_collection.delete_many( filter=filter, skip_error_check=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling delete_many on '{self.name}'") # if errors, quit early if this_dm_response.get("errors", []): partial_result = DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) all_dm_responses = dm_responses + [this_dm_response] raise DeleteManyException.from_responses( commands=[None for _ in all_dm_responses], raw_responses=all_dm_responses, partial_result=partial_result, ) else: this_dc = this_dm_response.get("status", {}).get("deletedCount") if this_dc is None: raise DataAPIFaultyResponseException( text="Faulty response from delete_many API command.", raw_response=this_dm_response, ) dm_responses.append(this_dm_response) deleted_count += this_dc must_proceed = this_dm_response.get("status", {}).get("moreData", False) logger.info(f"finished delete_many on '{self.name}'") return DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) async def delete_one(self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> DeleteResult-
Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a DeleteResult object summarizing the outcome of the delete operation.
Example
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0)Expand source code
@recast_method_async async def delete_one( self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling delete_one_by_predicate on '{self.name}'") do_response = await self._astra_db_collection.delete_one_by_predicate( filter=filter, timeout_info=base_timeout_info(_max_time_ms), sort=_sort, ) logger.info(f"finished calling delete_one_by_predicate on '{self.name}'") if "deletedCount" in do_response.get("status", {}): deleted_count = do_response["status"]["deletedCount"] if deleted_count == -1: return DeleteResult( deleted_count=None, raw_results=[do_response], ) else: # expected a non-negative integer: return DeleteResult( deleted_count=deleted_count, raw_results=[do_response], ) else: raise DataAPIFaultyResponseException( text="Faulty response from delete_one API command.", raw_response=do_response, ) async def distinct(self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None) ‑> List[Any]-
Return a list of the unique values of
keyacross the documents in the collection that match the provided filter.Args
key- the name of the field whose value is inspected across documents.
Keys can use dot-notation to descend to deeper document levels.
Example of acceptable
keyvalues: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
max_time_ms- a timeout, in milliseconds, with the same meaning as for
find. If not passed, the collection-level setting is used instead.
Returns
a list of all different values for
keyfound across the documents that match the filter. The result list has no repeated items.Example
>>> async def run_distinct(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) ... distinct0 = await acol.distinct("name") ... print("distinct('name')", distinct0) ... distinct1 = await acol.distinct("city") ... print("distinct('city')", distinct1) ... distinct2 = await acol.distinct("food") ... print("distinct('food')", distinct2) ... distinct3 = await acol.distinct("food.1") ... print("distinct('food.1')", distinct3) ... distinct4 = await acol.distinct("food.allergies") ... print("distinct('food.allergies')", distinct4) ... distinct5 = await acol.distinct("food.likes_fruit") ... print("distinct('food.likes_fruit')", distinct5) ... >>> asyncio.run(run_distinct(my_async_coll)) distinct('name') ['Emma', 'Marco'] distinct('city') ['Helsinki'] distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange'] distinct('food.1') ['orange'] distinct('food.allergies') [] distinct('food.likes_fruit') [True]Note
It must be kept in mind that
distinctis a client-side operation, which effectively browses all required documents using the logic of thefindmethod and collects the unique values found forkey. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large.Note
For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the
findcommand.Expand source code
async def distinct( self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None, ) -> List[Any]: """ Return a list of the unique values of `key` across the documents in the collection that match the provided filter. Args: key: the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable `key` values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`. If not passed, the collection-level setting is used instead. Returns: a list of all different values for `key` found across the documents that match the filter. The result list has no repeated items. Example: >>> async def run_distinct(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) ... distinct0 = await acol.distinct("name") ... print("distinct('name')", distinct0) ... distinct1 = await acol.distinct("city") ... print("distinct('city')", distinct1) ... distinct2 = await acol.distinct("food") ... print("distinct('food')", distinct2) ... distinct3 = await acol.distinct("food.1") ... print("distinct('food.1')", distinct3) ... distinct4 = await acol.distinct("food.allergies") ... print("distinct('food.allergies')", distinct4) ... distinct5 = await acol.distinct("food.likes_fruit") ... print("distinct('food.likes_fruit')", distinct5) ... >>> asyncio.run(run_distinct(my_async_coll)) distinct('name') ['Emma', 'Marco'] distinct('city') ['Helsinki'] distinct('food') [{'likes_fruit': True, 'allergies': []}, 'apple', 'orange'] distinct('food.1') ['orange'] distinct('food.allergies') [] distinct('food.likes_fruit') [True] Note: It must be kept in mind that `distinct` is a client-side operation, which effectively browses all required documents using the logic of the `find` method and collects the unique values found for `key`. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large. Note: For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the `find` command. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms f_cursor = AsyncCursor( collection=self, filter=filter, projection={key: True}, max_time_ms=None, overall_max_time_ms=_max_time_ms, ) return await f_cursor.distinct(key) # type: ignore[no-any-return] async def drop(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Drop the collection, i.e. delete it from the database along with all the documents it contains.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored.
Returns
a dictionary of the form {"ok": 1} to signal successful deletion.
Example
>>> async def drop_and_check(acol: AsyncCollection) -> None: ... doc0 = await acol.find_one({}) ... print("doc0", doc0) ... drop_result = await acol.drop() ... print("drop_result", drop_result) ... doc1 = await acol.find_one({}) ... >>> asyncio.run(drop_and_check(my_async_coll)) doc0 {'_id': '...', 'z': -10} drop_result {'ok': 1} Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collectionNote
Use with caution.
Note
Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.
Expand source code
async def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Drop the collection, i.e. delete it from the database along with all the documents it contains. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> async def drop_and_check(acol: AsyncCollection) -> None: ... doc0 = await acol.find_one({}) ... print("doc0", doc0) ... drop_result = await acol.drop() ... print("drop_result", drop_result) ... doc1 = await acol.find_one({}) ... >>> asyncio.run(drop_and_check(my_async_coll)) doc0 {'_id': '...', 'z': -10} drop_result {'ok': 1} Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection Note: Use with caution. Note: Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"dropping collection '{self.name}' (self)") drop_result = await self.database.drop_collection( self, max_time_ms=_max_time_ms ) logger.info(f"finished dropping collection '{self.name}' (self)") return drop_result # type: ignore[no-any-return] async def estimated_document_count(self, *, max_time_ms: Optional[int] = None) ‑> int-
Query the API server for an estimate of the document count in the collection.
Contrary to
count_documents, this method has no filtering parameters.Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a server-provided estimate count of the documents in the collection.
Example
>>> asyncio.run(my_async_coll.estimated_document_count()) 35700Expand source code
async def estimated_document_count( self, *, max_time_ms: Optional[int] = None, ) -> int: """ Query the API server for an estimate of the document count in the collection. Contrary to `count_documents`, this method has no filtering parameters. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a server-provided estimate count of the documents in the collection. Example: >>> asyncio.run(my_async_coll.estimated_document_count()) 35700 """ _max_time_ms = max_time_ms or self.api_options.max_time_ms ed_response = await self.command( {"estimatedDocumentCount": {}}, max_time_ms=_max_time_ms, ) if "count" in ed_response.get("status", {}): count: int = ed_response["status"]["count"] return count else: raise DataAPIFaultyResponseException( text="Faulty response from estimated_document_count API command.", raw_response=ed_response, ) def find(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> AsyncCursor-
Find documents on the collection, matching a certain provided filter.
The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the
findmethod was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of thesortoption in a non-vector-search manner.Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. skip- with this integer parameter, what would be the first
skipdocuments returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicitsortcriterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit- this (integer) parameter sets a limit over how many documents
are returned. Once
limitis reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search).
When running similarity search on a collection, no other sorting
criteria can be specified. Moreover, there is an upper bound
to the number of documents that can be returned. For details,
see the Note about upper bounds and the Data API documentation.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to
vector, provided such a service is configured for the collection, and achieves the same effect. DEPRECATED (removal in 2.0). Use a$vectorizekey in the sort clause dict instead. include_similarity- a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in each
returned document. Can only be used for vector ANN search, i.e.
when either
vectoris supplied or thesortparameter has the shape {"$vector": …}. include_sort_vector- a boolean to request query vector used in this search.
If set to True (and if the invocation is a vector search), calling
the
get_sort_vectormethod on the returned cursor will yield the vector used for the ANN search. sort- with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting, as well as
the one about upper bounds, for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in
sort. max_time_ms- a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead.
Returns
- an AsyncCursor object representing iterations over the matching documents
- (see the AsyncCursor object for how to use it. The simplest thing is to
run a for loopfor document in collection.sort(...):).
Examples
>>> async def run_finds(acol: AsyncCollection) -> None: ... filter = {"seq": {"$exists": True}} ... print("find results 1:") ... async for doc in acol.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... async_cursor1 = acol.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... ids = [doc["_id"] async for doc in async_cursor1] ... print("find results 2:", ids) ... async_cursor2 = acol.find({}, limit=3) ... seqs = await async_cursor2.distinct("seq") ... print("distinct results 3:", seqs) ... >>> asyncio.run(run_finds(my_async_coll)) find results 1: 48 35 7 11 13 find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...'] distinct results 3: [48, 35, 7]>>> async def run_vector_finds(acol: AsyncCollection) -> None: ... await acol.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) ... ann_tags = [ ... document["tag"] ... async for document in acol.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] ... return ann_tags ... >>> asyncio.run(run_vector_finds(my_async_coll)) ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE)>>> async_cursor = my_async_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0] >>> asyncio.run(async_cursor.__anext__()) {'_id': 'b13ce177-738e-47ec-bce1-77738ee7ec93', 'tag': 'A'} >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0]Note
The following are example values for the
sortparameter. When no particular order is required: sort={} When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ acollections.OrderedDictin these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]}Note
Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as
.distinct()on a cursor.Note
When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the
findwas started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor.Expand source code
def find( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> AsyncCursor: """ Find documents on the collection, matching a certain provided filter. The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the `find` method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the `sort` option in a non-vector-search manner. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. skip: with this integer parameter, what would be the first `skip` documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit `sort` criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit: this (integer) parameter sets a limit over how many documents are returned. Once `limit` is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. include_sort_vector: a boolean to request query vector used in this search. If set to True (and if the invocation is a vector search), calling the `get_sort_vector` method on the returned cursor will yield the vector used for the ANN search. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead. Returns: an AsyncCursor object representing iterations over the matching documents (see the AsyncCursor object for how to use it. The simplest thing is to run a for loop: `for document in collection.sort(...):`). Examples: >>> async def run_finds(acol: AsyncCollection) -> None: ... filter = {"seq": {"$exists": True}} ... print("find results 1:") ... async for doc in acol.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... async_cursor1 = acol.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... ids = [doc["_id"] async for doc in async_cursor1] ... print("find results 2:", ids) ... async_cursor2 = acol.find({}, limit=3) ... seqs = await async_cursor2.distinct("seq") ... print("distinct results 3:", seqs) ... >>> asyncio.run(run_finds(my_async_coll)) find results 1: 48 35 7 11 13 find results 2: ['d656cd9d-...', '479c7ce8-...', '96dc87fd-...', '83f0a21f-...'] distinct results 3: [48, 35, 7] >>> async def run_vector_finds(acol: AsyncCollection) -> None: ... await acol.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) ... ann_tags = [ ... document["tag"] ... async for document in acol.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] ... return ann_tags ... >>> asyncio.run(run_vector_finds(my_async_coll)) ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE) >>> async_cursor = my_async_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0] >>> asyncio.run(async_cursor.__anext__()) {'_id': 'b13ce177-738e-47ec-bce1-77738ee7ec93', 'tag': 'A'} >>> asyncio.run(async_cursor.get_sort_vector()) [3.0, 3.0] Note: The following are example values for the `sort` parameter. When no particular order is required: sort={} When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a `collections.OrderedDict` in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]} Note: Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as `.distinct()` on a cursor. Note: When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the `find` was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms if include_similarity is not None and not _is_vector_sort(_sort): raise ValueError( "Cannot use `include_similarity` when not searching through `vector`." ) return ( AsyncCursor( collection=self, filter=filter, projection=projection, max_time_ms=_max_time_ms, overall_max_time_ms=None, ) .skip(skip) .limit(limit) .sort(_sort) .include_similarity(include_similarity) .include_sort_vector(include_sort_vector) ) async def find_one(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Run a search, returning the first document in the collection that matches provided filters, if any is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search), extracting the most
similar document in the collection matching the filter.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. include_similarity- a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in the
returned document. Can only be used for vector ANN search, i.e.
when either
vectoris supplied or thesortparameter has the shape {"$vector": …}. sort- with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in
sort. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary expressing the required document, otherwise None.
Example
>>> async def demo_find_one(acol: AsyncCollection) -> None: .... print("Count:", await acol.count_documents({}, upper_bound=100)) ... result0 = await acol.find_one({}) ... print("result0", result0) ... result1 = await acol.find_one({"seq": 10}) ... print("result1", result1) ... result2 = await acol.find_one({"seq": 1011}) ... print("result2", result2) ... result3 = await acol.find_one({}, projection={"seq": False}) ... print("result3", result3) ... result4 = await acol.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... print("result4", result4) ... >>> >>> asyncio.run(demo_find_one(my_async_coll)) Count: 50 result0 {'_id': '479c7ce8-...', 'seq': 48} result1 {'_id': '93e992c4-...', 'seq': 10} result2 None result3 {'_id': '479c7ce8-...'} result4 {'_id': 'd656cd9d-...', 'seq': 49}>>> asyncio.run(my_async_coll.find_one( ... {}, ... sort={"$vector": [1, 0]}, ... projection={"*": True}, ... )) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}Note
See the
findmethod for more details on the accepted parameters (whereasskipandlimitare not valid parameters forfind_one).Expand source code
async def find_one( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Run a search, returning the first document in the collection that matches provided filters, if any is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary expressing the required document, otherwise None. Example: >>> async def demo_find_one(acol: AsyncCollection) -> None: .... print("Count:", await acol.count_documents({}, upper_bound=100)) ... result0 = await acol.find_one({}) ... print("result0", result0) ... result1 = await acol.find_one({"seq": 10}) ... print("result1", result1) ... result2 = await acol.find_one({"seq": 1011}) ... print("result2", result2) ... result3 = await acol.find_one({}, projection={"seq": False}) ... print("result3", result3) ... result4 = await acol.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) ... print("result4", result4) ... >>> >>> asyncio.run(demo_find_one(my_async_coll)) Count: 50 result0 {'_id': '479c7ce8-...', 'seq': 48} result1 {'_id': '93e992c4-...', 'seq': 10} result2 None result3 {'_id': '479c7ce8-...'} result4 {'_id': 'd656cd9d-...', 'seq': 49} >>> asyncio.run(my_async_coll.find_one( ... {}, ... sort={"$vector": [1, 0]}, ... projection={"*": True}, ... )) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]} Note: See the `find` method for more details on the accepted parameters (whereas `skip` and `limit` are not valid parameters for `find_one`). """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_cursor = self.find( filter=filter, projection=projection, skip=None, limit=1, vector=vector, vectorize=vectorize, include_similarity=include_similarity, sort=sort, max_time_ms=_max_time_ms, ) try: document = await fo_cursor.__anext__() return document # type: ignore[no-any-return] except StopAsyncIteration: return None async def find_one_and_delete(self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document in the collection and delete it. The deleted document, however, is the return value of the method.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
Either the document (or a projection thereof, as requested), or None if no matches were found in the first place.
Example
>>> async def do_find_one_and_delete(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) ... delete_result0 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) ... print("delete_result0", delete_result0) ... delete_result1 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... ) ... print("delete_result1", delete_result1) ... >>> asyncio.run(do_find_one_and_delete(my_async_coll)) delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'} delete_result1 NoneExpand source code
@recast_method_async async def find_one_and_delete( self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document in the collection and delete it. The deleted document, however, is the return value of the method. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: Either the document (or a projection thereof, as requested), or None if no matches were found in the first place. Example: >>> async def do_find_one_and_delete(acol: AsyncCollection) -> None: ... await acol.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) ... delete_result0 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) ... print("delete_result0", delete_result0) ... delete_result1 = await acol.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... ) ... print("delete_result1", delete_result1) ... >>> asyncio.run(do_find_one_and_delete(my_async_coll)) delete_result0 {'_id': 'f335cd0f-...', 'species': 'swan'} delete_result1 None """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _projection = normalize_optional_projection(projection) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_delete on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_delete( sort=_sort, filter=filter, projection=_projection, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_delete on '{self.name}'") if "document" in fo_response.get("data", {}): document = fo_response["data"]["document"] return document # type: ignore[no-any-return] else: deleted_count = fo_response.get("status", {}).get("deletedCount") if deleted_count == 0: return None else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_delete API command.", raw_response=fo_response, ) async def find_one_and_replace(self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
replacement- the new document to write into the collection.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True,
replacementis inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document- a flag controlling what document is returned:
if set to
ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set toReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before". max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
A document, either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the
return_documentparameter).Example
>>> async def do_find_one_and_replace(acol: AsyncCollection) -> None: ... await acol.insert_one({"_id": "rule1", "text": "all animals are equal"}) ... result0 = await acol.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_replace(my_async_coll)) result0 {'_id': 'rule1', 'text': 'all animals are equal'} result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'} result2 None result3 {'text': 'F=ma'}Expand source code
@recast_method_async async def find_one_and_replace( self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document, either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the `return_document` parameter). Example: >>> async def do_find_one_and_replace(acol: AsyncCollection) -> None: ... await acol.insert_one({"_id": "rule1", "text": "all animals are equal"}) ... result0 = await acol.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_replace(my_async_coll)) result0 {'_id': 'rule1', 'text': 'all animals are equal'} result1 {'_id': 'rule1', 'text': 'and the pigs are the rulers'} result2 None result3 {'text': 'F=ma'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) async def find_one_and_update(self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document on the collection and update it as requested, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document- a flag controlling what document is returned:
if set to
ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set toReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before". max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the
return_documentparameter).Example
>>> async def do_find_one_and_update(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_update(my_async_coll)) result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'} result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3} result2 None result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0}Expand source code
@recast_method_async async def find_one_and_update( self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and update it as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the `return_document` parameter). Example: >>> async def do_find_one_and_update(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) ... print("result0", result0) ... result1 = await acol.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result1", result1) ... result2 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result2", result2) ... result3 = await acol.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) ... print("result3", result3) ... >>> asyncio.run(do_find_one_and_update(my_async_coll)) result0 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'Marco': 'Polo'} result1 {'_id': 'f7c936d3-b0a0-45eb-a676-e2829662a57c', 'title': 'Mr.', 'rank': 3} result2 None result3 {'_id': 'db3d678d-14d4-4caa-82d2-d5fb77dab7ec', 'name': 'Johnny', 'rank': 0} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_update( update=update, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) def info(self) ‑> CollectionInfo-
Information on the collection (name, location, database), in the form of a CollectionInfo object.
Not to be confused with the collection
optionsmethod (related to the collection internal configuration).Example
>>> my_async_coll.info().database_info.region 'us-east1' >>> my_async_coll.info().full_name 'default_keyspace.my_v_collection'Note
the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for
Database.info()for more details.Expand source code
def info(self) -> CollectionInfo: """ Information on the collection (name, location, database), in the form of a CollectionInfo object. Not to be confused with the collection `options` method (related to the collection internal configuration). Example: >>> my_async_coll.info().database_info.region 'us-east1' >>> my_async_coll.info().full_name 'default_keyspace.my_v_collection' Note: the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for `Database.info()` for more details. """ return CollectionInfo( database_info=self.database.info(), namespace=self.namespace, name=self.name, full_name=self.full_name, ) async def insert_many(self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> InsertManyResult-
Insert a list of documents into the collection. This is not an atomic operation.
Args
documents- an iterable of dictionaries, each a document to insert.
Documents may specify their
_idfield or leave it out, in which case it will be added automatically. vectors- an optional list of vectors (as many vectors as the provided
documents) to associate to the documents when inserting.
Passing vectors this way is indeed equivalent to the "$vector" field
of the documents, however the two are mutually exclusive.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the documents instead. vectorize- an optional list of strings to be made into as many vectors
(one per document), if such a service is configured for the collection.
Passing this parameter is equivalent to providing a
$vectorizefield in the documents themselves, however the two are mutually exclusive. DEPRECATED (removal in 2.0). Use a$vectorizekey in the documents instead. ordered- if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster.
chunk_size- how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default.
concurrency- maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions.
max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here.
Returns
an InsertManyResult object.
Examples
>>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... im_result1 = await acol.insert_many( ... [ ... {"a": 10}, ... {"a": 5}, ... {"b": [True, False, False]}, ... ], ... ordered=True, ... ) ... print("inserted1", im_result1.inserted_ids) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... await acol.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) ... count2 = await acol.count_documents({}, upper_bound=100) ... print("count2", count2) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...'] count1 3 count2 53 >>> asyncio.run(my_async_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... )) InsertManyResult(...)Note
Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important.
Note
A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an
_idalready present on the collection, or its vector dimension may not match the collection setting.For an ordered insertion, the method will raise an exception at the first such faulty document – nevertheless, all documents processed until then will end up being written to the database.
For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised – and all insertable documents will have been written to the database, including those "after" the troublesome ones.
If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole
insert_manyoperation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database.Expand source code
@recast_method_async async def insert_many( self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> InsertManyResult: """ Insert a list of documents into the collection. This is not an atomic operation. Args: documents: an iterable of dictionaries, each a document to insert. Documents may specify their `_id` field or leave it out, in which case it will be added automatically. vectors: an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead. vectorize: an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the documents themselves, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead. ordered: if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster. chunk_size: how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default. concurrency: maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here. Returns: an InsertManyResult object. Examples: >>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... im_result1 = await acol.insert_many( ... [ ... {"a": 10}, ... {"a": 5}, ... {"b": [True, False, False]}, ... ], ... ordered=True, ... ) ... print("inserted1", im_result1.inserted_ids) ... count1 = await acol.count_documents({}, upper_bound=100) ... print("count1", count1) ... await acol.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) ... count2 = await acol.count_documents({}, upper_bound=100) ... print("count2", count2) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 inserted1 ['e3c2a684-...', '1de4949f-...', '167dacc3-...'] count1 3 count2 53 >>> asyncio.run(my_async_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... )) InsertManyResult(...) Note: Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important. Note: A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an `_id` already present on the collection, or its vector dimension may not match the collection setting. For an ordered insertion, the method will raise an exception at the first such faulty document -- nevertheless, all documents processed until then will end up being written to the database. For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised -- and all insertable documents will have been written to the database, including those "after" the troublesome ones. If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole `insert_many` operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database. """ check_deprecated_vector_ize( vector=None, vectors=vectors, vectorize=vectorize, kind="insert", from_async_method=True, ) if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered insert_many concurrently.") if chunk_size is None: _chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS else: _chunk_size = chunk_size _documents = _collate_vectors_to_documents(documents, vectors, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting {len(_documents)} documents in '{self.name}'") raw_results: List[Dict[str, Any]] = [] timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: options = {"ordered": True} inserted_ids: List[Any] = [] for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") chunk_response = await self._astra_db_collection.insert_many( documents=_documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished inserting a chunk of documents in '{self.name}'") # accumulate the results in this call chunk_inserted_ids = (chunk_response.get("status") or {}).get( "insertedIds", [] ) inserted_ids += chunk_inserted_ids raw_results += [chunk_response] # if errors, quit early if chunk_response.get("errors", []): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_response( command=None, raw_response=chunk_response, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result else: # unordered: concurrent or not, do all of them and parse the results options = {"ordered": False} sem = asyncio.Semaphore(_concurrency) async def concurrent_insert_chunk( document_chunk: List[DocumentType], ) -> Dict[str, Any]: async with sem: logger.info(f"inserting a chunk of documents in '{self.name}'") im_response = await self._astra_db_collection.insert_many( document_chunk, options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) return im_response if _concurrency > 1: tasks = [ asyncio.create_task( concurrent_insert_chunk(_documents[i : i + _chunk_size]) ) for i in range(0, len(_documents), _chunk_size) ] raw_results = await asyncio.gather(*tasks) else: raw_results = [ await concurrent_insert_chunk(_documents[i : i + _chunk_size]) for i in range(0, len(_documents), _chunk_size) ] # recast raw_results inserted_ids = [ inserted_id for chunk_response in raw_results for inserted_id in (chunk_response.get("status") or {}).get( "insertedIds", [] ) ] # check-raise if any( [chunk_response.get("errors", []) for chunk_response in raw_results] ): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_responses( commands=[None for _ in raw_results], raw_responses=raw_results, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result async def insert_one(self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> InsertOneResult-
Insert a single document in the collection in an atomic operation.
Args
document- the dictionary expressing the document to insert.
The
_idfield of the document can be left out, in which case it will be created automatically. vector- a vector (a list of numbers appropriate for the collection)
for the document. Passing this parameter is equivalent to
providing a
$vectorfield within the document itself, however the two are mutually exclusive. DEPRECATED (removal in 2.0). Use a$vectorkey in the document instead. vectorize- a string to be made into a vector, if such a service
is configured for the collection. Passing this parameter is
equivalent to providing a
$vectorizefield in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist withvector. DEPRECATED (removal in 2.0). Use a$vectorizekey in the document instead. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an InsertOneResult object.
Example
>>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... await acol.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) ... await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) ... count1 = await acol.count_documents({}, upper_bound=10) ... print("count1", count1) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 count1 2>>> asyncio.run(my_async_coll.insert_one({"tag": v", "$vector": [10, 11]})) InsertOneResult(...)Note
If an
_idis explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails.Expand source code
@recast_method_async async def insert_one( self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None, ) -> InsertOneResult: """ Insert a single document in the collection in an atomic operation. Args: document: the dictionary expressing the document to insert. The `_id` field of the document can be left out, in which case it will be created automatically. vector: a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing a `$vector` field within the document itself, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead. vectorize: a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with `vector`. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an InsertOneResult object. Example: >>> async def write_and_count(acol: AsyncCollection) -> None: ... count0 = await acol.count_documents({}, upper_bound=10) ... print("count0", count0) ... await acol.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) ... await acol.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) ... count1 = await acol.count_documents({}, upper_bound=10) ... print("count1", count1) ... >>> asyncio.run(write_and_count(my_async_coll)) count0 0 count1 2 >>> asyncio.run(my_async_coll.insert_one({"tag": v", "$vector": [10, 11]})) InsertOneResult(...) Note: If an `_id` is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="insert", from_async_method=True, ) _document = _collate_vector_to_document(document, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting one document in '{self.name}'") io_response = await self._astra_db_collection.insert_one( _document, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished inserting one document in '{self.name}'") if "insertedIds" in io_response.get("status", {}): if io_response["status"]["insertedIds"]: inserted_id = io_response["status"]["insertedIds"][0] return InsertOneResult( raw_results=[io_response], inserted_id=inserted_id, ) else: raise ValueError( "Could not complete a insert_one operation. " f"(gotten '${json.dumps(io_response)}')" ) else: raise ValueError( "Could not complete a insert_one operation. " f"(gotten '${json.dumps(io_response)}')" ) async def options(self, *, max_time_ms: Optional[int] = None) ‑> CollectionOptions-
Get the collection options, i.e. its configuration as read from the database.
The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a CollectionOptions instance describing the collection. (See also the database
list_collectionsmethod.)Example
>>> asyncio.run(my_async_coll.options()) CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))Expand source code
async def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions: """ Get the collection options, i.e. its configuration as read from the database. The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a CollectionOptions instance describing the collection. (See also the database `list_collections` method.) Example: >>> asyncio.run(my_async_coll.options()) CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine')) """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"getting collections in search of '{self.name}'") self_descriptors = [ coll_desc async for coll_desc in self.database.list_collections( max_time_ms=_max_time_ms ) if coll_desc.name == self.name ] logger.info(f"finished getting collections in search of '{self.name}'") if self_descriptors: return self_descriptors[0].options # type: ignore[no-any-return] else: raise CollectionNotFoundException( text=f"Collection {self.namespace}.{self.name} not found.", namespace=self.namespace, collection_name=self.name, ) async def replace_one(self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Replace a single document on the collection with a new one, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
replacement- the new document to write into the collection.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True,
replacementis inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an UpdateResult object summarizing the outcome of the replace operation.
Example
>>> async def do_replace_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.replace_one( ... {"Marco": {"$exists": True}}, ... {"Buda": "Pest"}, ... ) ... print("result0.update_info", result0.update_info) ... doc1 = await acol.find_one({"Buda": "Pest"}) ... print("doc1", doc1) ... result1 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_replace_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1} doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'}Expand source code
@recast_method_async async def replace_one( self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Replace a single document on the collection with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the replace operation. Example: >>> async def do_replace_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.replace_one( ... {"Marco": {"$exists": True}}, ... {"Buda": "Pest"}, ... ) ... print("result0.update_info", result0.update_info) ... doc1 = await acol.find_one({"Buda": "Pest"}) ... print("doc1", doc1) ... result1 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.replace_one( ... {"Mirco": {"$exists": True}}, ... {"Oh": "yeah?"}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_replace_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1} doc1 {'_id': '6e669a5a-...', 'Buda': 'Pest'} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '30e34e00-...'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None-
Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").
Args
caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Example
>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")Expand source code
def set_caller( self, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: """ Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller"). Args: caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Example: >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0") """ logger.info(f"setting caller to {caller_name}/{caller_version}") self._astra_db_collection.set_caller( caller_name=caller_name, caller_version=caller_version, ) def to_sync(self, *, database: Optional[Database] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> Collection-
Create a Collection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into a sync object).
Args
database- a Database object, instantiated earlier. This represents the database the new collection belongs to.
name- the collection name. This parameter should match an existing collection on the database.
namespace- this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.
embedding_api_key- optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e.
EmbeddingAPIKeyHeaderProvider). For some vectorize providers/models, if using header-based authentication, specialized subclasses ofEmbeddingHeadersProvidershould be supplied. collection_max_time_ms- a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
find,delete_many,insert_manyand so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Returns
the new copy, a Collection instance.
Example
>>> my_async_coll.to_sync().count_documents({}, upper_bound=100) 77Expand source code
def to_sync( self, *, database: Optional[Database] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> Collection: """ Create a Collection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into a sync object). Args: database: a Database object, instantiated earlier. This represents the database the new collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: the new copy, a Collection instance. Example: >>> my_async_coll.to_sync().count_documents({}, upper_bound=100) 77 """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return Collection( database=database or self.database.to_sync(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(_api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) async def update_many(self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
upsert- this parameter controls the behavior in absence of matches.
If True, a single new document (resulting from applying
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence.
Returns
an UpdateResult object summarizing the outcome of the update operation.
Example
>>> async def do_update_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) ... result0 = await acol.update_many( ... {"c": {"$ne": "green"}}, ... {"$set": {"nongreen": True}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_many(my_async_coll)) result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'}Note
Similarly to the case of
find(see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of theupdate_manycan result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not.Expand source code
@recast_method_async async def update_many( self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. upsert: this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> async def do_update_many(acol: AsyncCollection) -> None: ... await acol.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) ... result0 = await acol.update_many( ... {"c": {"$ne": "green"}}, ... {"$set": {"nongreen": True}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_many(my_async_coll)) result0.update_info {'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2} result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0} result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '79ffd5a3-ab99-4dff-a2a5-4aaa0e59e854'} Note: Similarly to the case of `find` (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the `update_many` can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not. """ api_options = { "upsert": upsert, } page_state_options: Dict[str, str] = {} um_responses: List[Dict[str, Any]] = [] um_statuses: List[Dict[str, Any]] = [] must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"starting update_many on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) while must_proceed: options = {**api_options, **page_state_options} logger.info(f"calling update_many on '{self.name}'") this_um_response = await self._astra_db_collection.update_many( update=update, filter=filter, options=options, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling update_many on '{self.name}'") this_um_status = this_um_response.get("status") or {} # # if errors, quit early if this_um_response.get("errors", []): partial_update_info = _prepare_update_info(um_statuses) partial_result = UpdateResult( raw_results=um_responses, update_info=partial_update_info, ) all_um_responses = um_responses + [this_um_response] raise UpdateManyException.from_responses( commands=[None for _ in all_um_responses], raw_responses=all_um_responses, partial_result=partial_result, ) else: if "status" not in this_um_response: raise DataAPIFaultyResponseException( text="Faulty response from update_many API command.", raw_response=this_um_response, ) um_responses.append(this_um_response) um_statuses.append(this_um_status) next_page_state = this_um_status.get("nextPageState") if next_page_state is not None: must_proceed = True page_state_options = {"pageState": next_page_state} else: must_proceed = False page_state_options = {} update_info = _prepare_update_info(um_statuses) logger.info(f"finished update_many on '{self.name}'") return UpdateResult( raw_results=um_responses, update_info=update_info, ) async def update_one(self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Update a single document on the collection as requested, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an UpdateResult object summarizing the outcome of the update operation.
Example
>>> async def do_update_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.update_one( ... {"Marco": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'}Expand source code
@recast_method_async async def update_one( self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Update a single document on the collection as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> async def do_update_one(acol: AsyncCollection) -> None: ... await acol.insert_one({"Marco": "Polo"}) ... result0 = await acol.update_one( ... {"Marco": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result0.update_info", result0.update_info) ... result1 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... ) ... print("result1.update_info", result1.update_info) ... result2 = await acol.update_one( ... {"Mirko": {"$exists": True}}, ... {"$inc": {"rank": 3}}, ... upsert=True, ... ) ... print("result2.update_info", result2.update_info) ... >>> asyncio.run(do_update_one(my_async_coll)) result0.update_info {'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) result1.update_info {'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) result2.update_info {'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '75748092-...'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find", from_async_method=True, ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = await self._astra_db_collection.find_one_and_update( update=update, sort=_sort, filter=filter, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) def with_options(self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AsyncCollection-
Create a clone of this collection with some changed attributes.
Args
name- the name of the collection. This parameter is useful to quickly spawn AsyncCollection instances each pointing to a different collection existing in the same namespace.
embedding_api_key- optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e.
EmbeddingAPIKeyHeaderProvider). For some vectorize providers/models, if using header-based authentication, specialized subclasses ofEmbeddingHeadersProvidershould be supplied. collection_max_time_ms- a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
find,delete_many,insert_manyand so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Returns
a new AsyncCollection instance.
Example
>>> my_other_async_coll = my_async_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... )Expand source code
def with_options( self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> AsyncCollection: """ Create a clone of this collection with some changed attributes. Args: name: the name of the collection. This parameter is useful to quickly spawn AsyncCollection instances each pointing to a different collection existing in the same namespace. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: a new AsyncCollection instance. Example: >>> my_other_async_coll = my_async_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... ) """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return self._copy( name=name, api_options=_api_options, caller_name=caller_name, caller_version=caller_version, )
class Collection (database: Database, name: str, *, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None)-
A Data API collection, the main object to interact with the Data API, especially for DDL operations. This class has a synchronous interface.
A Collection is spawned from a Database object, from which it inherits the details on how to reach the API server (endpoint, authentication token).
Args
database- a Database object, instantiated earlier. This represents the database the collection belongs to.
name- the collection name. This parameter should match an existing collection on the database.
namespace- this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.
api_options- An instance of
astrapy.api_options.CollectionAPIOptionsproviding the general settings for interacting with the Data API. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Examples
>>> from astrapy import DataAPIClient, Collection >>> my_client = astrapy.DataAPIClient("AstraCS:...") >>> my_db = my_client.get_database( ... "https://01234567-....apps.astra.datastax.com" ... ) >>> my_coll_1 = Collection(database=my_db, name="my_collection") >>> my_coll_2 = my_db.create_collection( ... "my_v_collection", ... dimension=3, ... metric="cosine", ... ) >>> my_coll_3a = my_db.get_collection("my_already_existing_collection") >>> my_coll_3b = my_db.my_already_existing_collection >>> my_coll_3c = my_db["my_already_existing_collection"]Note
creating an instance of Collection does not trigger actual creation of the collection on the database. The latter should have been created beforehand, e.g. through the
create_collectionmethod of a Database.Expand source code
class Collection: """ A Data API collection, the main object to interact with the Data API, especially for DDL operations. This class has a synchronous interface. A Collection is spawned from a Database object, from which it inherits the details on how to reach the API server (endpoint, authentication token). Args: database: a Database object, instantiated earlier. This represents the database the collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. api_options: An instance of `astrapy.api_options.CollectionAPIOptions` providing the general settings for interacting with the Data API. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Examples: >>> from astrapy import DataAPIClient, Collection >>> my_client = astrapy.DataAPIClient("AstraCS:...") >>> my_db = my_client.get_database( ... "https://01234567-....apps.astra.datastax.com" ... ) >>> my_coll_1 = Collection(database=my_db, name="my_collection") >>> my_coll_2 = my_db.create_collection( ... "my_v_collection", ... dimension=3, ... metric="cosine", ... ) >>> my_coll_3a = my_db.get_collection("my_already_existing_collection") >>> my_coll_3b = my_db.my_already_existing_collection >>> my_coll_3c = my_db["my_already_existing_collection"] Note: creating an instance of Collection does not trigger actual creation of the collection on the database. The latter should have been created beforehand, e.g. through the `create_collection` method of a Database. """ def __init__( self, database: Database, name: str, *, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: if api_options is None: self.api_options = CollectionAPIOptions() else: self.api_options = api_options additional_headers = self.api_options.embedding_api_key.get_headers() self._astra_db_collection: AstraDBCollection = AstraDBCollection( collection_name=name, astra_db=database._astra_db, namespace=namespace, caller_name=caller_name, caller_version=caller_version, additional_headers=additional_headers, ) # this comes after the above, lets AstraDBCollection resolve namespace self._database = database._copy( namespace=self._astra_db_collection.astra_db.namespace ) def __repr__(self) -> str: return ( f'{self.__class__.__name__}(name="{self.name}", ' f'namespace="{self.namespace}", database={self.database}, ' f"api_options={self.api_options})" ) def __eq__(self, other: Any) -> bool: if isinstance(other, Collection): return all( [ self._astra_db_collection == other._astra_db_collection, self.api_options == other.api_options, ] ) else: return False def __call__(self, *pargs: Any, **kwargs: Any) -> None: raise TypeError( f"'{self.__class__.__name__}' object is not callable. If you " f"meant to call the '{self.name}' method on a " f"'{self.database.__class__.__name__}' object " "it is failing because no such method exists." ) def _copy( self, *, database: Optional[Database] = None, name: Optional[str] = None, namespace: Optional[str] = None, api_options: Optional[CollectionAPIOptions] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> Collection: return Collection( database=database or self.database._copy(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) def with_options( self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> Collection: """ Create a clone of this collection with some changed attributes. Args: name: the name of the collection. This parameter is useful to quickly spawn Collection instances each pointing to a different collection existing in the same namespace. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: a new Collection instance. Example: >>> my_other_coll = my_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... ) """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return self._copy( name=name, api_options=_api_options, caller_name=caller_name, caller_version=caller_version, ) def to_async( self, *, database: Optional[AsyncDatabase] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> AsyncCollection: """ Create an AsyncCollection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into an async object). Args: database: an AsyncDatabase object, instantiated earlier. This represents the database the new collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: the new copy, an AsyncCollection instance. Example: >>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100)) 77 """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return AsyncCollection( database=database or self.database.to_async(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(_api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) def set_caller( self, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: """ Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller"). Args: caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Example: >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0") """ logger.info(f"setting caller to {caller_name}/{caller_version}") self._astra_db_collection.set_caller( caller_name=caller_name, caller_version=caller_version, ) def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions: """ Get the collection options, i.e. its configuration as read from the database. The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a CollectionOptions instance describing the collection. (See also the database `list_collections` method.) Example: >>> my_coll.options() CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine')) """ logger.info(f"getting collections in search of '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms self_descriptors = [ coll_desc for coll_desc in self.database.list_collections(max_time_ms=_max_time_ms) if coll_desc.name == self.name ] logger.info(f"finished getting collections in search of '{self.name}'") if self_descriptors: return self_descriptors[0].options # type: ignore[no-any-return] else: raise CollectionNotFoundException( text=f"Collection {self.namespace}.{self.name} not found.", namespace=self.namespace, collection_name=self.name, ) def info(self) -> CollectionInfo: """ Information on the collection (name, location, database), in the form of a CollectionInfo object. Not to be confused with the collection `options` method (related to the collection internal configuration). Example: >>> my_coll.info().database_info.region 'eu-west-1' >>> my_coll.info().full_name 'default_keyspace.my_v_collection' Note: the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for `Database.info()` for more details. """ return CollectionInfo( database_info=self.database.info(), namespace=self.namespace, name=self.name, full_name=self.full_name, ) @property def database(self) -> Database: """ a Database object, the database this collection belongs to. Example: >>> my_coll.database.name 'the_application_database' """ return self._database @property def namespace(self) -> str: """ The namespace this collection is in. Example: >>> my_coll.namespace 'default_keyspace' """ _namespace = self.database.namespace if _namespace is None: raise ValueError("The collection's DB is set with namespace=None") return _namespace @property def name(self) -> str: """ The name of this collection. Example: >>> my_coll.name 'my_v_collection' """ # type hint added as for some reason the typechecker gets lost return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type] @property def full_name(self) -> str: """ The fully-qualified collection name within the database, in the form "namespace.collection_name". Example: >>> my_coll.full_name 'default_keyspace.my_v_collection' """ return f"{self.namespace}.{self.name}" @recast_method_sync def insert_one( self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None, ) -> InsertOneResult: """ Insert a single document in the collection in an atomic operation. Args: document: the dictionary expressing the document to insert. The `_id` field of the document can be left out, in which case it will be created automatically. vector: a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing a `$vector` field within the document itself, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead. vectorize: a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with `vector`. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an InsertOneResult object. Examples: >>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...') >>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) InsertOneResult(raw_results=..., inserted_id='user-123') >>> my_coll.count_documents({}, upper_bound=10) 2 >>> my_coll.insert_one({"tag": "v", "$vector": [10, 11]}) InsertOneResult(...) Note: If an `_id` is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="insert" ) _document = _collate_vector_to_document(document, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting one document in '{self.name}'") io_response = self._astra_db_collection.insert_one( _document, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished inserting one document in '{self.name}'") if "insertedIds" in io_response.get("status", {}): if io_response["status"]["insertedIds"]: inserted_id = io_response["status"]["insertedIds"][0] return InsertOneResult( raw_results=[io_response], inserted_id=inserted_id, ) else: raise DataAPIFaultyResponseException( text="Faulty response from insert_one API command.", raw_response=io_response, ) else: raise DataAPIFaultyResponseException( text="Faulty response from insert_one API command.", raw_response=io_response, ) @recast_method_sync def insert_many( self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> InsertManyResult: """ Insert a list of documents into the collection. This is not an atomic operation. Args: documents: an iterable of dictionaries, each a document to insert. Documents may specify their `_id` field or leave it out, in which case it will be added automatically. vectors: an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead. vectorize: an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the documents themselves, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead. ordered: if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster. chunk_size: how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default. concurrency: maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here. Returns: an InsertManyResult object. Examples: >>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_many( ... [{"a": 10}, {"a": 5}, {"b": [True, False, False]}], ... ordered=True, ... ) InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...']) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) InsertManyResult(raw_results=..., inserted_ids=[... ...]) >>> my_coll.count_documents({}, upper_bound=100) 53 >>> my_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... ) InsertManyResult(...) Note: Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important. Note: A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an `_id` already present on the collection, or its vector dimension may not match the collection setting. For an ordered insertion, the method will raise an exception at the first such faulty document -- nevertheless, all documents processed until then will end up being written to the database. For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised -- and all insertable documents will have been written to the database, including those "after" the troublesome ones. If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole `insert_many` operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database. """ check_deprecated_vector_ize( vector=None, vectors=vectors, vectorize=vectorize, kind="insert" ) if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered insert_many concurrently.") if chunk_size is None: _chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS else: _chunk_size = chunk_size _documents = _collate_vectors_to_documents(documents, vectors, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting {len(_documents)} documents in '{self.name}'") raw_results: List[Dict[str, Any]] = [] timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: options = {"ordered": True} inserted_ids: List[Any] = [] for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") chunk_response = self._astra_db_collection.insert_many( documents=_documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished inserting a chunk of documents in '{self.name}'") # accumulate the results in this call chunk_inserted_ids = (chunk_response.get("status") or {}).get( "insertedIds", [] ) inserted_ids += chunk_inserted_ids raw_results += [chunk_response] # if errors, quit early if chunk_response.get("errors", []): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_response( command=None, raw_response=chunk_response, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result else: # unordered: concurrent or not, do all of them and parse the results options = {"ordered": False} if _concurrency > 1: with ThreadPoolExecutor(max_workers=_concurrency) as executor: def _chunk_insertor( document_chunk: List[Dict[str, Any]] ) -> Dict[str, Any]: logger.info(f"inserting a chunk of documents in '{self.name}'") im_response = self._astra_db_collection.insert_many( documents=document_chunk, options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) return im_response raw_results = list( executor.map( _chunk_insertor, ( _documents[i : i + _chunk_size] for i in range(0, len(_documents), _chunk_size) ), ) ) else: for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") raw_results.append( self._astra_db_collection.insert_many( _documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) # recast raw_results inserted_ids = [ inserted_id for chunk_response in raw_results for inserted_id in (chunk_response.get("status") or {}).get( "insertedIds", [] ) ] # check-raise if any( [chunk_response.get("errors", []) for chunk_response in raw_results] ): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_responses( commands=[None for _ in raw_results], raw_responses=raw_results, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result def find( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Cursor: """ Find documents on the collection, matching a certain provided filter. The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the `find` method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the `sort` option in a non-vector-search manner. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. skip: with this integer parameter, what would be the first `skip` documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit `sort` criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit: this (integer) parameter sets a limit over how many documents are returned. Once `limit` is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. include_sort_vector: a boolean to request query vector used in this search. If set to True (and if the invocation is a vector search), calling the `get_sort_vector` method on the returned cursor will yield the vector used for the ANN search. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead. Returns: a Cursor object representing iterations over the matching documents (see the Cursor object for how to use it. The simplest thing is to run a for loop: `for document in collection.sort(...):`). Examples: >>> filter = {"seq": {"$exists": True}} >>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... 37 35 10 36 27 >>> cursor1 = my_coll.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) >>> [doc["_id"] for doc in cursor1] ['97e85f81-...', '1581efe4-...', '...', '...'] >>> cursor2 = my_coll.find({}, limit=3) >>> cursor2.distinct("seq") [37, 35, 10] >>> my_coll.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) >>> ann_tags = [ ... document["tag"] ... for document in my_coll.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] >>> ann_tags ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE) >>> cursor = my_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> cursor.get_sort_vector() [3.0, 3.0] >>> matches = list(cursor) >>> cursor.get_sort_vector() [3.0, 3.0] Note: The following are example values for the `sort` parameter. When no particular order is required: sort={} # (default when parameter not provided) When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a `collections.OrderedDict` in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]} Note: Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as `.distinct()` on a cursor. Note: When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the `find` was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms if include_similarity is not None and not _is_vector_sort(_sort): raise ValueError( "Cannot use `include_similarity` when not searching through `vector`." ) return ( Cursor( collection=self, filter=filter, projection=projection, max_time_ms=_max_time_ms, overall_max_time_ms=None, ) .skip(skip) .limit(limit) .sort(_sort) .include_similarity(include_similarity) .include_sort_vector(include_sort_vector) ) def find_one( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Run a search, returning the first document in the collection that matches provided filters, if any is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary expressing the required document, otherwise None. Examples: >>> my_coll.find_one({}) {'_id': '68d1e515-...', 'seq': 37} >>> my_coll.find_one({"seq": 10}) {'_id': 'd560e217-...', 'seq': 10} >>> my_coll.find_one({"seq": 1011}) >>> # (returns None for no matches) >>> my_coll.find_one({}, projection={"seq": False}) {'_id': '68d1e515-...'} >>> my_coll.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) {'_id': '97e85f81-...', 'seq': 69} >>> my_coll.find_one({}, sort={"$vector": [1, 0]}, projection={"*": True}) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]} Note: See the `find` method for more details on the accepted parameters (whereas `skip` and `limit` are not valid parameters for `find_one`). """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_cursor = self.find( filter=filter, projection=projection, skip=None, limit=1, vector=vector, vectorize=vectorize, include_similarity=include_similarity, sort=sort, max_time_ms=_max_time_ms, ) try: document = fo_cursor.__next__() return document # type: ignore[no-any-return] except StopIteration: return None def distinct( self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None, ) -> List[Any]: """ Return a list of the unique values of `key` across the documents in the collection that match the provided filter. Args: key: the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable `key` values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`. If not passed, the collection-level setting is used instead. Returns: a list of all different values for `key` found across the documents that match the filter. The result list has no repeated items. Example: >>> my_coll.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...']) >>> my_coll.distinct("name") ['Marco', 'Emma'] >>> my_coll.distinct("city") ['Helsinki'] >>> my_coll.distinct("food") ['apple', 'orange', {'likes_fruit': True, 'allergies': []}] >>> my_coll.distinct("food.1") ['orange'] >>> my_coll.distinct("food.allergies") [] >>> my_coll.distinct("food.likes_fruit") [True] Note: It must be kept in mind that `distinct` is a client-side operation, which effectively browses all required documents using the logic of the `find` method and collects the unique values found for `key`. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large. Note: For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the `find` command. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms f_cursor = Cursor( collection=self, filter=filter, projection={key: True}, max_time_ms=None, overall_max_time_ms=_max_time_ms, ) return f_cursor.distinct(key) # type: ignore[no-any-return] @recast_method_sync def count_documents( self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None, ) -> int: """ Count the documents in the collection matching the specified filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. upper_bound: a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: the exact count of matching documents. Example: >>> my_coll.insert_many([{"seq": i} for i in range(20)]) InsertManyResult(...) >>> my_coll.count_documents({}, upper_bound=100) 20 >>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100) 4 >>> my_coll.count_documents({}, upper_bound=10) Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountException Note: Count operations are expensive: for this reason, the best practice is to provide a reasonable `upper_bound` according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info("calling count_documents") cd_response = self._astra_db_collection.count_documents( filter=filter, timeout_info=base_timeout_info(_max_time_ms), ) logger.info("finished calling count_documents") if "count" in cd_response.get("status", {}): count: int = cd_response["status"]["count"] if cd_response["status"].get("moreData", False): raise TooManyDocumentsToCountException( text=f"Document count exceeds {count}, the maximum allowed by the server", server_max_count_exceeded=True, ) else: if count > upper_bound: raise TooManyDocumentsToCountException( text="Document count exceeds required upper bound", server_max_count_exceeded=False, ) else: return count else: raise DataAPIFaultyResponseException( text="Faulty response from count_documents API command.", raw_response=cd_response, ) def estimated_document_count( self, *, max_time_ms: Optional[int] = None, ) -> int: """ Query the API server for an estimate of the document count in the collection. Contrary to `count_documents`, this method has no filtering parameters. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a server-provided estimate count of the documents in the collection. Example: >>> my_coll.estimated_document_count() 35700 """ _max_time_ms = max_time_ms or self.api_options.max_time_ms ed_response = self.command( {"estimatedDocumentCount": {}}, max_time_ms=_max_time_ms, ) if "count" in ed_response.get("status", {}): count: int = ed_response["status"]["count"] return count else: raise DataAPIFaultyResponseException( text="Faulty response from estimated_document_count API command.", raw_response=ed_response, ) @recast_method_sync def find_one_and_replace( self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the `return_document` parameter). Example: >>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"}) InsertOneResult(...) >>> my_coll.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) {'_id': 'rule1', 'text': 'all animals are equal'} >>> my_coll.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'rule1', 'text': 'and the pigs are the rulers'} >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) {'text': 'F=ma'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) @recast_method_sync def replace_one( self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Replace a single document on the collection with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the replace operation. Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.find_one({"Buda": "Pest"}) {'_id': '8424905a-...', 'Buda': 'Pest'} >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'}) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } logger.info(f"calling find_one_and_replace on '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_response = self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) @recast_method_sync def find_one_and_update( self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and update it as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the `return_document` parameter). Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) {'_id': 'a80106f2-...', 'Marco': 'Polo'} >>> my_coll.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3} >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_update( update=update, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) @recast_method_sync def update_one( self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Update a single document on the collection as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'}) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_update( update=update, sort=_sort, filter=filter, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) @recast_method_sync def update_many( self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. upsert: this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) InsertManyResult(...) >>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}}) UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}) >>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'}) Note: Similarly to the case of `find` (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the `update_many` can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not. """ api_options = { "upsert": upsert, } page_state_options: Dict[str, str] = {} um_responses: List[Dict[str, Any]] = [] um_statuses: List[Dict[str, Any]] = [] must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"starting update_many on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) while must_proceed: options = {**api_options, **page_state_options} logger.info(f"calling update_many on '{self.name}'") this_um_response = self._astra_db_collection.update_many( update=update, filter=filter, options=options, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling update_many on '{self.name}'") this_um_status = this_um_response.get("status") or {} # # if errors, quit early if this_um_response.get("errors", []): partial_update_info = _prepare_update_info(um_statuses) partial_result = UpdateResult( raw_results=um_responses, update_info=partial_update_info, ) all_um_responses = um_responses + [this_um_response] raise UpdateManyException.from_responses( commands=[None for _ in all_um_responses], raw_responses=all_um_responses, partial_result=partial_result, ) else: if "status" not in this_um_response: raise DataAPIFaultyResponseException( text="Faulty response from update_many API command.", raw_response=this_um_response, ) um_responses.append(this_um_response) um_statuses.append(this_um_status) next_page_state = this_um_status.get("nextPageState") if next_page_state is not None: must_proceed = True page_state_options = {"pageState": next_page_state} else: must_proceed = False page_state_options = {} update_info = _prepare_update_info(um_statuses) logger.info(f"finished update_many on '{self.name}'") return UpdateResult( raw_results=um_responses, update_info=update_info, ) @recast_method_sync def find_one_and_delete( self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document in the collection and delete it. The deleted document, however, is the return value of the method. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with `sort`. See the `find` method for more details on this parameter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the `find` method for more on sorting. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: Either the document (or a projection thereof, as requested), or None if no matches were found in the first place. Example: >>> my_coll.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) InsertManyResult(...) >>> my_coll.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) {'_id': '5997fb48-...', 'species': 'swan'} >>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}}) >>> # (returns None for no matches) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _projection = normalize_optional_projection(projection) logger.info(f"calling find_one_and_delete on '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_response = self._astra_db_collection.find_one_and_delete( sort=_sort, filter=filter, projection=_projection, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_delete on '{self.name}'") if "document" in fo_response.get("data", {}): document = fo_response["data"]["document"] return document # type: ignore[no-any-return] else: deleted_count = fo_response.get("status", {}).get("deletedCount") if deleted_count == 0: return None else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_delete API command.", raw_response=fo_response, ) @recast_method_sync def delete_one( self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with `sort`. See the `find` method for more details on this parameter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the `find` method for more on sorting. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling delete_one_by_predicate on '{self.name}'") do_response = self._astra_db_collection.delete_one_by_predicate( filter=filter, timeout_info=base_timeout_info(_max_time_ms), sort=_sort ) logger.info(f"finished calling delete_one_by_predicate on '{self.name}'") if "deletedCount" in do_response.get("status", {}): deleted_count = do_response["status"]["deletedCount"] if deleted_count == -1: return DeleteResult( deleted_count=None, raw_results=[do_response], ) else: # expected a non-negative integer: return DeleteResult( deleted_count=deleted_count, raw_results=[do_response], ) else: raise DataAPIFaultyResponseException( text="Faulty response from delete_one API command.", raw_response=do_response, ) @recast_method_sync def delete_many( self, filter: FilterType, *, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete all documents matching a provided filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. Passing an empty filter, `{}`, completely erases all contents of the collection. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=2) >>> my_coll.distinct("seq") [2] >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=0) Note: This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the `filter={}` case, whereby the operation is atomic. """ dm_responses: List[Dict[str, Any]] = [] deleted_count = 0 must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) logger.info(f"starting delete_many on '{self.name}'") while must_proceed: logger.info(f"calling delete_many on '{self.name}'") this_dm_response = self._astra_db_collection.delete_many( filter=filter, skip_error_check=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling delete_many on '{self.name}'") # if errors, quit early if this_dm_response.get("errors", []): partial_result = DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) all_dm_responses = dm_responses + [this_dm_response] raise DeleteManyException.from_responses( commands=[None for _ in all_dm_responses], raw_responses=all_dm_responses, partial_result=partial_result, ) else: this_dc = this_dm_response.get("status", {}).get("deletedCount") if this_dc is None: raise DataAPIFaultyResponseException( text="Faulty response from delete_many API command.", raw_response=this_dm_response, ) dm_responses.append(this_dm_response) deleted_count += this_dc must_proceed = this_dm_response.get("status", {}).get("moreData", False) logger.info(f"finished delete_many on '{self.name}'") return DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) @deprecation.deprecated( # type: ignore[misc] deprecated_in="1.3.0", removed_in="2.0.0", current_version=__version__, details="Use delete_many with filter={} instead.", ) def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Delete all documents in a collection. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> my_coll.distinct("seq") [2, 1, 0] >>> my_coll.count_documents({}, upper_bound=100) 4 >>> my_coll.delete_all() {'ok': 1} >>> my_coll.count_documents({}, upper_bound=100) 0 Note: Use with caution. """ dm_result = self.delete_many(filter={}, max_time_ms=max_time_ms) if dm_result.deleted_count == -1: return {"ok": 1} else: raise DataAPIFaultyResponseException( text="Unexpected response from collection.delete_many({}).", raw_response=None, ) def bulk_write( self, requests: Iterable[BaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> BulkWriteResult: """ Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently. This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation. Args: requests: an iterable over concrete subclasses of `BaseOperation`, such as `InsertMany` or `ReplaceOne`. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered: whether to launch the `requests` one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency: maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes. max_time_ms: a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk. Returns: A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the `requests` iterable. Example: >>> from astrapy.operations import InsertMany, ReplaceOne >>> op1 = InsertMany([{"a": 1}, {"a": 2}]) >>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True) >>> my_coll.bulk_write([op1, op2]) BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'}) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.distinct("replaced") [True] """ # lazy importing here against circular-import error from astrapy.operations import reduce_bulk_write_results if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered bulk_write concurrently.") _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"startng a bulk write on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: bulk_write_results: List[BulkWriteResult] = [] for operation_i, operation in enumerate(requests): try: this_bw_result = operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) bulk_write_results.append(this_bw_result) except CumulativeOperationException as exc: partial_result = exc.partial_result partial_bw_result = reduce_bulk_write_results( bulk_write_results + [ partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) ] ) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) except DataAPIResponseException as exc: # the cumulative exceptions, with their # partially-done-info, are handled above: # here it's just one-shot d.a.r. exceptions partial_bw_result = reduce_bulk_write_results(bulk_write_results) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) full_bw_result = reduce_bulk_write_results(bulk_write_results) logger.info(f"finished a bulk write on '{self.name}'") return full_bw_result else: def _execute_as_either( operation: BaseOperation, operation_i: int ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]: try: ex_result = operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) return (ex_result, None) except DataAPIResponseException as exc: return (None, exc) with ThreadPoolExecutor(max_workers=_concurrency) as executor: bulk_write_either_futures = [ executor.submit( _execute_as_either, operation, operation_i, ) for operation_i, operation in enumerate(requests) ] bulk_write_either_results = [ bulk_write_either_future.result() for bulk_write_either_future in bulk_write_either_futures ] # regroup bulk_write_successes = [ bwr for bwr, _ in bulk_write_either_results if bwr ] bulk_write_failures = [ bwf for _, bwf in bulk_write_either_results if bwf ] if bulk_write_failures: # extract and cumulate partial_results_from_failures = [ failure.partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) for failure in bulk_write_failures if isinstance(failure, CumulativeOperationException) ] partial_bw_result = reduce_bulk_write_results( bulk_write_successes + partial_results_from_failures ) # raise and recast the first exception all_dar_exceptions = [ bw_failure.data_api_response_exception() for bw_failure in bulk_write_failures ] dar_exception = all_dar_exceptions[0] raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=all_dar_exceptions, ) else: logger.info(f"finished a bulk write on '{self.name}'") return reduce_bulk_write_results(bulk_write_successes) def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Drop the collection, i.e. delete it from the database along with all the documents it contains. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> my_coll.find_one({}) {'_id': '...', 'a': 100} >>> my_coll.drop() {'ok': 1} >>> my_coll.find_one({}) Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection Note: Use with caution. Note: Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"dropping collection '{self.name}' (self)") drop_result = self.database.drop_collection(self, max_time_ms=_max_time_ms) logger.info(f"finished dropping collection '{self.name}' (self)") return drop_result # type: ignore[no-any-return] def command( self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None, ) -> Dict[str, Any]: """ Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload. Args: body: a JSON-serializable dictionary, the payload of the request. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary with the response of the HTTP request. Example: >>> my_coll.command({"countDocuments": {}}) {'status': {'count': 123}} """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling command on '{self.name}'") command_result = self.database.command( body=body, namespace=self.namespace, collection_name=self.name, max_time_ms=_max_time_ms, ) logger.info(f"finished calling command on '{self.name}'") return command_result # type: ignore[no-any-return]Instance variables
var database : Database-
a Database object, the database this collection belongs to.
Example
>>> my_coll.database.name 'the_application_database'Expand source code
@property def database(self) -> Database: """ a Database object, the database this collection belongs to. Example: >>> my_coll.database.name 'the_application_database' """ return self._database var full_name : str-
The fully-qualified collection name within the database, in the form "namespace.collection_name".
Example
>>> my_coll.full_name 'default_keyspace.my_v_collection'Expand source code
@property def full_name(self) -> str: """ The fully-qualified collection name within the database, in the form "namespace.collection_name". Example: >>> my_coll.full_name 'default_keyspace.my_v_collection' """ return f"{self.namespace}.{self.name}" var name : str-
The name of this collection.
Example
>>> my_coll.name 'my_v_collection'Expand source code
@property def name(self) -> str: """ The name of this collection. Example: >>> my_coll.name 'my_v_collection' """ # type hint added as for some reason the typechecker gets lost return self._astra_db_collection.collection_name # type: ignore[no-any-return, has-type] var namespace : str-
The namespace this collection is in.
Example
>>> my_coll.namespace 'default_keyspace'Expand source code
@property def namespace(self) -> str: """ The namespace this collection is in. Example: >>> my_coll.namespace 'default_keyspace' """ _namespace = self.database.namespace if _namespace is None: raise ValueError("The collection's DB is set with namespace=None") return _namespace
Methods
def bulk_write(self, requests: Iterable[BaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> BulkWriteResult-
Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently.
This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation.
Args
requests- an iterable over concrete subclasses of
BaseOperation, such asInsertManyorReplaceOne. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered- whether to launch the
requestsone after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency- maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes.
max_time_ms- a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk.
Returns
A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the
requestsiterable.Example
>>> from astrapy.operations import InsertMany, ReplaceOne >>> op1 = InsertMany([{"a": 1}, {"a": 2}]) >>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True) >>> my_coll.bulk_write([op1, op2]) BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'}) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.distinct("replaced") [True]Expand source code
def bulk_write( self, requests: Iterable[BaseOperation], *, ordered: bool = False, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> BulkWriteResult: """ Execute an arbitrary amount of operations such as inserts, updates, deletes either sequentially or concurrently. This method does not execute atomically, i.e. individual operations are each performed in the same way as the corresponding collection method, and each one is a different and unrelated database mutation. Args: requests: an iterable over concrete subclasses of `BaseOperation`, such as `InsertMany` or `ReplaceOne`. Each such object represents an operation ready to be executed on a collection, and is instantiated by passing the same parameters as one would the corresponding collection method. ordered: whether to launch the `requests` one after the other or in arbitrary order, possibly in a concurrent fashion. For performance reasons, False (default) should be preferred when compatible with the needs of the application flow. concurrency: maximum number of concurrent operations executing at a given time. It cannot be more than one for ordered bulk writes. max_time_ms: a timeout, in milliseconds, for the whole bulk write. Remember that, if the method call times out, then there's no guarantee about what portion of the bulk write has been received and successfully executed by the Data API. If not passed, the collection-level setting is used instead: in most cases, however, one should pass a relaxed timeout if longer sequences of operations are to be executed in bulk. Returns: A single BulkWriteResult summarizing the whole list of requested operations. The keys in the map attributes of BulkWriteResult (when present) are the integer indices of the corresponding operation in the `requests` iterable. Example: >>> from astrapy.operations import InsertMany, ReplaceOne >>> op1 = InsertMany([{"a": 1}, {"a": 2}]) >>> op2 = ReplaceOne({"z": 9}, replacement={"z": 9, "replaced": True}, upsert=True) >>> my_coll.bulk_write([op1, op2]) BulkWriteResult(bulk_api_results={0: ..., 1: ...}, deleted_count=0, inserted_count=3, matched_count=0, modified_count=0, upserted_count=1, upserted_ids={1: '2addd676-...'}) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.distinct("replaced") [True] """ # lazy importing here against circular-import error from astrapy.operations import reduce_bulk_write_results if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_BULK_WRITE_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered bulk_write concurrently.") _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"startng a bulk write on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: bulk_write_results: List[BulkWriteResult] = [] for operation_i, operation in enumerate(requests): try: this_bw_result = operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) bulk_write_results.append(this_bw_result) except CumulativeOperationException as exc: partial_result = exc.partial_result partial_bw_result = reduce_bulk_write_results( bulk_write_results + [ partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) ] ) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) except DataAPIResponseException as exc: # the cumulative exceptions, with their # partially-done-info, are handled above: # here it's just one-shot d.a.r. exceptions partial_bw_result = reduce_bulk_write_results(bulk_write_results) dar_exception = exc.data_api_response_exception() raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=[dar_exception], ) full_bw_result = reduce_bulk_write_results(bulk_write_results) logger.info(f"finished a bulk write on '{self.name}'") return full_bw_result else: def _execute_as_either( operation: BaseOperation, operation_i: int ) -> Tuple[Optional[BulkWriteResult], Optional[DataAPIResponseException]]: try: ex_result = operation.execute( self, index_in_bulk_write=operation_i, bulk_write_timeout_ms=timeout_manager.remaining_timeout_ms(), ) return (ex_result, None) except DataAPIResponseException as exc: return (None, exc) with ThreadPoolExecutor(max_workers=_concurrency) as executor: bulk_write_either_futures = [ executor.submit( _execute_as_either, operation, operation_i, ) for operation_i, operation in enumerate(requests) ] bulk_write_either_results = [ bulk_write_either_future.result() for bulk_write_either_future in bulk_write_either_futures ] # regroup bulk_write_successes = [ bwr for bwr, _ in bulk_write_either_results if bwr ] bulk_write_failures = [ bwf for _, bwf in bulk_write_either_results if bwf ] if bulk_write_failures: # extract and cumulate partial_results_from_failures = [ failure.partial_result.to_bulk_write_result( index_in_bulk_write=operation_i ) for failure in bulk_write_failures if isinstance(failure, CumulativeOperationException) ] partial_bw_result = reduce_bulk_write_results( bulk_write_successes + partial_results_from_failures ) # raise and recast the first exception all_dar_exceptions = [ bw_failure.data_api_response_exception() for bw_failure in bulk_write_failures ] dar_exception = all_dar_exceptions[0] raise BulkWriteException( text=dar_exception.text, error_descriptors=dar_exception.error_descriptors, detailed_error_descriptors=dar_exception.detailed_error_descriptors, partial_result=partial_bw_result, exceptions=all_dar_exceptions, ) else: logger.info(f"finished a bulk write on '{self.name}'") return reduce_bulk_write_results(bulk_write_successes) def command(self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload.
Args
body- a JSON-serializable dictionary, the payload of the request.
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary with the response of the HTTP request.
Example
>>> my_coll.command({"countDocuments": {}}) {'status': {'count': 123}}Expand source code
def command( self, body: Dict[str, Any], *, max_time_ms: Optional[int] = None, ) -> Dict[str, Any]: """ Send a POST request to the Data API for this collection with an arbitrary, caller-provided payload. Args: body: a JSON-serializable dictionary, the payload of the request. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary with the response of the HTTP request. Example: >>> my_coll.command({"countDocuments": {}}) {'status': {'count': 123}} """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling command on '{self.name}'") command_result = self.database.command( body=body, namespace=self.namespace, collection_name=self.name, max_time_ms=_max_time_ms, ) logger.info(f"finished calling command on '{self.name}'") return command_result # type: ignore[no-any-return] def count_documents(self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None) ‑> int-
Count the documents in the collection matching the specified filter.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
upper_bound- a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised.
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
the exact count of matching documents.
Example
>>> my_coll.insert_many([{"seq": i} for i in range(20)]) InsertManyResult(...) >>> my_coll.count_documents({}, upper_bound=100) 20 >>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100) 4 >>> my_coll.count_documents({}, upper_bound=10) Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountExceptionNote
Count operations are expensive: for this reason, the best practice is to provide a reasonable
upper_boundaccording to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered.Expand source code
@recast_method_sync def count_documents( self, filter: FilterType, *, upper_bound: int, max_time_ms: Optional[int] = None, ) -> int: """ Count the documents in the collection matching the specified filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. upper_bound: a required ceiling on the result of the count operation. If the actual number of documents exceeds this value, an exception will be raised. Furthermore, if the actual number of documents exceeds the maximum count that the Data API can reach (regardless of upper_bound), an exception will be raised. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: the exact count of matching documents. Example: >>> my_coll.insert_many([{"seq": i} for i in range(20)]) InsertManyResult(...) >>> my_coll.count_documents({}, upper_bound=100) 20 >>> my_coll.count_documents({"seq":{"$gt": 15}}, upper_bound=100) 4 >>> my_coll.count_documents({}, upper_bound=10) Traceback (most recent call last): ... ... astrapy.exceptions.TooManyDocumentsToCountException Note: Count operations are expensive: for this reason, the best practice is to provide a reasonable `upper_bound` according to the caller expectations. Moreover, indiscriminate usage of count operations for sizeable amounts of documents (i.e. in the thousands and more) is discouraged in favor of alternative application-specific solutions. Keep in mind that the Data API has a hard upper limit on the amount of documents it will count, and that an exception will be thrown by this method if this limit is encountered. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info("calling count_documents") cd_response = self._astra_db_collection.count_documents( filter=filter, timeout_info=base_timeout_info(_max_time_ms), ) logger.info("finished calling count_documents") if "count" in cd_response.get("status", {}): count: int = cd_response["status"]["count"] if cd_response["status"].get("moreData", False): raise TooManyDocumentsToCountException( text=f"Document count exceeds {count}, the maximum allowed by the server", server_max_count_exceeded=True, ) else: if count > upper_bound: raise TooManyDocumentsToCountException( text="Document count exceeds required upper bound", server_max_count_exceeded=False, ) else: return count else: raise DataAPIFaultyResponseException( text="Faulty response from count_documents API command.", raw_response=cd_response, ) def delete_all(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Delete all documents in a collection.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary of the form {"ok": 1} to signal successful deletion.
Example
>>> my_coll.distinct("seq") [2, 1, 0] >>> my_coll.count_documents({}, upper_bound=100) 4 >>> my_coll.delete_all() {'ok': 1} >>> my_coll.count_documents({}, upper_bound=100) 0Note
Use with caution.
Deprecated since version: 1.3.0
This will be removed in 2.0.0. Use delete_many with filter={} instead.
Expand source code
@deprecation.deprecated( # type: ignore[misc] deprecated_in="1.3.0", removed_in="2.0.0", current_version=__version__, details="Use delete_many with filter={} instead.", ) def delete_all(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Delete all documents in a collection. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> my_coll.distinct("seq") [2, 1, 0] >>> my_coll.count_documents({}, upper_bound=100) 4 >>> my_coll.delete_all() {'ok': 1} >>> my_coll.count_documents({}, upper_bound=100) 0 Note: Use with caution. """ dm_result = self.delete_many(filter={}, max_time_ms=max_time_ms) if dm_result.deleted_count == -1: return {"ok": 1} else: raise DataAPIFaultyResponseException( text="Unexpected response from collection.delete_many({}).", raw_response=None, ) def delete_many(self, filter: FilterType, *, max_time_ms: Optional[int] = None) ‑> DeleteResult-
Delete all documents matching a provided filter.
Args
filter- a predicate expressed as a dictionary according to the
Data API filter syntax. Examples are:
{}
{"name": "John"}
{"price": {"$lt": 100}}
{"$and": [{"name": "John"}, {"price": {"$lt": 100}}]}
See the Data API documentation for the full set of operators.
Passing an empty filter,
{}, completely erases all contents of the collection. max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls.
Returns
a DeleteResult object summarizing the outcome of the delete operation.
Example
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=2) >>> my_coll.distinct("seq") [2] >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=0)Note
This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the
filter={}case, whereby the operation is atomic.Expand source code
@recast_method_sync def delete_many( self, filter: FilterType, *, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete all documents matching a provided filter. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. Passing an empty filter, `{}`, completely erases all contents of the collection. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: keep in mind that this method entails successive HTTP requests to the API, depending on how many documents are to be deleted. For this reason, in most cases it is suggested to relax the timeout compared to other method calls. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=2) >>> my_coll.distinct("seq") [2] >>> my_coll.delete_many({"seq": {"$lte": 1}}) DeleteResult(raw_results=..., deleted_count=0) Note: This operation is in general not atomic. Depending on the amount of matching documents, it can keep running (in a blocking way) for a macroscopic time. In that case, new documents that are meanwhile inserted (e.g. from another process/application) will be deleted during the execution of this method call until the collection is devoid of matches. An exception is the `filter={}` case, whereby the operation is atomic. """ dm_responses: List[Dict[str, Any]] = [] deleted_count = 0 must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) logger.info(f"starting delete_many on '{self.name}'") while must_proceed: logger.info(f"calling delete_many on '{self.name}'") this_dm_response = self._astra_db_collection.delete_many( filter=filter, skip_error_check=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling delete_many on '{self.name}'") # if errors, quit early if this_dm_response.get("errors", []): partial_result = DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) all_dm_responses = dm_responses + [this_dm_response] raise DeleteManyException.from_responses( commands=[None for _ in all_dm_responses], raw_responses=all_dm_responses, partial_result=partial_result, ) else: this_dc = this_dm_response.get("status", {}).get("deletedCount") if this_dc is None: raise DataAPIFaultyResponseException( text="Faulty response from delete_many API command.", raw_response=this_dm_response, ) dm_responses.append(this_dm_response) deleted_count += this_dc must_proceed = this_dm_response.get("status", {}).get("moreData", False) logger.info(f"finished delete_many on '{self.name}'") return DeleteResult( deleted_count=deleted_count, raw_results=dm_responses, ) def delete_one(self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> DeleteResult-
Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
This parameter cannot be used together with
sort. See thefindmethod for more details on this parameter. DEPRECATED (removal in 2.0). Use a$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to
vector, provided such a service is configured for the collection, and achieves the same effect. DEPRECATED (removal in 2.0). Use a$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
deleted one. See the
findmethod for more on sorting. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a DeleteResult object summarizing the outcome of the delete operation.
Example
>>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0)Expand source code
@recast_method_sync def delete_one( self, filter: FilterType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> DeleteResult: """ Delete one document matching a provided filter. This method never deletes more than a single document, regardless of the number of matches to the provided filters. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with `sort`. See the `find` method for more details on this parameter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the `find` method for more on sorting. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a DeleteResult object summarizing the outcome of the delete operation. Example: >>> my_coll.insert_many([{"seq": 1}, {"seq": 0}, {"seq": 2}]) InsertManyResult(...) >>> my_coll.delete_one({"seq": 1}) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0, 2] >>> my_coll.delete_one( ... {"seq": {"$exists": True}}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) DeleteResult(raw_results=..., deleted_count=1) >>> my_coll.distinct("seq") [0] >>> my_coll.delete_one({"seq": 2}) DeleteResult(raw_results=..., deleted_count=0) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling delete_one_by_predicate on '{self.name}'") do_response = self._astra_db_collection.delete_one_by_predicate( filter=filter, timeout_info=base_timeout_info(_max_time_ms), sort=_sort ) logger.info(f"finished calling delete_one_by_predicate on '{self.name}'") if "deletedCount" in do_response.get("status", {}): deleted_count = do_response["status"]["deletedCount"] if deleted_count == -1: return DeleteResult( deleted_count=None, raw_results=[do_response], ) else: # expected a non-negative integer: return DeleteResult( deleted_count=deleted_count, raw_results=[do_response], ) else: raise DataAPIFaultyResponseException( text="Faulty response from delete_one API command.", raw_response=do_response, ) def distinct(self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None) ‑> List[Any]-
Return a list of the unique values of
keyacross the documents in the collection that match the provided filter.Args
key- the name of the field whose value is inspected across documents.
Keys can use dot-notation to descend to deeper document levels.
Example of acceptable
keyvalues: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
max_time_ms- a timeout, in milliseconds, with the same meaning as for
find. If not passed, the collection-level setting is used instead.
Returns
a list of all different values for
keyfound across the documents that match the filter. The result list has no repeated items.Example
>>> my_coll.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...']) >>> my_coll.distinct("name") ['Marco', 'Emma'] >>> my_coll.distinct("city") ['Helsinki'] >>> my_coll.distinct("food") ['apple', 'orange', {'likes_fruit': True, 'allergies': []}] >>> my_coll.distinct("food.1") ['orange'] >>> my_coll.distinct("food.allergies") [] >>> my_coll.distinct("food.likes_fruit") [True]Note
It must be kept in mind that
distinctis a client-side operation, which effectively browses all required documents using the logic of thefindmethod and collects the unique values found forkey. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large.Note
For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the
findcommand.Expand source code
def distinct( self, key: str, *, filter: Optional[FilterType] = None, max_time_ms: Optional[int] = None, ) -> List[Any]: """ Return a list of the unique values of `key` across the documents in the collection that match the provided filter. Args: key: the name of the field whose value is inspected across documents. Keys can use dot-notation to descend to deeper document levels. Example of acceptable `key` values: "field" "field.subfield" "field.3" "field.3.subfield" If lists are encountered and no numeric index is specified, all items in the list are visited. filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. max_time_ms: a timeout, in milliseconds, with the same meaning as for `find`. If not passed, the collection-level setting is used instead. Returns: a list of all different values for `key` found across the documents that match the filter. The result list has no repeated items. Example: >>> my_coll.insert_many( ... [ ... {"name": "Marco", "food": ["apple", "orange"], "city": "Helsinki"}, ... {"name": "Emma", "food": {"likes_fruit": True, "allergies": []}}, ... ] ... ) InsertManyResult(raw_results=..., inserted_ids=['c5b99f37-...', 'd6416321-...']) >>> my_coll.distinct("name") ['Marco', 'Emma'] >>> my_coll.distinct("city") ['Helsinki'] >>> my_coll.distinct("food") ['apple', 'orange', {'likes_fruit': True, 'allergies': []}] >>> my_coll.distinct("food.1") ['orange'] >>> my_coll.distinct("food.allergies") [] >>> my_coll.distinct("food.likes_fruit") [True] Note: It must be kept in mind that `distinct` is a client-side operation, which effectively browses all required documents using the logic of the `find` method and collects the unique values found for `key`. As such, there may be performance, latency and ultimately billing implications if the amount of matching documents is large. Note: For details on the behaviour of "distinct" in conjunction with real-time changes in the collection contents, see the Note of the `find` command. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms f_cursor = Cursor( collection=self, filter=filter, projection={key: True}, max_time_ms=None, overall_max_time_ms=_max_time_ms, ) return f_cursor.distinct(key) # type: ignore[no-any-return] def drop(self, *, max_time_ms: Optional[int] = None) ‑> Dict[str, Any]-
Drop the collection, i.e. delete it from the database along with all the documents it contains.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored.
Returns
a dictionary of the form {"ok": 1} to signal successful deletion.
Example
>>> my_coll.find_one({}) {'_id': '...', 'a': 100} >>> my_coll.drop() {'ok': 1} >>> my_coll.find_one({}) Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collectionNote
Use with caution.
Note
Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further.
Expand source code
def drop(self, *, max_time_ms: Optional[int] = None) -> Dict[str, Any]: """ Drop the collection, i.e. delete it from the database along with all the documents it contains. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Remember there is not guarantee that a request that has timed out us not in fact honored. Returns: a dictionary of the form {"ok": 1} to signal successful deletion. Example: >>> my_coll.find_one({}) {'_id': '...', 'a': 100} >>> my_coll.drop() {'ok': 1} >>> my_coll.find_one({}) Traceback (most recent call last): ... ... astrapy.exceptions.DataAPIResponseException: Collection does not exist, collection name: my_collection Note: Use with caution. Note: Once the method succeeds, methods on this object can still be invoked: however, this hardly makes sense as the underlying actual collection is no more. It is responsibility of the developer to design a correct flow which avoids using a deceased collection any further. """ _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"dropping collection '{self.name}' (self)") drop_result = self.database.drop_collection(self, max_time_ms=_max_time_ms) logger.info(f"finished dropping collection '{self.name}' (self)") return drop_result # type: ignore[no-any-return] def estimated_document_count(self, *, max_time_ms: Optional[int] = None) ‑> int-
Query the API server for an estimate of the document count in the collection.
Contrary to
count_documents, this method has no filtering parameters.Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a server-provided estimate count of the documents in the collection.
Example
>>> my_coll.estimated_document_count() 35700Expand source code
def estimated_document_count( self, *, max_time_ms: Optional[int] = None, ) -> int: """ Query the API server for an estimate of the document count in the collection. Contrary to `count_documents`, this method has no filtering parameters. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a server-provided estimate count of the documents in the collection. Example: >>> my_coll.estimated_document_count() 35700 """ _max_time_ms = max_time_ms or self.api_options.max_time_ms ed_response = self.command( {"estimatedDocumentCount": {}}, max_time_ms=_max_time_ms, ) if "count" in ed_response.get("status", {}): count: int = ed_response["status"]["count"] return count else: raise DataAPIFaultyResponseException( text="Faulty response from estimated_document_count API command.", raw_response=ed_response, ) def find(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Cursor-
Find documents on the collection, matching a certain provided filter.
The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the
findmethod was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of thesortoption in a non-vector-search manner.Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. skip- with this integer parameter, what would be the first
skipdocuments returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicitsortcriterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit- this (integer) parameter sets a limit over how many documents
are returned. Once
limitis reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search).
When running similarity search on a collection, no other sorting
criteria can be specified. Moreover, there is an upper bound
to the number of documents that can be returned. For details,
see the Note about upper bounds and the Data API documentation.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to
vector, provided such a service is configured for the collection, and achieves the same effect. DEPRECATED (removal in 2.0). Use a$vectorizekey in the sort clause dict instead. include_similarity- a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in each
returned document. Can only be used for vector ANN search, i.e.
when either
vectoris supplied or thesortparameter has the shape {"$vector": …}. include_sort_vector- a boolean to request query vector used in this search.
If set to True (and if the invocation is a vector search), calling
the
get_sort_vectormethod on the returned cursor will yield the vector used for the ANN search. sort- with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting, as well as
the one about upper bounds, for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in
sort. max_time_ms- a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead.
Returns
- a Cursor object representing iterations over the matching documents
- (see the Cursor object for how to use it. The simplest thing is to
run a for loopfor document in collection.sort(...):).
Examples
>>> filter = {"seq": {"$exists": True}} >>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... 37 35 10 36 27 >>> cursor1 = my_coll.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) >>> [doc["_id"] for doc in cursor1] ['97e85f81-...', '1581efe4-...', '...', '...'] >>> cursor2 = my_coll.find({}, limit=3) >>> cursor2.distinct("seq") [37, 35, 10]>>> my_coll.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) >>> ann_tags = [ ... document["tag"] ... for document in my_coll.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] >>> ann_tags ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE)>>> cursor = my_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> cursor.get_sort_vector() [3.0, 3.0] >>> matches = list(cursor) >>> cursor.get_sort_vector() [3.0, 3.0]Note
The following are example values for the
sortparameter. When no particular order is required: sort={} # (default when parameter not provided) When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ acollections.OrderedDictin these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]}Note
Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as
.distinct()on a cursor.Note
When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the
findwas started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor.Expand source code
def find( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, skip: Optional[int] = None, limit: Optional[int] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, include_sort_vector: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Cursor: """ Find documents on the collection, matching a certain provided filter. The method returns a Cursor that can then be iterated over. Depending on the method call pattern, the iteration over all documents can reflect collection mutations occurred since the `find` method was called, or not. In cases where the cursor reflects mutations in real-time, it will iterate over cursors in an approximate way (i.e. exhibiting occasional skipped or duplicate documents). This happens when making use of the `sort` option in a non-vector-search manner. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. skip: with this integer parameter, what would be the first `skip` documents returned by the query are discarded, and the results start from the (skip+1)-th document. This parameter can be used only in conjunction with an explicit `sort` criterion of the ascending/descending type (i.e. it cannot be used when not sorting, nor with vector-based ANN search). limit: this (integer) parameter sets a limit over how many documents are returned. Once `limit` is reached (or the cursor is exhausted for lack of matching documents), nothing more is returned. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search). When running similarity search on a collection, no other sorting criteria can be specified. Moreover, there is an upper bound to the number of documents that can be returned. For details, see the Note about upper bounds and the Data API documentation. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in each returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. include_sort_vector: a boolean to request query vector used in this search. If set to True (and if the invocation is a vector search), calling the `get_sort_vector` method on the returned cursor will yield the vector used for the ANN search. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting, as well as the one about upper bounds, for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for each single one of the underlying HTTP requests used to fetch documents as the cursor is iterated over. If not passed, the collection-level setting is used instead. Returns: a Cursor object representing iterations over the matching documents (see the Cursor object for how to use it. The simplest thing is to run a for loop: `for document in collection.sort(...):`). Examples: >>> filter = {"seq": {"$exists": True}} >>> for doc in my_coll.find(filter, projection={"seq": True}, limit=5): ... print(doc["seq"]) ... 37 35 10 36 27 >>> cursor1 = my_coll.find( ... {}, ... limit=4, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) >>> [doc["_id"] for doc in cursor1] ['97e85f81-...', '1581efe4-...', '...', '...'] >>> cursor2 = my_coll.find({}, limit=3) >>> cursor2.distinct("seq") [37, 35, 10] >>> my_coll.insert_many([ ... {"tag": "A", "$vector": [4, 5]}, ... {"tag": "B", "$vector": [3, 4]}, ... {"tag": "C", "$vector": [3, 2]}, ... {"tag": "D", "$vector": [4, 1]}, ... {"tag": "E", "$vector": [2, 5]}, ... ]) >>> ann_tags = [ ... document["tag"] ... for document in my_coll.find( ... {}, ... sort={"$vector": [3, 3]}, ... limit=3, ... ) ... ] >>> ann_tags ['A', 'B', 'C'] >>> # (assuming the collection has metric VectorMetric.COSINE) >>> cursor = my_coll.find( ... sort={"$vector": [3, 3]}, ... limit=3, ... include_sort_vector=True, ... ) >>> cursor.get_sort_vector() [3.0, 3.0] >>> matches = list(cursor) >>> cursor.get_sort_vector() [3.0, 3.0] Note: The following are example values for the `sort` parameter. When no particular order is required: sort={} # (default when parameter not provided) When sorting by a certain value in ascending/descending order: sort={"field": SortDocuments.ASCENDING} sort={"field": SortDocuments.DESCENDING} When sorting first by "field" and then by "subfield" (while modern Python versions preserve the order of dictionaries, it is suggested for clarity to employ a `collections.OrderedDict` in these cases): sort={ "field": SortDocuments.ASCENDING, "subfield": SortDocuments.ASCENDING, } When running a vector similarity (ANN) search: sort={"$vector": [0.4, 0.15, -0.5]} Note: Some combinations of arguments impose an implicit upper bound on the number of documents that are returned by the Data API. More specifically: (a) Vector ANN searches cannot return more than a number of documents that at the time of writing is set to 1000 items. (b) When using a sort criterion of the ascending/descending type, the Data API will return a smaller number of documents, set to 20 at the time of writing, and stop there. The returned documents are the top results across the whole collection according to the requested criterion. These provisions should be kept in mind even when subsequently running a command such as `.distinct()` on a cursor. Note: When not specifying sorting criteria at all (by vector or otherwise), the cursor can scroll through an arbitrary number of documents as the Data API and the client periodically exchange new chunks of documents. It should be noted that the behavior of the cursor in the case documents have been added/removed after the `find` was started depends on database internals and it is not guaranteed, nor excluded, that such "real-time" changes in the data would be picked up by the cursor. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms if include_similarity is not None and not _is_vector_sort(_sort): raise ValueError( "Cannot use `include_similarity` when not searching through `vector`." ) return ( Cursor( collection=self, filter=filter, projection=projection, max_time_ms=_max_time_ms, overall_max_time_ms=None, ) .skip(skip) .limit(limit) .sort(_sort) .include_similarity(include_similarity) .include_sort_vector(include_sort_vector) ) def find_one(self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Run a search, returning the first document in the collection that matches provided filters, if any is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to perform vector search (i.e. ANN,
or "approximate nearest-neighbours" search), extracting the most
similar document in the collection matching the filter.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. include_similarity- a boolean to request the numeric value of the
similarity to be returned as an added "$similarity" key in the
returned document. Can only be used for vector ANN search, i.e.
when either
vectoris supplied or thesortparameter has the shape {"$vector": …}. sort- with this dictionary parameter one can control the order
the documents are returned. See the Note about sorting for details.
Vector-based ANN sorting is achieved by providing a "$vector"
or a "$vectorize" key in
sort. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a dictionary expressing the required document, otherwise None.
Examples
>>> my_coll.find_one({}) {'_id': '68d1e515-...', 'seq': 37} >>> my_coll.find_one({"seq": 10}) {'_id': 'd560e217-...', 'seq': 10} >>> my_coll.find_one({"seq": 1011}) >>> # (returns None for no matches) >>> my_coll.find_one({}, projection={"seq": False}) {'_id': '68d1e515-...'} >>> my_coll.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) {'_id': '97e85f81-...', 'seq': 69} >>> my_coll.find_one({}, sort={"$vector": [1, 0]}, projection={"*": True}) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]}Note
See the
findmethod for more details on the accepted parameters (whereasskipandlimitare not valid parameters forfind_one).Expand source code
def find_one( self, filter: Optional[FilterType] = None, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, include_similarity: Optional[bool] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Run a search, returning the first document in the collection that matches provided filters, if any is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to perform vector search (i.e. ANN, or "approximate nearest-neighbours" search), extracting the most similar document in the collection matching the filter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. include_similarity: a boolean to request the numeric value of the similarity to be returned as an added "$similarity" key in the returned document. Can only be used for vector ANN search, i.e. when either `vector` is supplied or the `sort` parameter has the shape {"$vector": ...}. sort: with this dictionary parameter one can control the order the documents are returned. See the Note about sorting for details. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a dictionary expressing the required document, otherwise None. Examples: >>> my_coll.find_one({}) {'_id': '68d1e515-...', 'seq': 37} >>> my_coll.find_one({"seq": 10}) {'_id': 'd560e217-...', 'seq': 10} >>> my_coll.find_one({"seq": 1011}) >>> # (returns None for no matches) >>> my_coll.find_one({}, projection={"seq": False}) {'_id': '68d1e515-...'} >>> my_coll.find_one( ... {}, ... sort={"seq": astrapy.constants.SortDocuments.DESCENDING}, ... ) {'_id': '97e85f81-...', 'seq': 69} >>> my_coll.find_one({}, sort={"$vector": [1, 0]}, projection={"*": True}) {'_id': '...', 'tag': 'D', '$vector': [4.0, 1.0]} Note: See the `find` method for more details on the accepted parameters (whereas `skip` and `limit` are not valid parameters for `find_one`). """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_cursor = self.find( filter=filter, projection=projection, skip=None, limit=1, vector=vector, vectorize=vectorize, include_similarity=include_similarity, sort=sort, max_time_ms=_max_time_ms, ) try: document = fo_cursor.__next__() return document # type: ignore[no-any-return] except StopIteration: return None def find_one_and_delete(self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document in the collection and delete it. The deleted document, however, is the return value of the method.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
This parameter cannot be used together with
sort. See thefindmethod for more details on this parameter. DEPRECATED (removal in 2.0). Use a$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
This can be supplied in (exclusive) alternative to
vector, provided such a service is configured for the collection, and achieves the same effect. DEPRECATED (removal in 2.0). Use a$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
deleted one. See the
findmethod for more on sorting. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
Either the document (or a projection thereof, as requested), or None if no matches were found in the first place.
Example
>>> my_coll.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) InsertManyResult(...) >>> my_coll.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) {'_id': '5997fb48-...', 'species': 'swan'} >>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}}) >>> # (returns None for no matches)Expand source code
@recast_method_sync def find_one_and_delete( self, filter: FilterType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document in the collection and delete it. The deleted document, however, is the return value of the method. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. This parameter cannot be used together with `sort`. See the `find` method for more details on this parameter. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. This can be supplied in (exclusive) alternative to `vector`, provided such a service is configured for the collection, and achieves the same effect. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the deleted one. See the `find` method for more on sorting. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: Either the document (or a projection thereof, as requested), or None if no matches were found in the first place. Example: >>> my_coll.insert_many( ... [ ... {"species": "swan", "class": "Aves"}, ... {"species": "frog", "class": "Amphibia"}, ... ], ... ) InsertManyResult(...) >>> my_coll.find_one_and_delete( ... {"species": {"$ne": "frog"}}, ... projection=["species"], ... ) {'_id': '5997fb48-...', 'species': 'swan'} >>> my_coll.find_one_and_delete({"species": {"$ne": "frog"}}) >>> # (returns None for no matches) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) _projection = normalize_optional_projection(projection) logger.info(f"calling find_one_and_delete on '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_response = self._astra_db_collection.find_one_and_delete( sort=_sort, filter=filter, projection=_projection, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_delete on '{self.name}'") if "document" in fo_response.get("data", {}): document = fo_response["data"]["document"] return document # type: ignore[no-any-return] else: deleted_count = fo_response.get("status", {}).get("deletedCount") if deleted_count == 0: return None else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_delete API command.", raw_response=fo_response, ) def find_one_and_replace(self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
replacement- the new document to write into the collection.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True,
replacementis inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document- a flag controlling what document is returned:
if set to
ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set toReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before". max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the
return_documentparameter).Example
>>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"}) InsertOneResult(...) >>> my_coll.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) {'_id': 'rule1', 'text': 'all animals are equal'} >>> my_coll.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'rule1', 'text': 'and the pigs are the rulers'} >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) {'text': 'F=ma'}Expand source code
@recast_method_sync def find_one_and_replace( self, filter: FilterType, replacement: DocumentType, *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and replace it entirely with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no replacement was inserted (depending on the `return_document` parameter). Example: >>> my_coll.insert_one({"_id": "rule1", "text": "all animals are equal"}) InsertOneResult(...) >>> my_coll.find_one_and_replace( ... {"_id": "rule1"}, ... {"text": "some animals are more equal!"}, ... ) {'_id': 'rule1', 'text': 'all animals are equal'} >>> my_coll.find_one_and_replace( ... {"text": "some animals are more equal!"}, ... {"text": "and the pigs are the rulers"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'rule1', 'text': 'and the pigs are the rulers'} >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma^2"}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_replace( ... {"_id": "rule2"}, ... {"text": "F=ma"}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... projection={"_id": False}, ... ) {'text': 'F=ma'} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_replace on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) def find_one_and_update(self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = 'before', max_time_ms: Optional[int] = None) ‑> Optional[Dict[str, Any]]-
Find a document on the collection and update it as requested, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
projection- it controls which parts of the document are returned.
It can be an allow-list:
{"f1": True, "f2": True}, or a deny-list:{"fx": False, "fy": False}, but not a mixture (except for the_idand other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections{"*": True}and{"*": False}have the effect of returning the whole document and{}respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance,{"array": {"$slice": 2}},{"array": {"$slice": -2}},{"array": {"$slice": [4, 2]}}or{"array": {"$slice": [-4, 2]}}. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as$vectoror$vectorize. See the Data API documentation for more on projections. vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document- a flag controlling what document is returned:
if set to
ReturnDocument.BEFORE, or the string "before", the document found on database is returned; if set toReturnDocument.AFTER, or the string "after", the new document is returned. The default is "before". max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the
return_documentparameter).Example
>>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) {'_id': 'a80106f2-...', 'Marco': 'Polo'} >>> my_coll.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3} >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0}Expand source code
@recast_method_sync def find_one_and_update( self, filter: FilterType, update: Dict[str, Any], *, projection: Optional[ProjectionType] = None, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, return_document: str = ReturnDocument.BEFORE, max_time_ms: Optional[int] = None, ) -> Union[DocumentType, None]: """ Find a document on the collection and update it as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. projection: it controls which parts of the document are returned. It can be an allow-list: `{"f1": True, "f2": True}`, or a deny-list: `{"fx": False, "fy": False}`, but not a mixture (except for the `_id` and other special fields, which can be associated to both True or False independently of the rest of the specification). The special star-projections `{"*": True}` and `{"*": False}` have the effect of returning the whole document and `{}` respectively. For lists in documents, slice directives can be passed to select portions of the list: for instance, `{"array": {"$slice": 2}}`, `{"array": {"$slice": -2}}`, `{"array": {"$slice": [4, 2]}}` or `{"array": {"$slice": [-4, 2]}}`. An iterable over strings will be treated implicitly as an allow-list. The default projection (used if this parameter is not passed) does not necessarily include "special" fields such as `$vector` or `$vectorize`. See the Data API documentation for more on projections. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. return_document: a flag controlling what document is returned: if set to `ReturnDocument.BEFORE`, or the string "before", the document found on database is returned; if set to `ReturnDocument.AFTER`, or the string "after", the new document is returned. The default is "before". max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: A document (or a projection thereof, as required), either the one before the replace operation or the one after that. Alternatively, the method returns None to represent that no matching document was found, or that no update was applied (depending on the `return_document` parameter). Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.find_one_and_update( ... {"Marco": {"$exists": True}}, ... {"$set": {"title": "Mr."}}, ... ) {'_id': 'a80106f2-...', 'Marco': 'Polo'} >>> my_coll.find_one_and_update( ... {"title": "Mr."}, ... {"$inc": {"rank": 3}}, ... projection=["title", "rank"], ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'a80106f2-...', 'title': 'Mr.', 'rank': 3} >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) >>> # (returns None for no matches) >>> my_coll.find_one_and_update( ... {"name": "Johnny"}, ... {"$set": {"rank": 0}}, ... upsert=True, ... return_document=astrapy.constants.ReturnDocument.AFTER, ... ) {'_id': 'cb4ef2ab-...', 'name': 'Johnny', 'rank': 0} """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "returnDocument": return_document, "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_update( update=update, filter=filter, projection=normalize_optional_projection(projection), sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): ret_document = fo_response.get("data", {}).get("document") if ret_document is None: return None else: return ret_document # type: ignore[no-any-return] else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) def info(self) ‑> CollectionInfo-
Information on the collection (name, location, database), in the form of a CollectionInfo object.
Not to be confused with the collection
optionsmethod (related to the collection internal configuration).Example
>>> my_coll.info().database_info.region 'eu-west-1' >>> my_coll.info().full_name 'default_keyspace.my_v_collection'Note
the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for
Database.info()for more details.Expand source code
def info(self) -> CollectionInfo: """ Information on the collection (name, location, database), in the form of a CollectionInfo object. Not to be confused with the collection `options` method (related to the collection internal configuration). Example: >>> my_coll.info().database_info.region 'eu-west-1' >>> my_coll.info().full_name 'default_keyspace.my_v_collection' Note: the returned CollectionInfo wraps, among other things, the database information: as such, calling this method triggers the same-named method of a Database object (which, in turn, performs a HTTP request to the DevOps API). See the documentation for `Database.info()` for more details. """ return CollectionInfo( database_info=self.database.info(), namespace=self.namespace, name=self.name, full_name=self.full_name, ) def insert_many(self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None) ‑> InsertManyResult-
Insert a list of documents into the collection. This is not an atomic operation.
Args
documents- an iterable of dictionaries, each a document to insert.
Documents may specify their
_idfield or leave it out, in which case it will be added automatically. vectors- an optional list of vectors (as many vectors as the provided
documents) to associate to the documents when inserting.
Passing vectors this way is indeed equivalent to the "$vector" field
of the documents, however the two are mutually exclusive.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the documents instead. vectorize- an optional list of strings to be made into as many vectors
(one per document), if such a service is configured for the collection.
Passing this parameter is equivalent to providing a
$vectorizefield in the documents themselves, however the two are mutually exclusive. DEPRECATED (removal in 2.0). Use a$vectorizekey in the documents instead. ordered- if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster.
chunk_size- how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default.
concurrency- maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions.
max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here.
Returns
an InsertManyResult object.
Examples
>>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_many( ... [{"a": 10}, {"a": 5}, {"b": [True, False, False]}], ... ordered=True, ... ) InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...']) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) InsertManyResult(raw_results=..., inserted_ids=[... ...]) >>> my_coll.count_documents({}, upper_bound=100) 53 >>> my_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... ) InsertManyResult(...)Note
Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important.
Note
A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an
_idalready present on the collection, or its vector dimension may not match the collection setting.For an ordered insertion, the method will raise an exception at the first such faulty document – nevertheless, all documents processed until then will end up being written to the database.
For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised – and all insertable documents will have been written to the database, including those "after" the troublesome ones.
If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole
insert_manyoperation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database.Expand source code
@recast_method_sync def insert_many( self, documents: Iterable[DocumentType], *, vectors: Optional[Iterable[Optional[VectorType]]] = None, vectorize: Optional[Iterable[Optional[str]]] = None, ordered: bool = False, chunk_size: Optional[int] = None, concurrency: Optional[int] = None, max_time_ms: Optional[int] = None, ) -> InsertManyResult: """ Insert a list of documents into the collection. This is not an atomic operation. Args: documents: an iterable of dictionaries, each a document to insert. Documents may specify their `_id` field or leave it out, in which case it will be added automatically. vectors: an optional list of vectors (as many vectors as the provided documents) to associate to the documents when inserting. Passing vectors this way is indeed equivalent to the "$vector" field of the documents, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the documents instead. vectorize: an optional list of strings to be made into as many vectors (one per document), if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the documents themselves, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the documents instead. ordered: if False (default), the insertions can occur in arbitrary order and possibly concurrently. If True, they are processed sequentially. If there are no specific reasons against it, unordered insertions are to be preferred as they complete much faster. chunk_size: how many documents to include in a single API request. Exceeding the server maximum allowed value results in an error. Leave it unspecified (recommended) to use the system default. concurrency: maximum number of concurrent requests to the API at a given time. It cannot be more than one for ordered insertions. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: If many documents are being inserted, this method corresponds to several HTTP requests: in such cases one may want to specify a more tolerant timeout here. Returns: an InsertManyResult object. Examples: >>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_many( ... [{"a": 10}, {"a": 5}, {"b": [True, False, False]}], ... ordered=True, ... ) InsertManyResult(raw_results=..., inserted_ids=['184bb06f-...', '...', '...']) >>> my_coll.count_documents({}, upper_bound=100) 3 >>> my_coll.insert_many( ... [{"seq": i} for i in range(50)], ... concurrency=5, ... ) InsertManyResult(raw_results=..., inserted_ids=[... ...]) >>> my_coll.count_documents({}, upper_bound=100) 53 >>> my_coll.insert_many( ... [ ... {"tag": "a", "$vector": [1, 2]}, ... {"tag": "b", "$vector": [3, 4]}, ... ] ... ) InsertManyResult(...) Note: Unordered insertions are executed with some degree of concurrency, so it is usually better to prefer this mode unless the order in the document sequence is important. Note: A failure mode for this command is related to certain faulty documents found among those to insert: a document may have the an `_id` already present on the collection, or its vector dimension may not match the collection setting. For an ordered insertion, the method will raise an exception at the first such faulty document -- nevertheless, all documents processed until then will end up being written to the database. For unordered insertions, if the error stems from faulty documents the insertion proceeds until exhausting the input documents: then, an exception is raised -- and all insertable documents will have been written to the database, including those "after" the troublesome ones. If, on the other hand, there are errors not related to individual documents (such as a network connectivity error), the whole `insert_many` operation will stop in mid-way, an exception will be raised, and only a certain amount of the input documents will have made their way to the database. """ check_deprecated_vector_ize( vector=None, vectors=vectors, vectorize=vectorize, kind="insert" ) if concurrency is None: if ordered: _concurrency = 1 else: _concurrency = DEFAULT_INSERT_MANY_CONCURRENCY else: _concurrency = concurrency if _concurrency > 1 and ordered: raise ValueError("Cannot run ordered insert_many concurrently.") if chunk_size is None: _chunk_size = DEFAULT_INSERT_NUM_DOCUMENTS else: _chunk_size = chunk_size _documents = _collate_vectors_to_documents(documents, vectors, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting {len(_documents)} documents in '{self.name}'") raw_results: List[Dict[str, Any]] = [] timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) if ordered: options = {"ordered": True} inserted_ids: List[Any] = [] for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") chunk_response = self._astra_db_collection.insert_many( documents=_documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished inserting a chunk of documents in '{self.name}'") # accumulate the results in this call chunk_inserted_ids = (chunk_response.get("status") or {}).get( "insertedIds", [] ) inserted_ids += chunk_inserted_ids raw_results += [chunk_response] # if errors, quit early if chunk_response.get("errors", []): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_response( command=None, raw_response=chunk_response, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result else: # unordered: concurrent or not, do all of them and parse the results options = {"ordered": False} if _concurrency > 1: with ThreadPoolExecutor(max_workers=_concurrency) as executor: def _chunk_insertor( document_chunk: List[Dict[str, Any]] ) -> Dict[str, Any]: logger.info(f"inserting a chunk of documents in '{self.name}'") im_response = self._astra_db_collection.insert_many( documents=document_chunk, options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) return im_response raw_results = list( executor.map( _chunk_insertor, ( _documents[i : i + _chunk_size] for i in range(0, len(_documents), _chunk_size) ), ) ) else: for i in range(0, len(_documents), _chunk_size): logger.info(f"inserting a chunk of documents in '{self.name}'") raw_results.append( self._astra_db_collection.insert_many( _documents[i : i + _chunk_size], options=options, partial_failures_allowed=True, timeout_info=timeout_manager.remaining_timeout_info(), ) ) logger.info( f"finished inserting a chunk of documents in '{self.name}'" ) # recast raw_results inserted_ids = [ inserted_id for chunk_response in raw_results for inserted_id in (chunk_response.get("status") or {}).get( "insertedIds", [] ) ] # check-raise if any( [chunk_response.get("errors", []) for chunk_response in raw_results] ): partial_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) raise InsertManyException.from_responses( commands=[None for _ in raw_results], raw_responses=raw_results, partial_result=partial_result, ) # return full_result = InsertManyResult( raw_results=raw_results, inserted_ids=inserted_ids, ) logger.info( f"finished inserting {len(_documents)} documents in '{self.name}'" ) return full_result def insert_one(self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None) ‑> InsertOneResult-
Insert a single document in the collection in an atomic operation.
Args
document- the dictionary expressing the document to insert.
The
_idfield of the document can be left out, in which case it will be created automatically. vector- a vector (a list of numbers appropriate for the collection)
for the document. Passing this parameter is equivalent to
providing a
$vectorfield within the document itself, however the two are mutually exclusive. DEPRECATED (removal in 2.0). Use a$vectorkey in the document instead. vectorize- a string to be made into a vector, if such a service
is configured for the collection. Passing this parameter is
equivalent to providing a
$vectorizefield in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist withvector. DEPRECATED (removal in 2.0). Use a$vectorizekey in the document instead. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an InsertOneResult object.
Examples
>>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...') >>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) InsertOneResult(raw_results=..., inserted_id='user-123') >>> my_coll.count_documents({}, upper_bound=10) 2>>> my_coll.insert_one({"tag": "v", "$vector": [10, 11]}) InsertOneResult(...)Note
If an
_idis explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails.Expand source code
@recast_method_sync def insert_one( self, document: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, max_time_ms: Optional[int] = None, ) -> InsertOneResult: """ Insert a single document in the collection in an atomic operation. Args: document: the dictionary expressing the document to insert. The `_id` field of the document can be left out, in which case it will be created automatically. vector: a vector (a list of numbers appropriate for the collection) for the document. Passing this parameter is equivalent to providing a `$vector` field within the document itself, however the two are mutually exclusive. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the document instead. vectorize: a string to be made into a vector, if such a service is configured for the collection. Passing this parameter is equivalent to providing a `$vectorize` field in the document itself, however the two are mutually exclusive. Moreover, this parameter cannot coexist with `vector`. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the document instead. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an InsertOneResult object. Examples: >>> my_coll.count_documents({}, upper_bound=10) 0 >>> my_coll.insert_one( ... { ... "age": 30, ... "name": "Smith", ... "food": ["pear", "peach"], ... "likes_fruit": True, ... }, ... ) InsertOneResult(raw_results=..., inserted_id='ed4587a4-...-...-...') >>> my_coll.insert_one({"_id": "user-123", "age": 50, "name": "Maccio"}) InsertOneResult(raw_results=..., inserted_id='user-123') >>> my_coll.count_documents({}, upper_bound=10) 2 >>> my_coll.insert_one({"tag": "v", "$vector": [10, 11]}) InsertOneResult(...) Note: If an `_id` is explicitly provided, which corresponds to a document that exists already in the collection, an error is raised and the insertion fails. """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="insert" ) _document = _collate_vector_to_document(document, vector, vectorize) _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"inserting one document in '{self.name}'") io_response = self._astra_db_collection.insert_one( _document, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished inserting one document in '{self.name}'") if "insertedIds" in io_response.get("status", {}): if io_response["status"]["insertedIds"]: inserted_id = io_response["status"]["insertedIds"][0] return InsertOneResult( raw_results=[io_response], inserted_id=inserted_id, ) else: raise DataAPIFaultyResponseException( text="Faulty response from insert_one API command.", raw_response=io_response, ) else: raise DataAPIFaultyResponseException( text="Faulty response from insert_one API command.", raw_response=io_response, ) def options(self, *, max_time_ms: Optional[int] = None) ‑> CollectionOptions-
Get the collection options, i.e. its configuration as read from the database.
The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application.
Args
max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
a CollectionOptions instance describing the collection. (See also the database
list_collectionsmethod.)Example
>>> my_coll.options() CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine'))Expand source code
def options(self, *, max_time_ms: Optional[int] = None) -> CollectionOptions: """ Get the collection options, i.e. its configuration as read from the database. The method issues a request to the Data API each time is invoked, without caching mechanisms: this ensures up-to-date information for usages such as real-time collection validation by the application. Args: max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: a CollectionOptions instance describing the collection. (See also the database `list_collections` method.) Example: >>> my_coll.options() CollectionOptions(vector=CollectionVectorOptions(dimension=3, metric='cosine')) """ logger.info(f"getting collections in search of '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms self_descriptors = [ coll_desc for coll_desc in self.database.list_collections(max_time_ms=_max_time_ms) if coll_desc.name == self.name ] logger.info(f"finished getting collections in search of '{self.name}'") if self_descriptors: return self_descriptors[0].options # type: ignore[no-any-return] else: raise CollectionNotFoundException( text=f"Collection {self.namespace}.{self.name} not found.", namespace=self.namespace, collection_name=self.name, ) def replace_one(self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Replace a single document on the collection with a new one, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
replacement- the new document to write into the collection.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True,
replacementis inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an UpdateResult object summarizing the outcome of the replace operation.
Example
>>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.find_one({"Buda": "Pest"}) {'_id': '8424905a-...', 'Buda': 'Pest'} >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'})Expand source code
@recast_method_sync def replace_one( self, filter: FilterType, replacement: DocumentType, *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Replace a single document on the collection with a new one, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. replacement: the new document to write into the collection. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, `replacement` is inserted as a new document if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the replace operation. Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.replace_one({"Marco": {"$exists": True}}, {"Buda": "Pest"}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.find_one({"Buda": "Pest"}) {'_id': '8424905a-...', 'Buda': 'Pest'} >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.replace_one({"Mirco": {"$exists": True}}, {"Oh": "yeah?"}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '931b47d6-...'}) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } logger.info(f"calling find_one_and_replace on '{self.name}'") _max_time_ms = max_time_ms or self.api_options.max_time_ms fo_response = self._astra_db_collection.find_one_and_replace( replacement=replacement, filter=filter, sort=_sort, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_replace on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_replace API command.", raw_response=fo_response, ) def set_caller(self, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> None-
Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller").
Args
caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Example
>>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0")Expand source code
def set_caller( self, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> None: """ Set a new identity for the application/framework on behalf of which the Data API calls are performed (the "caller"). Args: caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Example: >>> my_coll.set_caller(caller_name="the_caller", caller_version="0.1.0") """ logger.info(f"setting caller to {caller_name}/{caller_version}") self._astra_db_collection.set_caller( caller_name=caller_name, caller_version=caller_version, ) def to_async(self, *, database: Optional[AsyncDatabase] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> AsyncCollection-
Create an AsyncCollection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into an async object).
Args
database- an AsyncDatabase object, instantiated earlier. This represents the database the new collection belongs to.
name- the collection name. This parameter should match an existing collection on the database.
namespace- this is the namespace to which the collection belongs. If not specified, the database's working namespace is used.
embedding_api_key- optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e.
EmbeddingAPIKeyHeaderProvider). For some vectorize providers/models, if using header-based authentication, specialized subclasses ofEmbeddingHeadersProvidershould be supplied. collection_max_time_ms- a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
find,delete_many,insert_manyand so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Returns
the new copy, an AsyncCollection instance.
Example
>>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100)) 77Expand source code
def to_async( self, *, database: Optional[AsyncDatabase] = None, name: Optional[str] = None, namespace: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> AsyncCollection: """ Create an AsyncCollection from this one. Save for the arguments explicitly provided as overrides, everything else is kept identical to this collection in the copy (the database is converted into an async object). Args: database: an AsyncDatabase object, instantiated earlier. This represents the database the new collection belongs to. name: the collection name. This parameter should match an existing collection on the database. namespace: this is the namespace to which the collection belongs. If not specified, the database's working namespace is used. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: the new copy, an AsyncCollection instance. Example: >>> asyncio.run(my_coll.to_async().count_documents({},upper_bound=100)) 77 """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return AsyncCollection( database=database or self.database.to_async(), name=name or self.name, namespace=namespace or self.namespace, api_options=self.api_options.with_override(_api_options), caller_name=caller_name or self._astra_db_collection.caller_name, caller_version=caller_version or self._astra_db_collection.caller_version, ) def update_many(self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
upsert- this parameter controls the behavior in absence of matches.
If True, a single new document (resulting from applying
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence.
Returns
an UpdateResult object summarizing the outcome of the update operation.
Example
>>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) InsertManyResult(...) >>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}}) UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}) >>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'})Note
Similarly to the case of
find(see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of theupdate_manycan result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not.Expand source code
@recast_method_sync def update_many( self, filter: FilterType, update: Dict[str, Any], *, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Apply an update operations to all documents matching a condition, optionally inserting one documents in absence of matches. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the documents, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. upsert: this parameter controls the behavior in absence of matches. If True, a single new document (resulting from applying `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the operation. If not passed, the collection-level setting is used instead: if a large number of document updates is anticipated, it is suggested to specify a larger timeout than in most other operations as the update will span several HTTP calls to the API in sequence. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> my_coll.insert_many([{"c": "red"}, {"c": "green"}, {"c": "blue"}]) InsertManyResult(...) >>> my_coll.update_many({"c": {"$ne": "green"}}, {"$set": {"nongreen": True}}) UpdateResult(raw_results=..., update_info={'n': 2, 'updatedExisting': True, 'ok': 1.0, 'nModified': 2}) >>> my_coll.update_many({"c": "orange"}, {"$set": {"is_also_fruit": True}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_many( ... {"c": "orange"}, ... {"$set": {"is_also_fruit": True}}, ... upsert=True, ... ) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '46643050-...'}) Note: Similarly to the case of `find` (see its docstring for more details), running this command while, at the same time, another process is inserting new documents which match the filter of the `update_many` can result in an unpredictable fraction of these documents being updated. In other words, it cannot be easily predicted whether a given newly-inserted document will be picked up by the update_many command or not. """ api_options = { "upsert": upsert, } page_state_options: Dict[str, str] = {} um_responses: List[Dict[str, Any]] = [] um_statuses: List[Dict[str, Any]] = [] must_proceed = True _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"starting update_many on '{self.name}'") timeout_manager = MultiCallTimeoutManager(overall_max_time_ms=_max_time_ms) while must_proceed: options = {**api_options, **page_state_options} logger.info(f"calling update_many on '{self.name}'") this_um_response = self._astra_db_collection.update_many( update=update, filter=filter, options=options, timeout_info=timeout_manager.remaining_timeout_info(), ) logger.info(f"finished calling update_many on '{self.name}'") this_um_status = this_um_response.get("status") or {} # # if errors, quit early if this_um_response.get("errors", []): partial_update_info = _prepare_update_info(um_statuses) partial_result = UpdateResult( raw_results=um_responses, update_info=partial_update_info, ) all_um_responses = um_responses + [this_um_response] raise UpdateManyException.from_responses( commands=[None for _ in all_um_responses], raw_responses=all_um_responses, partial_result=partial_result, ) else: if "status" not in this_um_response: raise DataAPIFaultyResponseException( text="Faulty response from update_many API command.", raw_response=this_um_response, ) um_responses.append(this_um_response) um_statuses.append(this_um_status) next_page_state = this_um_status.get("nextPageState") if next_page_state is not None: must_proceed = True page_state_options = {"pageState": next_page_state} else: must_proceed = False page_state_options = {} update_info = _prepare_update_info(um_statuses) logger.info(f"finished update_many on '{self.name}'") return UpdateResult( raw_results=um_responses, update_info=update_info, ) def update_one(self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None) ‑> UpdateResult-
Update a single document on the collection as requested, optionally inserting a new one if no match is found.
Args
filter- a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators.
update- the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax.
vector- a suitable vector, i.e. a list of float numbers of the appropriate
dimensionality, to use vector search (i.e. ANN,
or "approximate nearest-neighbours" search), as the sorting criterion.
In this way, the matched document (if any) will be the one
that is most similar to the provided vector.
DEPRECATED (removal in 2.0). Use a
$vectorkey in the sort clause dict instead. vectorize- a string to be made into a vector to perform vector search.
Using vectorize assumes a suitable service is configured for the collection.
DEPRECATED (removal in 2.0). Use a
$vectorizekey in the sort clause dict instead. sort- with this dictionary parameter one can control the sorting
order of the documents matching the filter, effectively
determining what document will come first and hence be the
replaced one. See the
findmethod for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key insort. upsert- this parameter controls the behavior in absence of matches.
If True, a new document (resulting from applying the
updateto an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms- a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead.
Returns
an UpdateResult object summarizing the outcome of the update operation.
Example
>>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'})Expand source code
@recast_method_sync def update_one( self, filter: FilterType, update: Dict[str, Any], *, vector: Optional[VectorType] = None, vectorize: Optional[str] = None, sort: Optional[SortType] = None, upsert: bool = False, max_time_ms: Optional[int] = None, ) -> UpdateResult: """ Update a single document on the collection as requested, optionally inserting a new one if no match is found. Args: filter: a predicate expressed as a dictionary according to the Data API filter syntax. Examples are: {} {"name": "John"} {"price": {"$lt": 100}} {"$and": [{"name": "John"}, {"price": {"$lt": 100}}]} See the Data API documentation for the full set of operators. update: the update prescription to apply to the document, expressed as a dictionary as per Data API syntax. Examples are: {"$set": {"field": "value}} {"$inc": {"counter": 10}} {"$unset": {"field": ""}} See the Data API documentation for the full syntax. vector: a suitable vector, i.e. a list of float numbers of the appropriate dimensionality, to use vector search (i.e. ANN, or "approximate nearest-neighbours" search), as the sorting criterion. In this way, the matched document (if any) will be the one that is most similar to the provided vector. *DEPRECATED* (removal in 2.0). Use a `$vector` key in the sort clause dict instead. vectorize: a string to be made into a vector to perform vector search. Using vectorize assumes a suitable service is configured for the collection. *DEPRECATED* (removal in 2.0). Use a `$vectorize` key in the sort clause dict instead. sort: with this dictionary parameter one can control the sorting order of the documents matching the filter, effectively determining what document will come first and hence be the replaced one. See the `find` method for more on sorting. Vector-based ANN sorting is achieved by providing a "$vector" or a "$vectorize" key in `sort`. upsert: this parameter controls the behavior in absence of matches. If True, a new document (resulting from applying the `update` to an empty document) is inserted if no matches are found on the collection. If False, the operation silently does nothing in case of no matches. max_time_ms: a timeout, in milliseconds, for the underlying HTTP request. If not passed, the collection-level setting is used instead. Returns: an UpdateResult object summarizing the outcome of the update operation. Example: >>> my_coll.insert_one({"Marco": "Polo"}) InsertOneResult(...) >>> my_coll.update_one({"Marco": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': True, 'ok': 1.0, 'nModified': 1}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}) UpdateResult(raw_results=..., update_info={'n': 0, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0}) >>> my_coll.update_one({"Mirko": {"$exists": True}}, {"$inc": {"rank": 3}}, upsert=True) UpdateResult(raw_results=..., update_info={'n': 1, 'updatedExisting': False, 'ok': 1.0, 'nModified': 0, 'upserted': '2a45ff60-...'}) """ check_deprecated_vector_ize( vector=vector, vectors=None, vectorize=vectorize, kind="find" ) _sort = _collate_vector_to_sort(sort, vector, vectorize) options = { "upsert": upsert, } _max_time_ms = max_time_ms or self.api_options.max_time_ms logger.info(f"calling find_one_and_update on '{self.name}'") fo_response = self._astra_db_collection.find_one_and_update( update=update, sort=_sort, filter=filter, options=options, timeout_info=base_timeout_info(_max_time_ms), ) logger.info(f"finished calling find_one_and_update on '{self.name}'") if "document" in fo_response.get("data", {}): fo_status = fo_response.get("status") or {} _update_info = _prepare_update_info([fo_status]) return UpdateResult( raw_results=[fo_response], update_info=_update_info, ) else: raise DataAPIFaultyResponseException( text="Faulty response from find_one_and_update API command.", raw_response=fo_response, ) def with_options(self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None) ‑> Collection-
Create a clone of this collection with some changed attributes.
Args
name- the name of the collection. This parameter is useful to quickly spawn Collection instances each pointing to a different collection existing in the same namespace.
embedding_api_key- optional API key(s) for interacting with the collection.
If an embedding service is configured, and this parameter is not None,
each Data API call will include the necessary embedding-related headers
as specified by this parameter. If a string is passed, it translates
into the one "embedding api key" header
(i.e.
EmbeddingAPIKeyHeaderProvider). For some vectorize providers/models, if using header-based authentication, specialized subclasses ofEmbeddingHeadersProvidershould be supplied. collection_max_time_ms- a default timeout, in millisecond, for the duration of each
operation on the collection. Individual timeouts can be provided to
each collection method call and will take precedence, with this value
being an overall default.
Note that for some methods involving multiple API calls (such as
find,delete_many,insert_manyand so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name- name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent.
caller_version- version of the caller.
Returns
a new Collection instance.
Example
>>> my_other_coll = my_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... )Expand source code
def with_options( self, *, name: Optional[str] = None, embedding_api_key: Optional[Union[str, EmbeddingHeadersProvider]] = None, collection_max_time_ms: Optional[int] = None, caller_name: Optional[str] = None, caller_version: Optional[str] = None, ) -> Collection: """ Create a clone of this collection with some changed attributes. Args: name: the name of the collection. This parameter is useful to quickly spawn Collection instances each pointing to a different collection existing in the same namespace. embedding_api_key: optional API key(s) for interacting with the collection. If an embedding service is configured, and this parameter is not None, each Data API call will include the necessary embedding-related headers as specified by this parameter. If a string is passed, it translates into the one "embedding api key" header (i.e. `astrapy.authentication.EmbeddingAPIKeyHeaderProvider`). For some vectorize providers/models, if using header-based authentication, specialized subclasses of `astrapy.authentication.EmbeddingHeadersProvider` should be supplied. collection_max_time_ms: a default timeout, in millisecond, for the duration of each operation on the collection. Individual timeouts can be provided to each collection method call and will take precedence, with this value being an overall default. Note that for some methods involving multiple API calls (such as `find`, `delete_many`, `insert_many` and so on), it is strongly suggested to provide a specific timeout as the default one likely wouldn't make much sense. caller_name: name of the application, or framework, on behalf of which the Data API calls are performed. This ends up in the request user-agent. caller_version: version of the caller. Returns: a new Collection instance. Example: >>> my_other_coll = my_coll.with_options( ... name="the_other_coll", ... caller_name="caller_identity", ... ) """ _api_options = CollectionAPIOptions( embedding_api_key=coerce_embedding_headers_provider(embedding_api_key), max_time_ms=collection_max_time_ms, ) return self._copy( name=name, api_options=_api_options, caller_name=caller_name, caller_version=caller_version, )