Insert documents

Inserts multiple documents into a collection.

Documents are stored in collections. They represent a single row or record of data in Astra DB Serverless databases. For more information, see About collections with the Data API.

If the collection is vector-enabled, pregenerated vector embeddings can be included by using the reserved $vector field for each document. If the collection has vectorize enabled, vector embeddings can be automatically generated from text specified in the reserved $vectorize field for each document. You can later use the $vector or $vectorize field to perform a vector search or hybrid search.

If the collection has lexical enabled, use the reserved $lexical field to store the text to index for the lexical search component of hybrid search.

Alternatively, you can use the $hybrid shorthand to populate the $vectorize and $lexical fields.

Method signature

Python
TypeScript
Java
curl

The following method belongs to the astrapy.Collection class.

insert_many(
  documents: Iterable[Dict[str, Any]],
  *,
  ordered: bool,
  chunk_size: int,
  concurrency: int
  general_method_timeout_ms: int,
  request_timeout_ms: int,
  timeout_ms: int,
) -> CollectionInsertManyResult

The following method belongs to the Collection class.

async insertMany(
  documents: MaybeId<Schema>[],
  options?: {
    ordered?: boolean,
    concurrency?: number,
    chunkSize?: number,
    timeout?: number | TimeoutDescriptor,
  },
): CollectionInsertManyResult<Schema>

The following methods belong to the com.datastax.astra.client.Collection class.

CollectionInsertManyResult insertMany(
  List<? extends T> documents
)

CollectionInsertManyResult insertMany(
  List<? extends T> documents,
  CollectionInsertManyOptions options
)

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": DOCUMENTS_JSON_ARRAY,
    "options": {
      "ordered": BOOLEAN,
    }
  }
}'

Result

Python
TypeScript
Java
curl

Inserts the specified documents and returns a CollectionInsertManyResult object that includes the IDs of the inserted documents and details about the success of the operation.

The ID value depends on the ID type. For more information, see Document IDs.

Example response:

CollectionInsertManyResult(inserted_ids=[
    "3f557bef-fd53-47ea-957b-effd53c7eaec",
    101,
    "132ffr343"
], raw_results=...)

Inserts the specified documents and returns a promise that resolves to a CollectionInsertManyResult<Schema> object that includes the IDs of the inserted documents and the number of inserted documents.

The ID value depends on the ID type. For more information, see Document IDs.

Example response:

{
  insertedCount: 3,
  insertedIds: [
    '92b3c4f4-db44-4440-b4c4-f4db54e440b8',
    101,
    '132ffr343',
  ]
}

Inserts the specified documents and returns a wrapper (CollectionInsertManyResult) that includes the IDs of the inserted documents.

The ID value depends on the ID type. For more information, see Document IDs.

Inserts the specified documents and returns a JSON object that includes the IDs of the inserted documents.

The ID value depends on the ID type. For more information, see Document IDs.

Example response:

{
  "status": {
    "insertedIds": [
      "3f557bef-fd53-47ea-957b-effd53c7eaec",
      101,
      "132ffr343"
    ]
  }
}

Parameters

Python
TypeScript
Java
curl

Name Type Summary

Name	Type	Summary
`documents`	`Iterable[Dict[str, Any]]`	An iterable of dictionaries, with each dictionary describing a document to insert. In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields: `_id`: An optional unique identifier for the document. If `_id` is omitted, it is created automatically based on the ID type. For more information, see Document IDs. `$vector`: An optional array of numbers representing a vector embedding for vector search. The `$vector` field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections. `$vectorize`: An optional string to generate vector embeddings from for vector search. The `$vectorize` field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize. `$lexical`: An optional string to make the document searchable for the lexical search component of hybrid search. The `$lexical` field is only supported for collections that have lexical search enabled. `$hybrid`: An optional string that populates both `$vectorize` and `$lexical`. The `$hybrid` shorthand is only supported for collections that have vectorize and lexical search enabled. The document may not contain both a `$vector` and a `$vectorize` field. If the document uses `$hybrid`, it may not contain a root-level `$vectorize` or `$lexical` field.
`ordered`	`bool`	Optional. Whether the insertions must be processed sequentially. If `False`, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to `False` for faster performance. Default: `False`
`chunk_size`	`int`	Optional. The number of documents to include in a single API request. DataStax recommends leaving this parameter unspecified to use the system default. Maximum: `100` Default: `50`
`concurrency`	`int`	Optional. The maximum number of concurrent requests to the API at a given time. If `ordered` is `True`, then `concurrency` must be `1` or unspecified. Default: `20` if `ordered` is `False`. `1` if `ordered` is `True`.
`general_method_timeout_ms`	`int`	Optional. The maximum time, in milliseconds, that the whole operation, which may involve multiple HTTP requests, can take. Default: The default value for the collection. This default is 30 seconds unless you specified a different default when you initialized the `Collection` or `DataAPIClient` object. For more information, see Timeout options. This parameter is aliased as `timeout_ms` for convenience.
`request_timeout_ms`	`int`	Optional. The maximum time, in milliseconds, that the client should wait for each underlying HTTP request. Default: The default value for the collection. This default is 10 seconds unless you specified a different default when you initialized the `Collection` object. For more information, see Timeout options.

documents

Iterable[Dict[str, Any]]

An iterable of dictionaries, with each dictionary describing a document to insert.

In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields:

_id: An optional unique identifier for the document. If _id is omitted, it is created automatically based on the ID type. For more information, see Document IDs.
$vector: An optional array of numbers representing a vector embedding for vector search. The $vector field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections.
$vectorize: An optional string to generate vector embeddings from for vector search. The $vectorize field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize.
$lexical: An optional string to make the document searchable for the lexical search component of hybrid search. The $lexical field is only supported for collections that have lexical search enabled.
$hybrid: An optional string that populates both $vectorize and $lexical. The $hybrid shorthand is only supported for collections that have vectorize and lexical search enabled.

The document may not contain both a $vector and a $vectorize field.

If the document uses $hybrid, it may not contain a root-level $vectorize or $lexical field.

ordered

bool

Optional. Whether the insertions must be processed sequentially. If False, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to False for faster performance.

Default: False

chunk_size

int

Optional. The number of documents to include in a single API request. DataStax recommends leaving this parameter unspecified to use the system default.

Maximum: 100

Default: 50

concurrency

int

Optional. The maximum number of concurrent requests to the API at a given time.

If ordered is True, then concurrency must be 1 or unspecified.

Default: 20 if ordered is False. 1 if ordered is True.

general_method_timeout_ms

int

Optional. The maximum time, in milliseconds, that the whole operation, which may involve multiple HTTP requests, can take.

Default: The default value for the collection. This default is 30 seconds unless you specified a different default when you initialized the Collection or DataAPIClient object. For more information, see Timeout options.

This parameter is aliased as timeout_ms for convenience.

request_timeout_ms

int

Optional. The maximum time, in milliseconds, that the client should wait for each underlying HTTP request.

Default: The default value for the collection. This default is 10 seconds unless you specified a different default when you initialized the Collection object. For more information, see Timeout options.

Name Type Summary

Name	Type	Summary
`documents`	`MaybeId<Schema>[]`	An array of documents to insert. In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields: `_id`: An optional unique identifier for the document. If `_id` is omitted, it is created automatically based on the ID type. For more information, see Document IDs. `$vector`: An optional array of numbers representing a vector embedding for vector search. The `$vector` field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections. `$vectorize`: An optional string to generate vector embeddings from for vector search. The `$vectorize` field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize. `$lexical`: An optional string to make the document searchable for the lexical search component of hybrid search. The `$lexical` field is only supported for collections that have lexical search enabled. For more information, see Create a collection. `$hybrid`: An optional string that populates both `$vectorize` and `$lexical`. The `$hybrid` shorthand is only supported for collections that have vectorize and lexical search enabled. The document may not contain both a `$vector` and a `$vectorize` field. If the document uses `$hybrid`, it may not contain a root-level `$vectorize` or `$lexical` field.
`options`	`CollectionInsertManyOptions`	Optional. The options for this operation. See the `options` table for more details.

documents

MaybeId<Schema>[]

An array of documents to insert.

In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields:

_id: An optional unique identifier for the document. If _id is omitted, it is created automatically based on the ID type. For more information, see Document IDs.
$vector: An optional array of numbers representing a vector embedding for vector search. The $vector field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections.
$vectorize: An optional string to generate vector embeddings from for vector search. The $vectorize field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize.
$lexical: An optional string to make the document searchable for the lexical search component of hybrid search. The $lexical field is only supported for collections that have lexical search enabled. For more information, see Create a collection.
$hybrid: An optional string that populates both $vectorize and $lexical. The $hybrid shorthand is only supported for collections that have vectorize and lexical search enabled.

The document may not contain both a $vector and a $vectorize field.

If the document uses $hybrid, it may not contain a root-level $vectorize or $lexical field.

options

CollectionInsertManyOptions

Optional. The options for this operation. See the options table for more details.

Properties of `options`:
Name	Type	Summary
`ordered`	`boolean`	Optional. Whether the insertions must be processed sequentially. If `False`, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to `False` for faster performance.
`concurrency`	`number`	Optional. The maximum number of concurrent requests to the API at a given time. If `ordered` is `true`, then `concurrency` must be `1` or unspecified. Default: `8` if `ordered` is `false`. `1` if `ordered` is `true`.
`chunkSize`	`number`	Optional. The number of documents to include in a single API request. DataStax recommends leaving this parameter unspecified to use the system default. Maximum: `100` Default: `50`
`timeout`	`number` \| `TimeoutDescriptor`	Optional. The timeout(s) to apply to this method. You can specify `requestTimeoutMs` and `generalMethodTimeoutMs`. Details about the `timeout` parameter The `TimeoutDescriptor` object can contain these properties: `requestTimeoutMs` (`number`): The maximum time, in milliseconds, that the client should wait for each underlying HTTP request. Default: The default value for the collection. This default is 10 seconds unless you specified a different default when you initialized the `Collection` or `DataAPIClient` object. `generalMethodTimeoutMs` (`number`): The maximum time, in milliseconds, that the whole operation, which may involve multiple HTTP requests, can take. Since this method issues a single HTTP request, `generalMethodTimeoutMs` and `requestTimeoutMs` are equivalent. If you specify both, the minimum of the two will be used. Default: The default value for the collection. This default is 30 seconds unless you specified a different default when you initialized the `Collection` or `DataAPIClient` object. If you specify a number instead of a `TimeoutDescriptor` object, that number will be applied to `generalMethodTimeoutMs`.

Name Type Summary

Name	Type	Summary
`documents`	`List<? extends T>`	A list of objects describing the documents to insert. In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields: `_id`: An optional unique identifier for the document. If `_id` is omitted, it is created automatically based on the ID type. For more information, see Document IDs. `$vector`: An optional array of numbers representing a vector embedding for vector search. The `$vector` field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections. `$vectorize`: An optional string to generate vector embeddings from for vector search. The `$vectorize` field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize. `$lexical`: An optional string to make the document searchable for the lexical search component of hybrid search. The `$lexical` field is only supported for collections that have lexical search enabled. For more information, see Create a collection. `$hybrid`: An optional string that populates both `$vectorize` and `$lexical`. The `$hybrid` shorthand is only supported for collections that have vectorize and lexical search enabled. The document may not contain both a `$vector` and a `$vectorize` field. If the document uses `$hybrid`, it may not contain a root-level `$vectorize` or `$lexical` field.
`options`	`CollectionInsertManyOptions`	Optional. The options for this operation. See the methods of the `CollectionInsertManyOptions` class for more details.

documents

List<? extends T>

A list of objects describing the documents to insert.

In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields:

_id: An optional unique identifier for the document. If _id is omitted, it is created automatically based on the ID type. For more information, see Document IDs.
$vector: An optional array of numbers representing a vector embedding for vector search. The $vector field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections.
$vectorize: An optional string to generate vector embeddings from for vector search. The $vectorize field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize.
$lexical: An optional string to make the document searchable for the lexical search component of hybrid search. The $lexical field is only supported for collections that have lexical search enabled. For more information, see Create a collection.
$hybrid: An optional string that populates both $vectorize and $lexical. The $hybrid shorthand is only supported for collections that have vectorize and lexical search enabled.

The document may not contain both a $vector and a $vectorize field.

If the document uses $hybrid, it may not contain a root-level $vectorize or $lexical field.

options

CollectionInsertManyOptions

Optional. The options for this operation. See the methods of the CollectionInsertManyOptions class for more details.

Methods of the `CollectionInsertManyOptions` class:
Name	Type	Summary
`ordered()`	`boolean`	Optional. Whether the insertions must be processed sequentially. If `false`, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to `false` for faster performance.
`concurrency()`	`int`	Optional. The maximum number of concurrent requests to the API at a given time. If `ordered` is `true`, then `concurrency` must be `1` or unspecified. Default: `1`.
`chunkSize()`	`int`	Optional. The number of documents to include in a single API request. DataStax recommends leaving this parameter unspecified to use the system default. Maximum: `100` Default: `50`
`timeout`	`int`	Optional. The maximum time, in milliseconds, that the client should wait for each underlying HTTP request. Default: The default value for the collection. This default is 30 seconds unless you specified a different default when you initialized the `Collection` or `DataAPIClient` object.

Use the insertMany command with these parameters:

Name Type Summary

Name	Type	Summary
`documents`	`array`	An array of JSON objects describing the documents to insert. In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields: `_id`: An optional unique identifier for the document. If `_id` is omitted, it is created automatically based on the ID type. For more information, see Document IDs. `$vector`: An optional array of numbers representing a vector embedding for vector search. The `$vector` field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections. `$vectorize`: An optional string to generate vector embeddings from for vector search. The `$vectorize` field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize. `$lexical`: An optional string to make the document searchable for the lexical search component of hybrid search. The `$lexical` field is only supported for collections that have lexical search enabled. For more information, see Create a collection. `$hybrid`: An optional string that populates both `$vectorize` and `$lexical`. The `$hybrid` shorthand is only supported for collections that have vectorize and lexical search enabled. The document may not contain both a `$vector` and a `$vectorize` field. If the document uses `$hybrid`, it may not contain a root-level `$vectorize` or `$lexical` field.
`options`	`object`	Optional. The options for this operation. See the `options` table for more details.

documents

array

An array of JSON objects describing the documents to insert.

In addition to open-ended fields that you can specify for each document, you may also specify the following reserved fields:

_id: An optional unique identifier for the document. If _id is omitted, it is created automatically based on the ID type. For more information, see Document IDs.
$vector: An optional array of numbers representing a vector embedding for vector search. The $vector field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections.
$vectorize: An optional string to generate vector embeddings from for vector search. The $vectorize field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize.
$lexical: An optional string to make the document searchable for the lexical search component of hybrid search. The $lexical field is only supported for collections that have lexical search enabled. For more information, see Create a collection.
$hybrid: An optional string that populates both $vectorize and $lexical. The $hybrid shorthand is only supported for collections that have vectorize and lexical search enabled.

The document may not contain both a $vector and a $vectorize field.

If the document uses $hybrid, it may not contain a root-level $vectorize or $lexical field.

options

object

Optional. The options for this operation. See the options table for more details.

Properties of `options`:
Name	Type	Summary
`ordered`	`boolean`	Optional. Whether the insertions must be processed sequentially. If `false`, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to `false` for faster performance. Default: `false`

Examples

The following examples demonstrate how to insert multiple documents into a collection.

Insert documents

The documents can have different structures.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient

# Get an existing collection
client = DataAPIClient()
database = client.get_database(
  "ASTRA_DB_API_ENDPOINT",
  token="ASTRA_DB_APPLICATION_TOKEN",
)
collection = database.get_collection("COLLECTION_NAME")

# Insert documents into the collection
result = collection.insert_many([
  {
    "name": "Jane Doe",
    "age": 42,
  },
  {
    "nickname": "Bobby",
    "color": "blue",
    "foods": ["carrots", "chocolate"],
  },
])

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany([
      {
        name: 'Jane Doe',
        age: 42,
      },
      {
        nickname: "Bobby",
        color: "blue",
        foods: ["carrots", "chocolate"],
      }
    ]);
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

package com.examples;

import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.collections.commands.results.CollectionInsertManyResult;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

        // Insert documents to the collection
        Document document1 = new Document()
            .append("name", "Jane Doe")
            .append("age", 42);
        Document document2 = new Document()
            .append("nickname", "Bobby")
            .append("color", "blue")
            .append("foods", Arrays.asList("carrots", "chocolate"));
        CollectionInsertManyResult result = collection.insertMany(List.of(document1, document2));
        System.out.println("IDs inserted: " + result.getInsertedIds());
    }
}

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42
      },
      {
        "nickname": "Bobby",
        "color": "blue",
        "foods": ["carrots", "chocolate"]
      }
    ]
  }
}'

Insert documents with vector embeddings

Use the reserved $vector field to insert documents with pregenerated vector embeddings.

The $vector field is only supported for vector-enabled collections. For more information, see $vector and $vectorize in collections.

You may also insert a mix of documents with and without the $vector field.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient
from astrapy.data_types import DataAPIVector


# Get an existing collection
client = DataAPIClient()
database = client.get_database(
  "ASTRA_DB_API_ENDPOINT",
  token="ASTRA_DB_APPLICATION_TOKEN",
)
collection = database.get_collection("COLLECTION_NAME")

# Insert documents to the collection
# The following also demonstrates use of both plain lists and DataAPIVector
result = collection.insert_many([
  {
    "name": "Jane Doe",
    "age": 42,
    "$vector": [.45, .32, .31]
  },
  {
    "nickname": "Bobby",
    "$vector": DataAPIVector([.08, .68, .30]),
  },
])

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany([
      {
        name: 'Jane Doe',
        age: 42,
        $vector: [.45, .32, .31],
      },
      {
        nickname: "Bobby",
        $vector: [.08, .68, .30],
      }
    ]);
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

package com.examples;

import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.core.vector.DataAPIVector;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.collections.commands.results.CollectionInsertManyResult;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

        // Insert documents to the collection
        Document document1 = new Document()
            .append("name", "Jane Doe")
            .append("age", 42)
            .append("$vector", new DataAPIVector(new float[] {0.45f, 0.32f, 0.41f}));
        Document document2 = new Document()
            .append("nickname", "Bobby")
            .append("$vector", new DataAPIVector(new float[] {0.08f, 0.68f, 0.3f}));
        CollectionInsertManyResult result = collection.insertMany(List.of(document1, document2));
        System.out.println("IDs inserted: " + result.getInsertedIds());
    }
}

You can provide the vector embeddings as an array of floats, or you can use $binary to provide the vector embeddings as a Base64-encoded string. $binary can be more performant.

Array of floats
$binary

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vector": [.12, .52, .32]
      },
      {
        "nickname": "Bobby",
        "$vector": [0.3, 0.6, 0.5]
      }
    ]
  }
}'

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vector": {"$binary": "PfXCjz8FHrg+o9cK"}
      },
      {
        "nickname": "Bobby",
        "$vector": {"$binary": "PpmZmj8ZmZo/AAAA"}
      }
    ]
  }
}'

Insert documents and generate vector embeddings

Use the reserved $vectorize field to generate a vector embedding automatically. The value of $vectorize can be any string.

The $vectorize field is only supported for collections that have vectorize enabled. For more information, see $vector and $vectorize in collections and Auto-generate embeddings with vectorize.

You may also insert a mix of documents with and without the $vectorize field.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient


# Get an existing collection
client = DataAPIClient()
database = client.get_database(
  "ASTRA_DB_API_ENDPOINT",
  token="ASTRA_DB_APPLICATION_TOKEN",
)
collection = database.get_collection("COLLECTION_NAME")

# Insert documents into the collection
result = collection.insert_many([
  {
    "name": "Jane Doe",
    "age": 42,
    "$vectorize": "Text to vectorize for this document",
  },
  {
    "nickname": "Bobby",
    "$vectorize": "Text to vectorize for this document",
  },
])

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany([
      {
        name: 'Jane Doe',
        age: 42,
        $vectorize: "Text to vectorize for this document",
      },
      {
        nickname: "Bobby",
        $vectorize: "Text to vectorize for this document",
      }
    ]);
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

package com.examples;

import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.collections.commands.results.CollectionInsertManyResult;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

        // Insert documents into the collection
        Document document1 = new Document()
            .append("name", "Jane Doe")
            .append("age", 42)
            .append("$vectorize", "Text to vectorize for this document");
        Document document2 = new Document()
            .append("nickname", "Bobby")
            .append("$vectorize", "Text to vectorize for this document");
        CollectionInsertManyResult result = collection.insertMany(List.of(document1, document2));
        System.out.println("IDs inserted: " + result.getInsertedIds());
    }
}

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vectorize": "Text to vectorize for this document"
      },
      {
        "nickname": "Bobby",
        "$vectorize": "Text to vectorize for this document"
      }
    ]
  }
}'

Insert documents for retrieval with hybrid search

Hybrid search, lexical search, and reranking are currently in public preview. Development is ongoing, and the features and functionality are subject to change. Astra DB Serverless, and the use of such, is subject to the DataStax Preview Terms.

If you plan to use hybrid search to find documents, each document must have both the $lexical field and the $vector field populated.

Python
TypeScript
Java
curl

from astrapy import DataAPIClient


# Get an existing collection
client = DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
database = client.get_database("ASTRA_DB_API_ENDPOINT")
collection = database.get_collection("COLLECTION_NAME")

# Insert documents
result = collection.insert_many([
    {
      "name": "Jane Doe",
      "$vector": [.08, .68, .30],
      "$lexical": "Text for lexical search",
    },
    {
      "name": "Mary Day",
      "$vectorize": "Text for vector search",
      "$lexical": "Text for lexical search",
    },
    {
      "name": "Bobby",
      "$hybrid": "Common text for both vectorize and lexical search",
    },
])

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany([
      {
        name: 'Jane Doe',
        $vector: [.08, .68, .30],
        $lexical: "Text for lexical search",
      },
      {
        name: 'Mary Day',
        $vectorize: "Text for vector search",
        $lexical: "Text for lexical search",
      },
      {
        name: 'Bobby',
        $hybrid: "Common text for both vectorize and lexical search",
      }
    ]);
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.core.hybrid.Hybrid;
import com.datastax.astra.client.core.vector.DataAPIVector;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

    Document document1 = new Document().append("name", "John Doe")
      .append("$vector", new DataAPIVector(new float[] {0.45f, 0.32f, 0.41f}))
      .append("$lexical", "Text for lexical search");
    Document document2 = new Document().append("name", "Mary Day")
      .append("$vectorize", "Text for vector search")
      .append("$lexical", "Text for lexical search");
    Document document3 = new Document().append("name", "'Bobby'")
      .append("$hybrid", "Common text for both vectorize and lexical search");

    collection.insertMany(document1, document2, document3);
  }
}

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "$vector": [.08, .68, .30],
        "$lexical": "Text for lexical search"
      },
      {
        "name": "Mary Day",
        "$vectorize": "Text for vector search",
        "$lexical": "Text for lexical search"
      },
      {
        "name": "Bobby",
        "$hybrid": "Common text for both vectorize and lexical search"
      }
    ]
  }
}'

Insert documents and specify the IDs

Python
TypeScript
Java
curl

from astrapy import DataAPIClient


# Get an existing collection
client = DataAPIClient()
database = client.get_database(
  "ASTRA_DB_API_ENDPOINT",
  token="ASTRA_DB_APPLICATION_TOKEN",
)
collection = database.get_collection("COLLECTION_NAME")

# Insert documents into the collection
result = collection.insert_many([
  {
    "name": "Jane Doe",
    "_id": 1,
  },
  {
    "nickname": "Bobby",
    "_id": "b_023",
  },
])

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany([
      {
        name: 'Jane Doe',
        _id: 1,
      },
      {
        nickname: "Bobby",
        _id: '23'
      }
    ]);
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

package com.examples;

import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.collections.commands.results.CollectionInsertManyResult;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

        // Insert documents to the collection
        Document document1 = new Document(1)
            .append("name", "Jane Doe");
        Document document2 = new Document("23")
            .append("nickname", "Bobby");
        CollectionInsertManyResult result = collection.insertMany(List.of(document1, document2));
        System.out.println("IDs inserted: " + result.getInsertedIds());
    }
}

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "_id": 1
      },
      {
        "nickname": "Bobby",
        "_id": "23"
      }
    ]
  }
}'

Insert documents and specify insertion behavior

Python
TypeScript
Java
curl

from astrapy import DataAPIClient

# Get an existing collection
client = DataAPIClient()
database = client.get_database(
  "ASTRA_DB_API_ENDPOINT",
  token="ASTRA_DB_APPLICATION_TOKEN",
)
collection = database.get_collection("COLLECTION_NAME")

# Insert documents into the collection
result = collection.insert_many(
  [
    {
      "name": "Jane Doe",
      "age": 42,
    },
    {
      "nickname": "Bobby",
      "color": "blue",
      "foods": ["carrots", "chocolate"],
    },
  ],
  chunk_size=2,
  concurrency=2,
  ordered=False,
  general_method_timeout_ms=1000,
)

import { DataAPIClient, CollectionInsertManyError } from '@datastax/astra-db-ts';

// Get an existing collection
const client = new DataAPIClient('ASTRA_DB_APPLICATION_TOKEN');
const database = client.db('ASTRA_DB_API_ENDPOINT');
const collection = database.collection('COLLECTION_NAME');

// Insert documents into the collection
(async function () {
  try {
    const result = await collection.insertMany(
      [
        {
          name: 'Jane Doe',
          age: 42,
        },
        {
          nickname: "Bobby",
          color: "blue",
          foods: ["carrots", "chocolate"],
        }
      ],
      {
        chunkSize: 2,
        concurrency: 2,
        ordered: false,
      }
    );
  } catch (error) {
    if (error instanceof CollectionInsertManyError) {
      console.log(error.insertedIds());
    }
  }
})();

package com.examples;

import com.datastax.astra.client.collections.Collection;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.collections.definition.documents.Document;
import com.datastax.astra.client.collections.commands.options.CollectionInsertManyOptions;
import com.datastax.astra.client.collections.commands.results.CollectionInsertManyResult;

public class InsertMany {

    public static void main(String[] args) {
        // Get an existing collection
        Collection<Document> collection = new DataAPIClient("ASTRA_DB_APPLICATION_TOKEN")
            .getDatabase("ASTRA_DB_API_ENDPOINT")
            .getCollection("COLLECTION_NAME");

        // Define the insertion options
        CollectionInsertManyOptions options = new CollectionInsertManyOptions()
            .chunkSize(20)
            .concurrency(3)
            .ordered(false)
            .timeout(1000);

        // Insert documents into the collection
        Document document1 = new Document()
            .append("name", "Jane Doe")
            .append("age", 42);
        Document document2 = new Document()
            .append("nickname", "Bobby")
            .append("color", "blue")
            .append("foods", Arrays.asList("carrots", "chocolate"));
        CollectionInsertManyResult result = collection.insertMany(List.of(document1, document2), options);
        System.out.println("IDs inserted: " + result.getInsertedIds());
    }
}

curl -sS -L -X POST "ASTRA_DB_API_ENDPOINT/api/json/v1/ASTRA_DB_KEYSPACE/COLLECTION_NAME" \
--header "Token: ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42
      },
      {
        "nickname": "Bobby",
        "color": "blue",
        "foods": ["carrots", "chocolate"]
      }
    ],
    "options": {
      "ordered": false
    }
  }
}'

Client reference

Python
TypeScript
Java
curl

For more information, see the client reference.

Client reference documentation is not applicable for HTTP.

Insert documents

Method signature

Result

Parameters

Examples

Insert documents

Insert documents with vector embeddings

Insert documents and generate vector embeddings

Insert documents for retrieval with hybrid search

Insert documents and specify the IDs

Insert documents and specify insertion behavior

Client reference

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