Python driver quickstart
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DataStax recommends using the Python client with Serverless (Vector) databases. Use the Python driver only if you are working with an existing application that previously used a CQL-based driver or if you plan to explicitly use CQL. |
Review the Connection methods comparison page to determine the option that best suits your use case.
This quickstart explains how to use the Python driver to connect to your database, load a set of vector embeddings, and perform a similarity search to find vectors that are close to the one in your query.
Prerequisites
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An active Astra account
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An active Serverless (Non-Vector) database
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A secure connect bundle downloaded from the Astra Portal
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Python 3.7 or later
Install the cassandra-driver package
Check your pip version and, if necessary, upgrade pip before you install the cassandra-driver package.
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Verify that pip is version 23.0 or higher:
pip --version -
Upgrade pip, if needed:
python -m pip install --upgrade pip -
Install the
cassandra-driverpackage:pip install cassandra-driver
Import libraries and connect to the database
Import the necessary libraries and establish a connection to your database.
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DataStax recommends this basic configuration for use cases that are not proofs of concept or for production use. For proofs of concept or production use, see Production configuration. |
import os
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
session = Cluster(
cloud={"secure_connect_bundle": os.environ["ASTRA_DB_SECURE_BUNDLE_PATH"]},
auth_provider=PlainTextAuthProvider("token", os.environ["ASTRA_DB_APPLICATION_TOKEN"]),
).connect()After you connect to the database, you can use the driver to perform operations on your database.
Create a table and vector-compatible Storage Attached Index (SAI)
Create a table named vector_test in your database with columns for an integer id, text, and a 5-dimensional vector.
Then, create a custom index on the vector column of this table using a storage-attached index with a dot product similarity function for efficient vector searches.
keyspace = "default_keyspace"
v_dimension = 5
session.execute((
"CREATE TABLE IF NOT EXISTS {keyspace}.vector_test (id INT PRIMARY KEY, "
"text TEXT, vector VECTOR<FLOAT,{v_dimension}>);"
).format(keyspace=keyspace, v_dimension=v_dimension))
session.execute((
"CREATE CUSTOM INDEX IF NOT EXISTS idx_vector_test "
"ON {keyspace}.vector_test "
"(vector) USING 'StorageAttachedIndex' WITH OPTIONS = "
"{{'similarity_function' : 'cosine'}};"
).format(keyspace=keyspace))Load data
Insert a few documents with embeddings into the collection.
text_blocks = [
(1, "Chat bot integrated sneakers that talk to you", [0.1, 0.15, 0.3, 0.12, 0.05]),
(2, "An AI quilt to help you sleep forever", [0.45, 0.09, 0.01, 0.2, 0.11]),
(3, "A deep learning display that controls your mood", [0.1, 0.05, 0.08, 0.3, 0.6]),
]
for block in text_blocks:
id, text, vector = block
session.execute(
f"INSERT INTO {keyspace}.vector_test (id, text, vector) VALUES (%s, %s, %s)",
(id, text, vector)
)Perform a similarity search
Find documents that are close to a specific vector embedding.
ann_query = (
f"SELECT id, text, similarity_cosine(vector, [0.15, 0.1, 0.1, 0.35, 0.55]) as sim FROM {keyspace}.vector_test "
"ORDER BY vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] LIMIT 2"
)
for row in session.execute(ann_query):
print(f"[{row.id}] \"{row.text}\" (sim: {row.sim:.4f})")Migrate the Python driver
If necessary, you can migrate an earlier DataStax Python driver to a version that can connect to your Astra DB database.
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In your existing DataStax Python driver code, modify the connection code to use the secure connect bundle credentials.
In the
cloud_configparameter, include the path to the secure connect bundle for your Astra DB database (secure-connect-DATABASE_NAME.zip).from cassandra.cluster import Cluster from cassandra.auth import PlainTextAuthProvider cloud_config= { 'secure_connect_bundle': '/*SECURE_CONNECT_BUNDLE_PATH*/secure-connect-*DATABASE_NAME*.zip' } auth_provider = PlainTextAuthProvider('clientId', 'clientSecret') cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider) session = cluster.connect() -
Run your Python script to connect to your database.
