Integrate LangChain with Astra DB Serverless

query_builder 15 min

LangChain can use Astra DB Serverless to store and retrieve vectors for ML applications.

Prerequisites

This guide requires the following:

Connect to the Serverless (Vector) database

  1. Import libraries and connect to the database.

    Create a .env file in the folder where you will create your Python script. Populate the file with the Astra DB application token and endpoint values from the Database Details section of your database’s Overview tab, and your OpenAI API key.

    .envplaintext
    ASTRA_DB_APPLICATION_TOKEN="TOKEN"
    ASTRA_DB_API_ENDPOINT="API_ENDPOINT"
    ASTRA_DB_KEYSPACE="default_keyspace" # A keyspace that exists in the database
    OPENAI_API_KEY="API_KEY"

    The database endpoint format is https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com.

  2. Import your dependencies. To avoid a namespace collision, don’t name the file langchain.py.

    integrate.pypython
    import os
    from langchain_astradb import AstraDBVectorStore
    from langchain_core.documents import Document
    from langchain_openai import OpenAIEmbeddings
    
    from datasets import load_dataset
    from dotenv import load_dotenv
  3. Load your environment variables.

    integrate.pypython
    load_dotenv()
    
    ASTRA_DB_APPLICATION_TOKEN = os.environ["ASTRA_DB_APPLICATION_TOKEN"]
    ASTRA_DB_API_ENDPOINT = os.environ["ASTRA_DB_API_ENDPOINT"]
    ASTRA_DB_KEYSPACE = os.environ.get("ASTRA_DB_KEYSPACE")
    OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

    If you’re using Microsoft Azure OpenAI, include these additional environment variables:

    plaintext
    OPENAI_API_TYPE="azure"
    OPENAI_API_VERSION="2023-05-15"
    OPENAI_API_BASE="https://RESOURCE_NAME.openai.azure.com"
    OPENAI_API_KEY="API_KEY"

Create embeddings from text

  1. Specify the embeddings model, database, and collection to use. If the collection does not exist, it is created automatically.

    integrate.pypython
    embedding = OpenAIEmbeddings()
    vstore = AstraDBVectorStore(
        collection_name="test",
        embedding=embedding,
        token=ASTRA_DB_APPLICATION_TOKEN,
        api_endpoint=ASTRA_DB_API_ENDPOINT,
        namespace=ASTRA_DB_KEYSPACE,
    )
  2. Load a small dataset of philosophical quotes with the Python dataset module.

    integrate.pypython
    philo_dataset = load_dataset("datastax/philosopher-quotes")["train"]
    print("An example entry:")
    print(philo_dataset[16])
  3. Process metadata and convert to LangChain documents.

    integrate.pypython
    docs = []
    for entry in philo_dataset:
        metadata = {"author": entry["author"]}
        if entry["tags"]:
            # Add metadata tags to the metadata dictionary
            for tag in entry["tags"].split(";"):
                metadata[tag] = "y"
        # Add a LangChain document with the quote and metadata tags
        doc = Document(page_content=entry["quote"], metadata=metadata)
        docs.append(doc)
  4. Compute embeddings for each document and store in the database.

    integrate.pypython
    inserted_ids = vstore.add_documents(docs)
    print(f"\nInserted {len(inserted_ids)} documents.")

Verify integration

Show quotes that are similar to a specific quote.

integrate.pypython
results = vstore.similarity_search("Our life is what we make of it", k=3)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")

Run the code

Run the code you defined earlier.

shell
python integrate.py

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