> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Astra DB integrations

> Integrate with Astra DB using LangChain Python.

> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless AI-ready database built on `Apache Cassandra®` and made conveniently available through an easy-to-use JSON API.

See a [tutorial provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/tutorials/chatbot.html).

## Installation and setup

Install the following Python package:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install "langchain-astradb>=0.6,<0.7"
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-astradb>=0.6,<0.7
  ```
</CodeGroup>

Create a database (if needed) and get the [connection secrets](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html#create-a-database-and-store-your-credentials).
Set the following variables:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
ASTRA_DB_API_ENDPOINT="API_ENDPOINT"
ASTRA_DB_APPLICATION_TOKEN="TOKEN"
```

## Vector store

A few typical initialization patterns are shown here:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_astradb import AstraDBVectorStore

vector_store = AstraDBVectorStore(
    embedding=my_embedding,
    collection_name="my_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)


from astrapy.info import VectorServiceOptions

vector_store_vectorize = AstraDBVectorStore(
    collection_name="my_vectorize_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
    collection_vector_service_options=VectorServiceOptions(
        provider="nvidia",
        model_name="NV-Embed-QA",
    ),
)


from astrapy.info import (
    CollectionLexicalOptions,
    CollectionRerankOptions,
    RerankServiceOptions,
    VectorServiceOptions,
)

vector_store_hybrid = AstraDBVectorStore(
    collection_name="my_hybrid_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
    collection_vector_service_options=VectorServiceOptions(
        provider="nvidia",
        model_name="NV-Embed-QA",
    ),
    collection_lexical=CollectionLexicalOptions(analyzer="standard"),
    collection_rerank=CollectionRerankOptions(
        service=RerankServiceOptions(
            provider="nvidia",
            model_name="nvidia/llama-3.2-nv-rerankqa-1b-v2",
        ),
    ),
)
```

Notable features of class `AstraDBVectorStore`:

* native async API;
* metadata filtering in search;
* MMR (maximum marginal relevance) search;
* server-side embedding computation (["vectorize"](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html) in Astra DB parlance);
* auto-detect its settings from an existing, pre-populated Astra DB collection;
* [hybrid search](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html#the-hybrid-search-process) (vector + BM25 and then a rerank step);
* support for non-Astra Data API (e.g. self-hosted [HCD](https://docs.datastax.com/en/hyper-converged-database/1.1/get-started/get-started-hcd.html) deployments);

Learn more in the [example notebook](/oss/python/integrations/vectorstores/astradb).

See the [example provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/integrations/langchain.html).

## LLM cache

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain.globals import set_llm_cache
from langchain_astradb import AstraDBCache

set_llm_cache(AstraDBCache(
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
))
```

## Semantic LLM cache

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain.globals import set_llm_cache
from langchain_astradb import AstraDBSemanticCache

set_llm_cache(AstraDBSemanticCache(
    embedding=my_embedding,
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
))
```

## Document loader

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_astradb import AstraDBLoader

loader = AstraDBLoader(
    collection_name="my_collection",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)
```

Learn more in the [example notebook](/oss/python/integrations/document_loaders/astradb).

## Self-querying retriever

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_astradb import AstraDBVectorStore
from langchain_classic.retrievers.self_query.base import SelfQueryRetriever

vector_store = AstraDBVectorStore(
    embedding=my_embedding,
    collection_name="my_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

retriever = SelfQueryRetriever.from_llm(
    my_llm,
    vector_store,
    document_content_description,
    metadata_field_info
)
```

## Store

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_astradb import AstraDBStore

store = AstraDBStore(
    collection_name="my_kv_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)
```

See the API Reference for the [AstraDBStore](https://reference.langchain.com/python/langchain-astradb/storage/AstraDBStore).

## Byte store

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_astradb import AstraDBByteStore

store = AstraDBByteStore(
    collection_name="my_kv_store",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)
```

See the API reference for the [AstraDBByteStore](https://reference.langchain.com/python/langchain-astradb/storage/AstraDBByteStore).

***

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