langchain_md_files/integrations/providers/astradb.mdx

# Astra DB

> [DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless 
> vector-capable 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:
```bash
pip install "langchain-astradb>=0.1.0"
```

Get the [connection secrets](https://docs.datastax.com/en/astra/astra-db-vector/get-started/quickstart.html).
Set up the following environment variables:

```python
ASTRA_DB_APPLICATION_TOKEN="TOKEN"
ASTRA_DB_API_ENDPOINT="API_ENDPOINT"
```

## Vector Store

```python
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,
)
```

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

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

## Chat message history

```python
from langchain_astradb import AstraDBChatMessageHistory

message_history = AstraDBChatMessageHistory(
    session_id="test-session",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)
```

See the [usage example](/docs/integrations/memory/astradb_chat_message_history#example).

## LLM Cache

```python
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,
))
```

Learn more in the [example notebook](/docs/integrations/llm_caching#astra-db-caches) (scroll to the Astra DB section).


## Semantic LLM Cache

```python
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,
))
```

Learn more in the [example notebook](/docs/integrations/llm_caching#astra-db-caches) (scroll to the appropriate section).

Learn more in the [example notebook](/docs/integrations/memory/astradb_chat_message_history).

## Document loader

```python
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](/docs/integrations/document_loaders/astradb).

## Self-querying retriever

```python
from langchain_astradb import AstraDBVectorStore
from langchain.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
)
```

Learn more in the [example notebook](/docs/integrations/retrievers/self_query/astradb).

## Store

```python
from langchain_astradb import AstraDBStore

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

Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbstore).

## Byte Store

```python
from langchain_astradb import AstraDBByteStore

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

Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbbytestore).