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Astra DB

This page lists the integrations available with Astra DB and Apache Cassandra®.

Setup​

Install the following Python package:

pip install "astrapy>=0.5.3"

Astra DB​

DataStax Astra DB is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API.

Vector Store​

from langchain.vectorstores import AstraDB
vector_store = AstraDB(
  embedding=my_embedding,
  collection_name="my_store",
  api_endpoint="...",
  token="...",
)

Learn more in the example notebook.

LLM Cache​

from langchain.globals import set_llm_cache
from langchain.cache import AstraDBCache
set_llm_cache(AstraDBCache(
    api_endpoint="...",
    token="...",
))

Learn more in the example notebook (scroll to the Astra DB section).

Semantic LLM Cache​

from langchain.globals import set_llm_cache
from langchain.cache import AstraDBSemanticCache
set_llm_cache(AstraDBSemanticCache(
    embedding=my_embedding,
    api_endpoint="...",
    token="...",
))

Learn more in the example notebook (scroll to the appropriate section).

Chat message history​

from langchain.memory import AstraDBChatMessageHistory
message_history = AstraDBChatMessageHistory(
    session_id="test-session"
    api_endpoint="...",
    token="...",
)

Learn more in the example notebook.

Apache Cassandra and Astra DB through CQL​

Cassandra is a NoSQL, row-oriented, highly scalable and highly available database. Starting with version 5.0, the database ships with vector search capabilities. DataStax Astra DB through CQL is a managed serverless database built on Cassandra, offering the same interface and strengths.

These databases use the CQL protocol (Cassandra Query Language). Hence, a different set of connectors, outlined below, shall be used.

Vector Store​

from langchain.vectorstores import Cassandra
vector_store = Cassandra(
  embedding=my_embedding,
  table_name="my_store",
)

Learn more in the example notebook (scroll down to the CQL-specific section).

Memory​

from langchain.memory import CassandraChatMessageHistory
message_history = CassandraChatMessageHistory(session_id="my-session")

Learn more in the example notebook.

LLM Cache​

from langchain.cache import CassandraCache
langchain.llm_cache = CassandraCache()

Learn more in the example notebook (scroll to the Cassandra section).

Semantic LLM Cache​

from langchain.cache import CassandraSemanticCache
cassSemanticCache = CassandraSemanticCache(
  embedding=my_embedding,
  table_name="my_store",
)

Learn more in the example notebook (scroll to the appropriate section).