How to Build Durable Chat Memory for RAG Using ScyllaDB and LangChain
Learn how to use ScyllaDB Vector Search and persistent chat history with LangChain to build RAG chatbots that retain context across restarts and replicas.
The post demonstrates how to integrate ScyllaDB Vector Search into a LangChain project for Retrieval Augmented Generation (RAG) use cases, as well as using ScyllaDB as a durable conversation memory within LangChain. Large language models are stateless, so RAG alone doesn't retain user inputs. ScyllaDB offers high availability, predictable low latency, horizontal scalability, and built-in vector search.
ScyllaDB is integrated with LangChain by reusing the existing Cassandra connector. The setup involves installing dependencies, filling in environment variables with ScyllaDB credentials, and connecting to ScyllaDB Cloud. The chatbot ingests articles, answers questions using retrieved context, and preserves conversation history in the database using CassandraChatMessageHistory.
This implementation ensures chat history survival through process crashes, server restarts, and horizontal scaling.
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