Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/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.
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LangChain’s default in-memory chat history disappears when a process restarts and cannot reliably share context across application replicas. This guide shows how to use ScyllaDB as both a vector store and a durable conversation-history backend. With ScyllaDB Vector Search, LangChain’s Cassandra integrations, and RunnableWithMessageHistory, developers can retrieve relevant documents, preserve conversations by session ID, and build RAG chatbots that maintain context across restarts and horizontally scaled deployments.