{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Good Tech Companies ","title":"Build Durable Chat Memory for RAG Using ScyllaDB and LangChain","html":"","width":"100%","height":180,"duration":331,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/build-durable-chat-memory-for-rag-using-scylladb-and-langchain.\nLearn how to use ScyllaDB Vector Search and persistent chat history with LangChain to build RAG chatbots that retain context across restarts and replicas.\nCheck more stories related to undefined at: https://hackernoon.com/c/undefined.\n You can also check exclusive content about #scylladb-vector-search, #scalable-rag-architecture, #conversational-ai-persistence, #rag-chatbot-memory, #langchain-scylladb-integration, #scylladb-vector-database, #langchain-persistent-memory, #good-company, and more.\nThis story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page,\n and for more stories, please visit hackernoon.com.\nLangChain’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.","thumbnail_url":"https://img.transistorcdn.com/HZ9CRzf5js9DK86xzUVMWBRbXYwg4dA8xVXJGVzpL6Y/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTNl/MjgwMmI0ZmEzNThj/YmJiOWNiN2UyZmRm/MzY3My5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}