Adding Semantic Caching and Memory to Your RAG Application Using MongoDB and LangChain
Originally Published Mar 20, 2024
This guide outlines how to enhance Retrieval-Augmented Generation (RAG) applications with semantic caching and memory using MongoDB and LangChain. It explains integrating semantic caching to improve response efficiency and relevance by storing query results based on semantics. Additionally, it describes adding memory for maintaining conversation history, enabling context-aware interactions.
The tutorial includes steps for setting up MongoDB, implementing semantic caching, and incorporating these features into RAG applications with LangChain, leading to improved response times and enriched user interactions through efficient data retrieval and personalized experiences.
Authors: Richmond Alake, Apoorva Joshi