jonathanscholtes/LangChain-RAG-Pattern-with-React-FastAPI-and-Cosmos-DB-Vector-Store
Complete project (web, api, data) covering the implementation of the RAG (Retrieval Augmented Generation) pattern using Azure Cosmos DB for MongoDB vCore and LangChain. The RAG pattern combines leverages the new vector search capabilities for Azure Cosmos DB.
This project helps developers integrate Retrieval Augmented Generation (RAG) capabilities into their applications. It takes your existing data, stores it in a vector database, and uses it to generate contextually relevant, 'grounded' answers to user questions. This is for software developers looking to build AI-powered conversational interfaces or knowledge retrieval systems.
No commits in the last 6 months.
Use this if you are a developer seeking a working example to build a Q&A application that provides answers based on your private data, rather than relying solely on a large language model's general knowledge.
Not ideal if you are a non-technical user looking for a ready-to-use application, as this is a developer demo requiring setup and coding.
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Language
Python
License
MIT
Category
Last pushed
Mar 09, 2024
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