Azure-AI-RAG-Architecture-React-FastAPI-and-Cosmos-DB-Vector-Store and LangChain-RAG-Pattern-with-React-FastAPI-and-Cosmos-DB-Vector-Store
These are **ecosystem siblings** — both demonstrate the same RAG architecture pattern on Azure using identical core components (React frontend, FastAPI backend, Cosmos DB vector store), with one example using LangChain as an abstraction layer while the other implements RAG more directly.
About Azure-AI-RAG-Architecture-React-FastAPI-and-Cosmos-DB-Vector-Store
jonathanscholtes/Azure-AI-RAG-Architecture-React-FastAPI-and-Cosmos-DB-Vector-Store
This project demonstrates deploying a secure, scalable Generative AI (GenAI) solution on Azure using a Retrieval-Augmented Generation (RAG) architecture and Azure best practices. Leveraging CosmosDB, Azure OpenAI, and a React + Python FastAPI framework, it ensures efficient data retrieval, security, and an intuitive user experience.
About LangChain-RAG-Pattern-with-React-FastAPI-and-Cosmos-DB-Vector-Store
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.
Scores updated daily from GitHub, PyPI, and npm data. How scores work