Full-Stack GraphRAG App with DSPy, FastAPI, React & Neo4j

Description

Go beyond simple RAG. In this project breakdown, I show you how to build a production-grade, scalable LLM Memory API from scratch. This isn't just a concept—it's a complete project. I'll walk you through using the DSPy framework for faithful, controlled generation, FastAPI for a high-performance backend, Neo4j as a knowledge graph memory layer, and a React/ReactFlow frontend for real-time interaction and visualization. #DSPy #GraphRAG #FastAPI #Neo4j #React #LLM Key Technologies & Skills Showcased: - Python & FastAPI for scalable backends. - DSPy for programmatic LLM pipelines (ChainOfThought). - Neo4j and graphiti for knowledge graph storage and retrieval. - Multi-modal data ingestion (PDF, DOCX, Text). - React & ReactFlow for dynamic, real-time UI. - WebSocket for live data synchronization. - Singleton pattern for efficient resource management. - Pydantic for robust API data contracts. Github: https://github.com/dhruvmojila/Memory_Api.git