RAG Study Buddy Built a full-stack, AI-powered study assistant that allows users to upload PDF textbooks and documents, instantly transforming them into interactive learning environments.

The application leverages an advanced Retrieval-Augmented Generation (RAG) pipeline orchestrated with LangChain. Uploaded documents are parsed, chunked, and embedded into a Pinecone vector database. Upon querying, the system retrieves the most semantically relevant sections and passes them to OpenAI's API, ensuring that all answers are strictly grounded in the source text to drastically reduce hallucinations.

Beyond standard Q&A, the system automatically generates study flashcards and maps out document relationships using knowledge graph visualization.

Tech Stack: Python · FastAPI · Next.js · LangChain · Pinecone · OpenAI API

This project demonstrates my ability to build production-ready AI microservices, manage complex backend RAG logic (including semantic chunking and embedding strategies), and integrate them seamlessly with a modern web frontend.