Medical RAG Chatbot (LangChain + Pinecone + Flask)
Pankaj Kumar Pramanik
Built CareSage, a Retrieval-Augmented Generation (RAG) medical chatbot that answers user questions using knowledge pulled from PDF documents instead of guessing. The app ingests PDFs with PyPDF, chunks and embeds content using sentence-transformers, stores vectors in Pinecone, and retrieves the most relevant passages at query time. A LangChain pipeline then combines the retrieved context with an OpenAI LLM to generate grounded, helpful responses.
The system is served through a lightweight Flask backend with a clean chat UI, environment-based configuration via python-dotenv, and modular components for document ingestion, indexing, retrieval, and response generation—making it easy to extend to new medical document sets or internal knowledge bases.
Tech stack: Flask, LangChain, Pinecone, OpenAI, sentence-transformers, PyPDF, Python.
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Posted Feb 10, 2025
Built a medical RAG chatbot: ingest PDFs, generate embeddings (sentence-transformers), retrieve via Pinecone, and answer with LangChain + OpenAI in Flask.
