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Offline PDF chat with RAG using local LLMs and vector search

A production-oriented Retrieval-Augmented Generation (RAG) system for chatting with PDFs. It uses local LLMs via Ollama, ChromaDB for vector search, and LangChain to provide grounded answers from uploaded documents, fully offline. Aimed at users needing private, local document Q&A.

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1 engagement·1 source·Sat, Jul 11, 2026, 03:21 AM
Notable capabilities: PDF ingestion and chunking, vector search with ChromaDB, local embeddings via Ollama (nomic-embed-text), query rewriting using conversation history, cross-encoder re-ranking, conversational memory (last 5 turns). Tech stack: Streamlit, ChromaDB, Ollama, LangChain. Traction: 1 star on GitHub.

Entities

Ollama(tool)LangChain(tool)ChromaDB(tool)Streamlit(tool)nomic-embed-text(model)

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