kriti524/LRAG-Chatbot
A local Retrieval-Augmented Generation (RAG) chatbot built with LangChain, Ollama, ChromaDB, Flask, and Streamlit. Supports document ingestion, semantic search, conversational memory, and source-aware AI responses using local LLMs.
SUMMARY AI summary by gpt-5-mini
Local RAG Chatbot is a self-hosted Retrieval-Augmented Generation assistant that lets you ingest local text documents and chat with them using a local LLM. It’s aimed at developers, researchers, or privacy-conscious users who need offline RAG workflows. Key features: document ingestion from docs/, chunking and embedding-based semantic search, conversational memory, source-aware answers, persistent vector store with ChromaDB, and local LLM inference via Ollama. The stack uses LangChain for RAG orchestration, Flask for the API backend, and Streamlit for the UI. Typical flow: ingest → chunk → embeddings → ChromaDB → retriever → LangChain RAG chain → Ollama LLM → response + sources. Requires Ollama running and .txt docs; run ingestion before querying.
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Owner
B.Tech Data Science and Engineering Python, Data science, Machine learning, LLM's
Dates
| Created on GitHub | 2026-05-11 |
| Last push | 2026-05-11 |
| First seen here | 2026-05-11 |
| Last fetched | 2026-05-11 16:13 |
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