BlogIQ by BlueBash

This project implements an advanced Retrieval Augmented Generation (RAG) workflow to enhance question-answering accuracy and reduce LLM hallucinations. It leverages LangGraph to create a stateful, multi-step process that includes document retrieval, relevance grading, and web search fallback. Maintained by junfanz1 on GitHub, where it has earned 22 stars from the community.

Advanced-RAG-LangGraph is a Streamlit-based web application that implements an advanced Retrieval-Augmented Generation (RAG) pipeline using LangGraph, ChromaDB, and Tavily to enable interactive document-based Q&A with enhanced retrieval and error-handling capabilities. Maintained by chitralputhran on GitHub, where it has earned 37 stars from the community.

An AI-powered chatbot that leverages RAG (Retrieval-Augmented Generation) to answer your questions based on the content of uploaded PDFs. Maintained by Balaji-R-05 on GitHub, where it has earned 42 stars from the community.