Popular GitHub Repositories for Retreival Augmented Generation

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

A new novel multi-modality (Vision) RAG architecture

My attempt at implementing retreival augmented generation on Ollama and other LLM services using chromadb and langchain while also providing an easy to understand, clean code for others since nobody else does

This project is an innovative coffee shop application designed to bring an engaging and personalized experience to coffee lovers. The app leverages AI-powered agents for chat-based interactions and integrates modern web and mobile development techniques to provide seamless ordering and delivery services.

The goal of this project is to develop a RAG system using Agent from LangGraph to improve the travelling experience of tourists.

This repository contains an end-to-end Retrieval-Augmented Generation (RAG) system that leverages Deepseek and Ollama to provide intelligent responses based on any pdf content.

? Toy Project - ????♂? A RAG chatbot for my resume | The ultimate job-hunting sidekick RAG chatbot that knows your resume better than you #Google #Gemini-API #retrieval-augmented-generation #streamlit

ChatBot for live scores of cricket matches.

A RAG based approach to building a chatbot, that uses llama3 at its core, and can enable users to chat with pdfs, by storing pdf data in a vectordb (Chroma) and retrieves using FAISS

Explore web scrapping and search engine for thesis search, combine with RAG

DeepSeek Chat RAG is a document-based Q&A system using Retrieval-Augmented Generation (RAG) and Groq’s LLM for efficient information retrieval.

The codes and resources of Google 5-Day Generative AI (Gen-AI) Bootcamp (March 31, 2025 – April 4, 2025) led by Google’s ML researchers and engineers. This bootcamp combines in-depth conceptual insights with hands-on coding exercises, equipping participants with the skills to confidently build Gen-AI projects.

This Streamlit application serves as a comprehensive AI-powered research tool featuring three main modes: Research Assistant (fetching information from arXiv papers and Wikipedia), Document Q&A (allowing users to upload PDFs and ask questions about them using RAG), and Web Search (retrieving and summarizing web content)

AI-powered PDF question-answering app using Retrieval Augmented Generation (RAG). Leverages OpenAI, Hugging Face, and Pinecone for NLP and semantic search. Built with Next.js.

News Summerization RAG system developed with Meta-Llama-3-8B-Instruct (LLM), ChromaDB (Vector Database), and the BART summarizer (Hugging Face).