Popular GitHub Repositories for Rag Pipeline

Quickest way to production grade RAG UI.

Build a RAG preprocessing pipeline

Production-ready Chainlit RAG application with Pinecone pipeline offering all Groq and OpenAI Models, to chat with your documents.

Search for a holiday and get destination advice from an LLM. Observability by Dynatrace.

Demo LLM (RAG pipeline) web app running locally using docker-compose. LLM and embedding models are consumed as services from OpenAI.

AI-driven prompt generation and evaluation system, designed to optimize the use of Language Models (LLMs) in various industries. The project consists of both frontend and backend components, facilitating prompt generation, automatic evaluation data generation, and prompt testing.

A GenAI based search system that scans numerous fashion product descriptions to recommend suitable options based on user queries.

Chat-with-Your-Documents is an AI-powered document chatbot using RAG, FastAPI, and React.js for local PDF question answering.

RAG & LLM with Pathway to Process your Financial Reports and Tables from both structured data (tables) & unstructured data (text/images) with Claude Sonet 3.5 using Google Drive Connector of Pathway using streamlit

This project implements document ingestion, embedding generation, and retrieval-augmented generation (RAG). If you are looking for a small project to understand the implementation of basic RAG then this project is good to go.

ML-Powered Research Assistant: A web application that processes PDF research documents, providing individual summaries, a comparative final summary, sentiment analysis, keyword extraction, and a RAG-powered chatbot for querying content. Built with a FastAPI backend and Next.js frontend, it leverages LLaMA and DistilBERT for NLP adv. capabilities.

This repository offers a hands-on guide to mastering Generative AI with Langchain and Huggingface. It covers key concepts, practical implementation, and deployment strategies to help AI enthusiasts, developers, and professionals build and optimize AI models efficiently