Quickest way to production grade RAG UI.
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Updated
Mar 28, 2025 - TypeScript
Quickest way to production grade RAG UI.
Generate & Ship UI with minimal effort - Open Source Generative UI with natural language
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.
This repo is for advanced RAG systems, each branch will represent a project based on RAG.
Demo LLM (RAG pipeline) web app running locally using docker-compose. LLM and embedding models are consumed as services from OpenAI.
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
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.
RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.
Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq
Powerful framework for building applications with Large Language Models (LLMs), enabling seamless integration with memory, agents, and external data sources.
Chat-with-Your-Documents is an AI-powered document chatbot using RAG, FastAPI, and React.js for local PDF question answering.
A GenAI based search system that scans numerous fashion product descriptions to recommend suitable options based on user queries.
It's an AI chatbot based on RAG pipeline for answering queries related to Sitare University.
This is a production-ready applications using RAG-based Language Model.
WebScraperAI is a powerful tool that enables users to perform question-answering on website content using web scraping and retrieval-augmented generation (RAG) with LlamaIndex. It supports multiple LLMs, including OpenAI GPT-3.5, GPT-4, Gemini Pro, Gemini Ultra, and DeepSeek.
A Question-Answering chatbot built using RAG (Retrieval-Augmented Generation) with conversation memory. This project uses LangChain, various LLM options, and vector stores to create an intelligent chatbot that can answer questions about Jessup Cellars winery.
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.
Hybrid Search RAG Pipeline integrating BM25 and vector search techniques using LangChain
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