Popular GitHub Repositories for Rags

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

Trench — Open-Source Analytics Infrastructure. A single production-ready Docker image built on ClickHouse, Kafka, and Node.js for tracking events, page views. Easily build product analytics dashboards, LLM RAGs, observability platforms, or any other analytics product.

Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, LLMs, RAGs, LangChain, LangGraph, Fine-tuning Llama 3 & AI Agents (CrewAI)

MIT-licensed Framework for LLMs, RAGs, Chatbots testing. Configurable via YAML and integrable into CI pipelines for automated testing.

An AI Assistant to answer user queries based on your own knowledge base!

Langchain Models for RAGs and Agents

LambdaRAG is a Retrieval Augmented Generation Chat AI Demo. Please read the full RAGs to Riches blog series.

Collection of different types of RAGs (Query Translation Patterns)

A german workshop where you learn how to build RAGs with Langchain

This repo is build to facilitate the state-of-the-art RAGs approaches with it's use cases and detailed descriptions.

This repository contains the implementation of a Retrieval-Augmented Generation (RAG) agent using Large Language Models (LLMs). RAG agents combine the power of information retrieval with text generation, enabling applications such as intelligent question-answering systems, and more.

A full-stack chatbot application that uses RAGS to interact intelligently with users based on custom-loaded knowledgebases. It supports dynamic dataset loading for seamless updates. The chatbot’s language model is evaluated on relevance, accuracy, coherence, completeness, creativity, tone, and alignment with intent, ensuring high-quality chats

In this repo implemented LLMs on custom data, using the power of Langchain and RAG.

This Repository contains the real life use cases of Gen-Ai (Google's Gemini + Simple RAG) in Finance Domain. It helps in summarizing any articles , but will be useful for analyzing those lengthy and complicated financial articles .

The applications of frameworks like Langchain and APIs for creating AI agents and RAGs

Experimenting with different kinds of RAGs Systems

Cultivating linguistic forests from YC startup pitches using bio-inspired grammar trees to map pitch patterns.

Prompt-engineered RAGs for Open Domain Complex QA

Build RAG applications in Python with Gradient in minutes