An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.

An intelligent customer support system powered by LangGraph and LangChain that uses Retrieval-Augmented Generation (RAG) to provide accurate, context-aware responses to customer queries. Built with FastAPI, FAISS, and multi-stage validation for production-ready deployment.

Multi-step Agentic Self-Corrective RAG with websearch

RAG Evaluation Pipeline with RAGAS

A tutorial on how to build Summary Brief from Evaluation Report - Offline+Open Source

Optimizing a Retrieval-Augmented Generation (RAG) system on the CNN/Daily Mail dataset using LangChain, with performance benchmarking and analysis via RAGAS.

This project(RAG) focuses on operationalizing LLMs by integrating OpenAI, MLflow, FastAPI, and RAGAS for evaluation. It allows users to deploy and manage LLMs, track model runs, and log evaluation metrics in MLflow. The project also features MLflow traces that logs all the user inputs ,responses ,retrieved contexts ,and other essential metrices.

This project aims to develop an enterprise-grade Retrieval-Augmented Generation (RAG) system by automating the prompt engineering process. The goal is to create a comprehensive solution that simplifies the task of crafting effective prompts for Language Models (LLMs), enabling businesses to leverage advanced AI capabilities more efficiently.

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.

This project demonstrates how to generate synthetic test data for Retrieval Augmented Generation (RAG) using Ragas.

This project focuses on developing a Retrieval-Augmented Generation (RAG) system tailored for Contract Q&A.

This project focuses on Automatic Prompt Engineering (APE) for Retrieval-Augmented Generation (RAG) systems.

Создание RAG-системы для поиска по ГОСТам (pdf)

The objective is to build, evaluate, and improve a Retrieval-Augmented Generation (RAG) system for Contract Q&A, simulating interaction with a contract by asking questions and getting precise answers.

检索生成系统评价模型 - 检索内容排序+生成标准答案+RAGAs评分

Trabalho de Conclusão de Curso do MBA em Data Science e Analytics da USP/ESALQ, turma 2023. Desenvolve um sistema de recuperação da informação baseado em LLMs e RAG, aplicado à lista RENAME de medicamentos essenciais. O protótipo utiliza embeddings, bancos vetoriais e LangChain, com avaliação realizada pelo framework RAGAS.

Generative AI RAG Chatbot for Electricity and Gas Company

RAG Pipeline Evaluation and monitoring on AWS using RAGAS

A RAG system for Contract Q&A that enables chatting with a contract and asking questions about the contract. It has an interface build with React and FastAPI in backend integrating rag-pipeline with Autogen agents and websockets for communication. Evaluation of the RAG is done using RAGAS.

A LangChain-based Retrieval-Augmented Generation (RAG) chatbot for medical data. Integrates with Gemini/Grok AI to deliver accurate, context-aware answers in healthcare and biomedical domains.