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.

Multi-step Agentic Self-Corrective RAG with websearch

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

From-scratch Python implementation of a RAG-based QA system, along with RAGAS evaluation

This Repo is Public Repo which Help to Find the AI response , it Checks the following thing,Provides quantitative evaluation on different metrics like factuality, coherence, and relevance to assess the quality of responses.

LLM AI chatbot using Advanced Retrieval Augmented Generation (RAG), Langchain, and Streamlit to answer questions about information contained in numerous files.

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

This repository implements Ragas Metrics on a RAG system with OpenLIT observability tool.