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.

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

A Contract Q&A Retrieval-Augmented Generation (RAG) system with LangChain's advanced retrieval methods. Evaluation is done with RAGAs metrics.

Generative AI RAG Chatbot for Electricity and Gas Company

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

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

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.

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.

Different approaches to evaluate RAG !!!

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

Multi-step Agentic Self-Corrective RAG with websearch

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.

This repository implements the evaluation metrics from RAGAS to a RAG pipeline on langflow.

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

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.

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.

SMAI Project. Made an abstractive qa RAG chatbot using Langchain and experimented with variety of vector stores and retrievers and evaluated them using Ragas

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

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