An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.
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Updated
Sep 20, 2024 - Jupyter Notebook
An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.
This project demonstrates how to generate synthetic test data for Retrieval Augmented Generation (RAG) using 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.
Optimizing a Retrieval-Augmented Generation (RAG) system on the CNN/Daily Mail dataset using LangChain, with performance benchmarking and analysis via 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.
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.
Multi-step Agentic Self-Corrective RAG with websearch
Generative AI RAG Chatbot for Electricity and Gas Company
Different approaches to evaluate RAG !!!
A Contract Q&A Retrieval-Augmented Generation (RAG) system with LangChain's advanced retrieval methods. Evaluation is done with RAGAs metrics.
SMAI Project. Made an abstractive qa RAG chatbot using Langchain and experimented with variety of vector stores and retrievers and evaluated them using Ragas
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