An LLM Chatbot that dynamically retrieves and processes resumes using RAG to perform resume screening.
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Updated
Dec 18, 2024 - Jupyter Notebook
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.
Hybrid enterprise AI agent built for ORRA. It utilizes a robust, RAGAS-evaluated retrieval pipeline and features a FastAPI API to allow seamless querying of ORRA documents and FAQs.
Production-ready RAG system for technical documentation with hybrid retrieval, cross-encoder reranking, and RAGAS evaluation metrics.
Forty hands-on recipes for production-grade retrieval-augmented generation, Nebius-first and provider-agnostic.
Full-stack RAG template: FastAPI + LangChain LCEL + Next.js 14. Multi-KB retrieval, hybrid search (dense+BM25/RRF), FlashRank reranking, SSE streaming, RAGAS evaluation & JWT auth. MySQL + Chroma + MinIO.
Multi-step Agentic Self-Corrective RAG with websearch
Production-grade RAG pipelines with evaluation baked in
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.
Optimizing a Retrieval-Augmented Generation (RAG) system on the CNN/Daily Mail dataset using LangChain, with performance benchmarking and analysis via RAGAS.
Enterprise RAG pipelines with native IRIS vector search. 6 production implementations with RAGAS evaluation, LangChain, AWS/Azure configs. No external VectorDB required.
12-week lab-driven curriculum: cloud/infra engineer → AI Agent/LLM engineer. Local-first MLX stack, measured engineering, every claim grounded in a runnable RESULTS.md.
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 focuses on developing a Retrieval-Augmented Generation (RAG) system tailored for Contract Q&A.
This project demonstrates how to generate synthetic test data for Retrieval Augmented Generation (RAG) using Ragas.
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