Successfully designed and developed a customer support chatbot that leverages LangChain and Pinecone for efficient retrieval-augmented generation (RAG), enabling intelligent and context-aware responses to user queries.

Classifies the presence of key exogenous variables in projects subject to federal NEPA (National Environmental Policy Act) review using NEPA environmental permitting reports (EISs) as input, and LangChain, Pinecone, and OpenAI API (GPT-4) to parse the reports efficiently with low costs ($0.40/report, on average) and high (>85%) correctness.

This repository provides the companion Jupyter Notebooks for the book Context-Engineering for Multi-Agent Systems. It technically details the progressive construction of a Context Engine moving from simple prompts to foundational concepts to a production-ready system semantic blueprint. The code is a guide to building Multi-Agent Systems (MAS)

A user-friendly RAG-powered fitness assistant — a conversational AI that understands your fitness goals, experience level, and equipment availability. It can help you select the perfect exercises, suggest alternative options, and keep you motivated to stay consistent with your routine, making fitness more accessible and personalised.

📚This repository includes a PDF question-and-answer chatbot that utilizes sophisticated natural language processing techniques. To ask about the document, users can submit it in PDF format and get quick responses through automatic extraction by the machine.