A project-based course repository for developing AI agents using LangChain v1+ and LangGraph: search agents, RAG systems, reflection agents, and code interpreters.

🧠 A production-grade, agentic RAG platform for portfolio intelligence, combining LangChain, Chroma/FAISS, Hugging Face embeddings, and Ollama with dynamic entity extraction, backend API tool-chaining, and a real-time interactive assistant across deploy-ready frontend, backend, and infrastructure stacks.

Hands-on LangChain and LangGraph study guide covering RAG, LangGraph workflows, multi-agent systems, and advanced agentic AI patterns, with HTML ebook chapters and runnable Python examples.

Advanced RAG + Raptor: A sophisticated document processing and retrieval system combining hierarchical document clustering with advanced query processing. Features HTML-to-markdown conversion, recursive document clustering, query expansion, cross-encoder re-ranking, and contextual response generation using LangChain, Vertex AI, PostgreSQL/pgvector,

No description available

🤖 Advanced Multi-Agent AI Template: Production-ready system combining Groq's speed with LangChain's flexibility. Features RAG, document processing, and real-time agent collaboration. Built with Streamlit for instant deployment. Ready to customize and scale! 🚀

A cutting-edge, enterprise-grade AI-powered Loan Origination System for business banking that uses LangChain, LangGraph, LangFuse, and RAG (Retrieval-Augmented Generation) to automate the complete loan underwriting process with industry-leading accuracy and real-time intelligence.

AI-powered document query system using LangChain, ChromaDB, and OpenAI for efficient RAG-based information retrieval.

Golang RAG chatbot for university admissions. Built with LangChain, pgvector, Neo4j & Gemini AI. Features semantic search, knowledge graphs, async processing & hexagonal architecture. Demonstrates high-performance AI in Go as alternative to Python RAG systems.

ContractAdvisorRAG: Development of a Q&A system with RAG using Langchain. This repository contains the code for building an AI-based legal assistance system for contracts. It includes retrieval and generation components, backend and frontend integration, as well as tools for evaluating and optimizing the RAG system using RAGAS.

Multi-Agent Automation System: A modular Python framework that automates web scraping, AI-powered use case generation, and Kaggle dataset retrieval using LangChain, RAG, and Gemini AI.

Retrieval-Augmented Generation (RAG) combines information retrieval with AI-generated responses to improve accuracy and contextual relevance. This project demonstrates the design and implementation of a RAG-based system using Node.js, Express, LangChain, and MySQL, optimized with caching, parallel processing, and AI-driven query handling.

This repository contains a Streamlit-based Document Question Answering System implementing the Retrieve-and-Generate (RAG) architecture, utilizing Streamlit for the UI, LangChain for text processing, and Google Generative AI for embeddings.

Building Private AI Memory with Ollama, LangChain & Chroma - 100% Local RAG System

AI-powered Question Answering system for the 1337 Coding School handbook using FastAPI, LangChain, Ollama, and RAG.

🚀 AI-powered financial market intelligence system using Google Gemini, LangChain, RAG & multi-agent orchestration. Real-time stock analysis, sentiment analysis, and ML-based predictions.

CrediTrust AI Complaint-Answering Chatbot: A Python-based RAG system using LangChain and ChromaDB that converts customer complaints into actionable insights. Ask plain-English queries, retrieve relevant complaints, and get concise, context-aware answers via a Gradio chatbot interface.

AI Research Assistant is a multi-agent system built with LangChain, LangGraph, and LangSmith. It lets users ask research questions and returns search results, summaries, and synthesized reports in a ChatGPT-like Streamlit UI. Easily extendable with real APIs or RAG.

The RAG Application (Gemini) is a Streamlit web application designed as a question-answering system. It retrieves information from uploaded PDFs using Google Generative AI and LangChain, allowing users to ask questions about the document's content and receive detailed, context-aware answers.

It combines document retrieval, dynamic entity extraction, and external API calls to generate context-aware responses using a Hugging Face language model via Ollama. The system runs entirely in Google Colab and is compatible with both Linux and Windows.

🛠️ Explore hands-on notebooks to master LLMs, RAG, LangChain, CrewAI, and multi-agent systems for effective AI learning and experimentation.

AI-powered rural healthcare system built with Django using an Agentic RAG architecture (LangGraph + LangChain). Combines local document retrieval with SerpAPI web search fallback. Includes chatbot, appointments, awareness campaigns, and document management.

Build AI-powered document search without OpenAI bills. This RAG system uses Ollama for local LLM inference and LangChain for intelligent retrieval. Free, private, and works offline. Your data never leaves your machine.

Intelligent Vehicle Inventory System A high-performance Retrieval Augmented Generation (RAG) application that revolutionizes car inventory management through advanced AI-powered natural language queries. Built with Express.js, MongoDB Atlas Vector Search, and LangChain.hange streams automatically update vector embeddings when inventory changes

🤖 Advanced RAG Document Chat System - Chat with your documents using AI! Upload PDF, DOCX, or TXT files and ask questions to get intelligent answers with source citations. Powered by LangChain, FLAN-T5, and FAISS. 100% free, no API keys required, runs locally. Perfect for research, education, business analysis, and legal review.

RAG-powered resume evaluation and mock interview system using LangChain. Upload your resume, get structured feedback, and practice technical interviews with AI-generated questions. Tech: Python | LangChain | ChromaDB

Building Services Automation Terminal - Intelligent Agentic AI RAG system for mechanical engineering workflows using Python, Cassandra NoSQL, VectorDB, and LangChain.

AI Engineer building production-grade ML systems | LLMs, RAG, ML Pipelines | Python, TensorFlow, LangChain | Open to opportunities

LangChain-based RAG system for querying US & EU AI regulations and Stanford Encyclopedia of Philosophy articles pertinent to AI ethics etc.

🌟 RAG System with Gemini and LangChain A production-ready Retrieval-Augmented Generation (RAG) system powered by Google's Gemini model and LangChain, featuring document upload, semantic search, and AI-powered question answering. Key Features 🔍 Document Processing: PDF ingestion with text chunking 🧠 Vector Embeddings: Google's Gemini embedding