A full-stack application that allows users to analyze and chat about code repositories using Google's Generative AI and LangChain. The system clones GitHub repositories, processes the code files, and enables intelligent conversations about the codebase.

Chat-with-Your-Documents is an AI-powered document chatbot using RAG, FastAPI, and React.js for local PDF question answering.

Rag Qna

For Educational Use Only (But Really) RAG-LLM workflow for building a knowledge-base around a canvas system

Atomic is a full-stack AI-powered contextual chatbot application built with React, Node.js, and Google Gemini AI. The application features multiple AI personalities, real-time chat functionality, user authentication, and persistent memory using vector embeddings.

A RAG with node.js, neo4j vector db, ollama, langchain and openai

Pinecone To Store Vector Embedding And Embedded Search

Backend for a RAG-powered news chatbot providing real-time AI responses, semantic search, and news retrieval using Node.js, Socket.IO, PostgreSQL, Redis, and Qdrant.

A Retrieval-Augmented Generation (RAG)-based AI assistant tailored for a hospital. It provides accurate, context-aware answers to patient queries by combining the hospital's medical knowledge base with a large language model (LLM). Ideal for answering FAQs, drug information, and hospital-specific guidelines.

RAGPilot is an intelligent Retrieval-Augmented Generation (RAG) interface that lets users chat with their own documents and web content. Upload PDFs or ingest URLs, and RAGPilot retrieves the most relevant context using semantic search and generates accurate responses with an LLM.

AI-powered PDF chat system using LangChain, Pinecone & Google Gemini. Ask questions about any PDF document and get intelligent answers.

Convenience Docker Postgres images with pre-installed pgvector

An LLM powered chatbot that can answer questions based on your specific data

A comprehensive AI-powered market research platform that allows users to upload financial documents and analyze them using advanced AI technology. The platform consists of a Node.js backend with AI integration and a modern Next.js frontend.

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📄 Create a lightweight Node.js backend for Retrieval-Augmented Generation, enabling efficient document embedding, storage, and semantic search.