A multi-tenant website assistant API with RAG functionality and a frontend. For a more dynamic and useful website experience.

🧠 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.

The Agriculture Assistant is an LLM-powered system aiding farmers with agricultural advice. It has Frontend (React), Backend (Python), and RAG subsystems, supporting text/image/audio input, Mandarin audio output, and knowledge retrieval via vector databases.

A QA assistant combining keyword search and RAG with reranking. Deep optimization with multiple adjustable parameters and open-source model support (vllm). It uses LangGraph for workflow orchestration and LlamaIndex for retrieval. Openwebui is used for frontend.

AI-powered teaching assistant using RAG (Retrieval-Augmented Generation) with FastAPI backend and Streamlit frontend

A fully local Cybercrime SOP assistant for India, combining a FastAPI backend, React frontend, and Ollama-powered RAG system to provide guidance, resources, and reporting workflows without cloud dependencies.

Legal Assistant RAG web application with backend and frontend parts in C# and Angular

🤖 AI personal homepage with chat assistant. React frontend + Python Flask backend with RAG system for intelligent responses about your profile.

🚀 UnchainAI - Assistant IA conversationnel entièrement local avec modèles Ollama. 12 personas spécialisées (Web Designer, Python, Cybersécurité, Cloud). Génération de code, création de sites web, RAG avec Qdrant. Frontend Next.js + Backend FastAPI. Confidentialité totale - vos données restent sur votre machine. Déploiement Docker en un clic.

Frontend Angular 19 do assistente de vendas com IA. Interface de chat com memória de conversa e painel de upload de documentos para a base de conhecimento RAG.

No description available

Wikipedia AI agent research assistant. LangChain's LangGraph's ReAct agent architecture, LLMs (OpenAI, Anthropic, Google), Wikipedia API, RAG with FAISS vector db, semantic chunking, GraphRAG, Streamlit frontend, terminal and web interfaces

AI-powered legal research assistant using RAG architecture (FastAPI + Docker + Vector embeddings + React frontend).

Production-style RAG knowledge assistant with FastAPI, OpenAI, FAISS, PDF ingestion, React frontend, Docker, and AWS deployment.

A production-ready AI business assistant with RAG-powered document search, FastAPI backend, React frontend, PostgreSQL chat storage, and Dockerized deployment.

AI Codebase Assistant: FastAPI backend with React frontend using LLMs and RAG to fetch, chunk, and analyze GitHub repositories, providing contextual explanations across multiple file types.

RAG-powered AI assistant for web developers, grounded in the Baseline standard to generate truly reliable and modern frontend code. 🚀 A Baseline Tooling Hackathon project.

Chat RAG Assistant: Full‑stack AI chat with retrieval‑augmented generation. Backend: FastAPI, LangChain (OpenAI), Chroma, PostgreSQL. Frontend: React + TypeScript + Tailwind + shadcn/ui. Real‑time WebSocket chat

AI-powered Medical Assistant backend built with FastAPI. Includes RAG pipeline, embeddings, vector search, and fallback LLM logic (OpenAI → Groq). Designed for integration with a Next.js frontend.

A Retrieval-Augmented Generation (RAG) AI Assistant built with FastAPI backend and Streamlit frontend. This application allows users to upload documents (PDF, TXT, DOCX) and interact with an AI assistant that can answer questions based on the uploaded content using OpenAI's language models.

Travel assistant chatbot that leverages Retrieval-Augmented Generation (RAG) and IBM watsonx.ai to provide personalized travel recommendations and chat experiences. It features a web frontend and a WhatsApp/Twilio integration for conversational access.

An intelligent assistant that answers questions based on uploaded documents using RAG (Retrieval-Augmented Generation) powered by LangChain and LLaMA 3.2 Instruct 1B. The system features a sleek, interactive frontend built with Dash.

A powerful, retrieval-augmented generation (RAG) medical assistant using advanced question fusion, FAISS-based dense retrieval, and Gemini LLM for high-quality answers. Built with Flask backend and a modern HTML+JS frontend for easy interaction.

RAGQueryAI is an AI assistant using RAG with Gemini AI, ChromaDB, and SQLite for precise product queries. It features a Flask API, Express.js middleware, and a React.js frontend, combining SQL queries with vector retrieval for accuracy.

An end-to-end Medical Assistant powered by RAG + Agentic AI. It enables medical Q&A with citations, patient registration, appointment confirmations, medicine stock tracking, and case summarization. Frontend built with Streamlit, backend with FastAPI, SQLite/Supabase, and vectorDB FAISS.

A production-grade, AI-powered personal assistant that answers questions using your uploaded documents (PDFs, Markdown, JSON, etc.) with Retrieval-Augmented Generation (RAG). Built with Golang backend, Next.js frontend, Qdrant vector database, and AWS S3 storage. Fully Dockerized with LocalStack for local testing.

A full-stack AI-powered travel planning assistant that combines RAG (Retrieval-Augmented Generation), agentic workflows (n8n), and fine-tuned LLMs to help users plan trips with ease. The system features a responsive React frontend, a scalable API backend, and is fully containerized with Docker.

I developed an intelligent AI assistant that provides contextual and accurate responses to customer queries by leveraging product manuals, FAQs, and ticket history. The system uses RAG (Retrieval-Augmented Generation) with LangChain + ChromaDB, delivering a seamless user experience through a React.js frontend and a Flask backend.

OLIR Chatbot is a document-aware AI assistant built using Retrieval-Augmented Generation (RAG). Users can upload PDF documents and chat with an AI that responds based on the uploaded content. The system includes a modern React-based frontend and a powerful FastAPI backend, featuring semantic search, GPT-based reasoning, and persistent chat history.

Simplet frontend for Open RAG Assistant API Service