#retrieval-augmented-generation

企业级 AI Agent 平台 | Spring Boot 3 + LangChain4j | ReAct 推理 + 多路召回 RAG + 语义缓存 + 多智能体协作

ESG knowledge graph + RAG agent. Turn long-form sustainability disclosures into cited answers, extracted entities, and a reviewable graph of evidence.

Hands-on Generative AI: 15 RAG modules from document loaders to guardrails, plus Papeer — a research paper assistant with citations and claim verification. Also LangGraph workflows and LangSmith tracing.

Agent + Multimodal RAG 全栈学习项目：LangGraph PAE/ReAct、Multi-Query、BM25 + pgvector 混合检索、RAG-Fusion/RRF、MinerU/OCR/ASR、evidence 引用与 HITL；React + Spring Boot + FastAPI。

AI document assistant with a production RAG pipeline — hybrid BM25 + semantic search, cross-encoder reranking, and streaming answers. Built with FastAPI, Streamlit, ChromaDB, and DeepSeek LLM. Fully Dockerized and deployed on Railway.

Self-hosted knowledge base for AI agents: ingest Markdown into Qdrant with H1-H...-aware chunking, hybrid (dense+sparse) search and cross-encoder reranking.

Trustworthy AI Knowledge Workbench — Verifiable RAG with citation tracking, document-grounded review, and self-contained desktop mode. Windows / macOS / Linux / Android / iOS.

AI Research Agent 2026 🔍 - Autonomous Deep Web Explorer & Adversarial Synthesis

A reference n8n workflow that turns a folder of business documents into a chat agent that only answers from those documents. Retrieval-augmented, grounded, with refusal when the answer is not in the indexed sources.

🧠 A persistent memory RAG chatbot that never forgets. Uses Phi-3-mini LLM, ChromaDB vector database, and Ebbinghaus memory decay curves.

AI agent for Azure DevOps work item analysis, sprint readiness, RAG-based code understanding, and developer task planning.

On-Premise RAG (продвинутая версия): vLLM/Ollama + bge-m3 + reranker + Qdrant + hybrid (BM25+RRF) + PDR + Basic Auth + Prometheus/Grafana + локальная оценка. Запускается на Ollama без GPU. Тех-задача Petrel AI (Astana Hub).

A practical, first-principles handbook on production RAG & agentic systems — RU/EN/SK, built with Docusaurus.

A RAG pipeline that reads PDFs and answers questions using LangChain and ChromaDB. The Streamlit app uses Qwen/Qwen2.5-7B-Instruct while a quantized Zephyr-7B LLM is used in the notebook.

Self-healing RAG on n8n with LLM-as-judge eval, drift detection, investigator agent, and canary A/B deploys.

Enterprise RAG platform for secure document intelligence using hybrid retrieval, reranking, Gemini LLM, RBAC, HITL, citations, and web search.

A Research Cortex. A research companion for Claude. Your library, notes & memory stay on your machine; answers cited from your own library; 30+ specialist skills; auto cross-pollination across papers, meetings, ideas, notes & journal; weekly self-review that drafts improvements for your approval. Reasoning runs on the Claude API.

AI agent that answers questions about YouTube tutorials and your own codebase — available as a Chrome extension, desktop app, and web app.

A Retrieval-Augmented Generation (RAG) chatbot for answering LPDP scholarship questions using Hybrid Retrieval, Cross-Encoder Reranking, and Llama 3.3.

A small, working Retrieval-Augmented Generation API (FastAPI + Chroma + Claude). Ingest documents, ask questions, get answers grounded only in your data with cited sources.

Enterprise document assistant built with ASP.NET Core, FastAPI, PostgreSQL, Redis, Docker Compose, semantic search and a local RAG workflow.

Enterprise-grade Retrieval-Augmented Generation (RAG) chatbot with FastAPI, React, FAISS, Gemini/Ollama, PostgreSQL, Redis, JWT authentication, RBAC, and semantic document search.

Production-style RAG operations console with grounded retrieval, document ingestion, evaluation, cost analysis, and bias-aware LLM judging.

Reproducibility materials for: A Multi-Source RAG Framework for Intelligent Agent Orchestration in Enterprise CRM Systems (IEEE Access 2026)

Medical Chatbot using Python, RAG, PDF Knowledge Base, LangChain & Mistral AI. Extracts medical information from PDFs and answers user questions with intelligent retrieval and AI-powered responses.

Document-Driven Aviation RAG Assistant built using FastAPI, FAISS, BM25, Gemini API, and Streamlit. Provides grounded answers from aviation manuals with citations, hallucination prevention, and hybrid retrieval.

AI-powered e-commerce assistant using Gemini, RAG pipeline, FAISS vector search, and Streamlit for interactive product recommendations.

Document Q&A chatbot built with FastAPI, Supabase pgvector, and Groq — answers strictly from the uploaded document, with Docker support.

Identify bird species from images along with audio and explore them with AI-powered insights.

An AI-powered YouTube Q&A system that lets users chat with video content using RAG pipelines, LangChain, FastAPI, and vector search.

12-question wizard that recommends an optimal RAG architecture (Standard / Graph / Agentic) with an AI-evaluated pipeline.

Federal Acquisition Regulation Retrieval-Augmented Generation (RAG) Chatbot

Production-shaped agentic RAG: LangGraph plan->act->verify agent + hybrid retrieval + guardrails + an eval gate in CI. Provider-agnostic; runs offline with no keys.

🏥 AI-powered Clinical Document RAG Engine — Ask questions about medical documents, get precise answers with citations. 86% retrieval accuracy. Free LLM support (Gemini/Groq/Claude).

And end to end RAG-based medical application (medibot) - Groq LLM, Langchain & Pinecone Vector Database.

Agentic AI portfolio: planning agents, multi-agent orchestration, RAG, memory systems, semantic routing, evaluation pipelines, and business workflow automation.

HarryBot is a retrieval-based chatbot that answers Harry Potter questions using FAISS vector search, conversational memory, similarity thresholding, and a Streamlit interface.

End-to-end RAG system built from scratch with recursive chunking, persistent indexing, vector + BM25 hybrid retrieval, evaluation, FastAPI, Docker, and AWS deployment testing.

AI-powered Retrieval-Augmented Generation (RAG) chatbot built with n8n and Google Gemini. Supports PDF upload, semantic search, document question answering, and summarization using vector embeddings.

AI-powered legacy code migration platform that transforms .NET (C#) applications into Java using LangChain, Milvus Vector Database, RAG, validation pipelines, security analysis, and automated project generation.