Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.

👨🏻‍💻 Meet Lumina – my personal AI assistant powered by hybrid RAG with Pinecone vector search and Neo4j graph traversal, Google AI, LangChain, and an MCP server with 30+ tools. Features real-time streaming, conversation branching, and a multi-agent agentic AI pipeline for intelligent, grounded responses with inline citations.

Agentic Graph RAG: Skeleton Indexing + VectorCypher + Agentic Router with Self-Correction

Building production-ready Retrieval-Augmented Generation (RAG) systems with LangGraph orchestration and local Ollama models for privacy-preserving AI applications.

AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).

使用langraph构建Agentic-RAG

agentic_graph_rag

Code from the ODSC Agentic Graph RAG workshop combining vector, FTS & graph retrieval for RAG. Includes observability and guardrails for evaluating outputs.

A LangGraph-based conversational RAG system: Multi-turn retrieval and adaptive query optimization

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In this story, I have a super quick tutorial showing you how to create a multi-agent chatbot using LangGraph, RAG, and long-term memory to build a powerful agent chatbot for your business or personal use.

MCP server combining Qdrant vector search, Neo4j knowledge graphs, and Crawl4AI web intelligence with agentic RAG capabilities. FastMCP 2.0 architecture with enterprise security, monitoring, and Kubernetes deployment. AI/ML engineering powerhouse.

A high-performance agentic RAG system combining Graphiti's temporal knowledge graphs with LangGraph's multi-agent orchestration to achieve 100x faster retrieval speeds than traditional RAG through intelligent graph-based indexing and parallel agent processing.

Advanced GraphRAG implementation featuring automated schema generation, optimized batch processing, and comprehensive visualization tools for building intelligent knowledge graphs from documents

Zero cost Agentic Graph RAG chatbot architecture utilizing local Gemma 4, Oracle Cloud ARM, Neo4j, and Qdrant

LangGraph RAG with Grading

Config files for my GitHub profile.

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A space for learning GraphRAG with Microsoft Agent Framework and Neo4j, with a Blazor front end orchestrated by Aspire. Downloads, splits and ingests PDF data into Neo4j, and queries it using an agent workflow. Uses models from Azure Foundry as well as local Ollama models.

A Visual and Interactive tool to learn and explore Hybrid RAG (Vector + Graph DBs) and Agentic RAG systems.

An agentic graph-based Retrieval-Augmented Generation (RAG) system for querying PDFs and the web, built with LangGraph, Gemini LLM, ChromaDB, and Streamlit. Features intelligent multi-node workflows for ingestion, retrieval, web search, and reasoning.

A comprehensive collection of RAG (Retrieval Augmented Generation) implementations 📚✨, from foundational concepts to advanced agentic 🤖 and knowledge graph 🌐 RAGs

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Hybrid RAG system combining vector search with knowledge graphs, built on Pydantic AI agents, FastAPI, PostgreSQL+pgvector, Neo4j+Graphiti

Agentic RAG for long documents, Tree and Graph based reasoning. Cited answers down to the pixel

Movie Graph RAG is an **agentic retrieval-augmented generation (RAG) system** that fuses a **knowledge graph** with **vector similarity search** to enable reasoning-driven queries in the movie domain.

A complete Agentic RAG system using LangGraph, a Knowledge Graph, multi-hop retrieval, and local LLMs (Ollama). Includes ingestion, graph builder, custom retriever, tools, and full CLI agent workflow.

Production-grade LlamaIndex agentic system skill for Claude Code. Covers semantic ingestion, property graphs, context RAG, event-driven workflows, and Phoenix observability. Supports 14+ AI coding agents via skilz installer.