Neo4j GraphRAG for Python

Repository for https://graphacademy.neo4j.com/courses/genai-graphrag-python

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GraphRAG, Outperforms traditional RAG ( Retrieval-Augmented Generation ) for Query Focused Summarization. Opensource research of Knowledge Graph to improving the accuracy of data discovery, solving RAG pain points, solving Black-box hallucination, and to enhance LLMs. Knowledge Graph to GenAI. PDF AI Agent/Chatbot with Python, Neo4j and LangChain

GraphRAG is a Python project for question answering (QA) over a user's personal knowledge graph stored in Neo4j.

Advanced GraphRAG Optimizer: A full Python RAG pipeline with semantic retrieval (FAISS + Neo4j), LLM response (Ollama/Mistral), and explainable AI answers.

GraphRAG with ADK + Python + Neo4j

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Akamai Compute graphrag implementation using Python, ollama, and neo4j

This repository contains Projects which includes python, LLM, OpenAI API, Langchain, Prompt Engineering, OCR,GraphRAG, neo4j

A Python library (Graphygie) to simplify building GraphRAG pipelines using the UMLS knowledge graph on Neo4j

Social media graph analytics project using Neo4j, Python, and GPT-powered GraphRAG to analyze homophily and influence in tagging networks.

Context-aware GraphRAG system that combines vector search with Neo4j graph traversal for multi-hop reasoning over large document collections. Built with Python, LangChain, Groq, and Neo4j, it features LLM-driven graph ETL, hybrid retrieval, benchmarking, and a polished demo UI for exploring graph-powered RAG end to end.

GraphRAG system for multi-repository code analysis. Combines Neo4j graph traversal with vector search to enable semantic code queries across repositories via MCP (Model Context Protocol). Supports Python, JavaScript/TypeScript, and Java via tree-sitter.

A Python-based research assistant that demonstrates GraphRAG by combining vector embeddings with knowledge graph traversal. This system ingests PDF documents, extracts entities and relationships, stores them in Neo4j, and answers questions using hybrid retrieval that goes beyond simple semantic search

The Severance Protocol: A 10-week public sprint building an archival system that ingests and models the DPLA's federated cultural heritage data. Culminates in a conversational AI powered by a Neo4j knowledge graph and GraphRAG architecture. Stack: Python, Terraform, Docker, CI/CD, LangChain.

A polyglot (Rust/Python/C#) local intelligence engine that bridges the gap between human activity and digital memory. monday-kernel utilizes Windows API hooks to track focus, OpenAI Whisper for live meeting transcription, and a Hybrid GraphRAG architecture (Neo4j + ChromaDB) to provide semantically aware retrieval of personal knowledge.