RAG & Wissen
graphrag
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
★ 36,3 Tsd. SterneMIT
Anleitung ansehen →llm-graph-builder ist ein Open-Source-Projekt aus der Kategorie RAG & Wissen von neo4j-labs (neo4j-labs/llm-graph-builder auf GitHub). Es hat 5.276 Sterne auf GitHub und 882 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 6. Oktober 2026 auf GitHub aktualisiert.
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
Decide which unstructured sources should become a graph and what entities and relationships matter.
Create a Neo4j 5.23+ database (Aura free tier works) with APOC, and note its URI and credentials.
Run the backend and frontend (Docker Compose for most setups, or separately for Neo4j Desktop) and set the Neo4j and LLM environment variables.
Import a first source, generate the graph, view it in Neo4j Bloom, and ask questions in the chat interface.
Turns documents and other unstructured sources into nodes, relationships and properties using advanced LLMs.
Generate graphs under a custom schema you define, or reuse existing schemas configured in the settings.
Ingests local files, YouTube, Wikipedia, Amazon S3, Google Cloud Storage and web pages.
Chat with your data through the graph, with response metadata showing the sources behind answers; a standalone /chat-only route is included.
Works with multiple LLM providers (including OpenAI, Gemini, Anthropic and Diffbot options) and embedding models from OpenAI, Gemini, Amazon Titan or Sentence Transformers.
Optional per-user token usage tracking with daily/monthly consumption views and limit checks via API.
Clone LLM Graph Builder and enter the project directory.
git clone https://github.com/neo4j-labs/llm-graph-builder.git
cd llm-graph-builderSet your Neo4j connection and model keys, create the backend virtual environment and install requirements.
# Set NEO4J_URI, NEO4J_USERNAME, NEO4J_PASSWORD, NEO4J_DATABASE in backend/.env
cd backend
python3.12 -m venv venv && source venv/bin/activate
pip install -r requirements.txtRun the API server, then start the frontend dev server in a second terminal.
uvicorn score:app --reload
# frontend: cd frontend && yarn && yarn run devIt is a neo4j-labs project, maintained by Neo4j's labs team and contributors.
The app is Apache-2.0 licensed; you need a Neo4j database (Aura has a free tier) and an LLM provider account for extraction.
Extraction and chat use LLMs, so you need a key for a supported provider (or a local model setup such as Ollama, which the README covers).
Manual pipelines require custom extraction code per source; this tool provides the ingestion, LLM extraction, schema handling and chat UI as one ready application on Neo4j.
Create a Neo4j 5.23+ database, clone the repo, set the environment variables, and start the backend and frontend.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
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