Build graph RAG context with Neo4j LLM Graph Builder

¿Qué hace Build graph RAG context with Neo4j LLM Graph Builder?

Convert a bounded document set into a Neo4j knowledge graph, inspect extracted nodes and relationships, and use it for graph-backed RAG.

Prerequisites

Neo4j Database 5.23 or later with APOC, Python 3.12 backend, configured LLM provider credentials, optional front-end and Docker deployment paths from the upstream README.

Installation

Use the upstream install or setup path that matches your environment:

• pip install -r requirements.txt -c constraints.txt

Requirements and caveats from upstream:

• 
• Python 3.12 or higher (for local/separate backend deployment)
• If using Neo4j Desktop, you will need to deploy the backend and frontend separately (docker-compose is not supported).

Basic usage or getting-started notes:

• Neo4j Database 5.23 or later with APOC installed.

• Neo4j Aura databases (including the free tier) are supported.

• Create a .env file in the backend folder by copying backend/example.env.

• Source: https://github.com/neo4j-labs/llm-graph-builder

• Extracted from upstream docs: https://raw.githubusercontent.com/neo4j-labs/llm-graph-builder/HEAD/README.md

Documentation

• https://github.com/neo4j-labs/llm-graph-builder

Source

• Agent Skill Exchange