Setting Up and Running GraphRAG with Neo4j

The Neo4j GraphRAG Python package provides a complete solution for creating end-to-end workflows, from transforming unstructured data into a knowledge graph to enabling knowledge graph retrieval and building full GraphRAG pipelines. This package simplifies integrating knowledge graphs into Python-based GenAI applications (like knowledge assistants, search APIs, chatbots, or report generators), improving the accuracy, relevance, and explainability of retrieval-augmented generation (RAG).

This guide demonstrates how to use the GraphRAG Python package, build a GraphRAG pipeline, and explore various knowledge graph retrieval methods to customize your GenAI application.

Table of Contents:

• GraphRAG: Enhancing GenAI with Knowledge Graphs
• Core Components of the GraphRAG Knowledge Graph Pipeline
• Setting Up a Neo4j Database
  • Installing Necessary Libraries
  • Configuring Neo4j Connection Details
  • Setting the OpenAI API Key
• 1. Building and Defining the Knowledge Graph Pipeline
  • Neo4j Driver Initialization
  • Initializing the LLM and Embedding Model
  • Defining Node Labels
  • Defining Relationship Types
  • Creating the Prompt Template
  • Constructing the Knowledge Graph Pipeline
• 2. Retrieving Data from Your Knowledge Graph
  • Vector Retriever and Knowledge Graph Retrieval
  • Utilizing the VectorCypherRetriever for Graph Traversal
• 3. Building a GraphRAG Pipeline
  • Instantiating and Running GraphRAG
• Conclusion
• Frequently Asked Questions

GraphRAG: Leveraging Knowledge Graphs to Improve GenAI

GraphRAG tackles common LLM challenges like hallucinations by combining knowledge graphs with RAG. It enriches responses with context-specific information, resulting in higher-quality, more precise outputs than traditional RAG methods. Knowledge graphs provide crucial contextual data, enabling LLMs to provide reliable answers and function effectively in complex scenarios. Unlike traditional RAG, which relies on fragmented text data, GraphRAG incorporates both structured and semi-structured data into the retrieval process.

The GraphRAG Python package facilitates knowledge graph creation and advanced retrieval methods (graph traversals, text-to-Cypher query generation, vector searches, and full-text searches). It also offers tools for building complete RAG pipelines, enabling seamless GraphRAG integration with Neo4j in GenAI applications.

Key Components of the GraphRAG Knowledge Graph Construction Pipeline

The GraphRAG knowledge graph (KG) construction pipeline comprises several essential components for transforming raw text into structured data optimized for RAG:

• Document Parser: Extracts text from various document formats (e.g., PDFs).
• Document Chunker: Divides text into smaller segments suitable for LLM token limits.
• Chunk Embedder (Optional): Generates vector embeddings for each chunk, enabling semantic matching.
• Schema Builder: Defines the KG structure, guiding entity extraction and ensuring consistency.
• LexicalGraphBuilder (Optional): Creates a lexical graph linking documents and chunks.
• Entity and Relation Extractor: Identifies entities (e.g., people, dates) and their relationships.
• Knowledge Graph Writer: Stores entities and relations in the graph database for retrieval.
• Entity Resolver: Merges duplicate or similar entities into a single node for graph integrity.

These components collaboratively create a dynamic knowledge graph powering GraphRAG, enabling more precise and context-aware LLM responses.

Setting Up a Neo4j Database

The initial step in the RAG workflow is setting up a retrieval database. Neo4j AuraDB offers a convenient way to launch a free Graph Database. AuraDB Free is suitable for basic use, while AuraDB Professional (Pro) provides enhanced memory and performance for larger ingestion and retrieval tasks. For this guide, we'll use the free tier.

After logging into Neo4j AuraDB and creating a free instance, you'll obtain credentials (username, Neo4j URL, and password) to connect to your database.

Installing Required Libraries

Install necessary libraries using pip:

pip install fsspec openai numpy torch neo4j-graphrag

Configuring Neo4j Connection Details

NEO4J_URI = ""
username = ""
password = ""

Replace placeholders with your Neo4j credentials.

Setting the OpenAI API Key

import os
os.environ['OPENAI_API_KEY'] = ''

Replace the placeholder with your OpenAI API key.

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Conclusion (rewritten):

This guide showcased how the Neo4j GraphRAG Python package enhances retrieval-augmented generation (RAG) by integrating knowledge graphs with large language models (LLMs). We demonstrated building a knowledge graph from research documents, storing it in Neo4j, and using retrieval methods (VectorRetriever and VectorCypherRetriever) to generate accurate, contextually relevant responses.

The combination of knowledge graphs and RAG mitigates issues like hallucinations and provides domain-specific context, improving response quality. The use of multiple retrieval techniques further enhances accuracy and relevance. GraphRAG with Neo4j provides a powerful toolset for building knowledge-driven applications requiring both precise data retrieval and natural language generation.

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