Kickstart your GenAI development effortlessly with Neo4j’s ecosystem tool, GraphRAG!
Neo4j’s GraphRAG combines knowledge graphs with RAG to enhance GenAI applications, improving accuracy and reducing hallucinations by leveraging structured data. Key tools include: the Knowledge Graph Builder for transforming unstructured text into graphs; NeoConverse for natural language query processing; and seamless integrations with LangChain, LlamaIndex, and other GenAI frameworks. This ecosystem accelerates development while ensuring explainable AI, helping businesses uncover insights across fraud detection, customer analytics, and IoT applications.
With Neo4j’s GraphRAG ecosystem tools, you can easily get started with knowledge graph-based GenAI applications, improving response quality and interpretability while accelerating development and adoption.
GraphRAG combines retrieval-augmented generation (RAG) with knowledge graphs to address key LLM challenges like hallucinations and lack of domain-specific context. Unlike traditional RAG solutions that only provide access to fragmented text data, GraphRAG integrates structured and semi-structured information into the retrieval process.
Knowledge graphs provide contextual memory, enabling LLMs to answer questions reliably and act as trusted agents in complex workflows. GraphRAG helps users create knowledge graphs from unstructured text and leverages them—or existing graph databases—to retrieve relevant information for generative tasks using vector and graph searches.
Key Tools
Knowledge Graph Builder: Quickly transforms unstructured text (PDFs, Word docs, YouTube transcripts, Wikipedia pages, etc.) into structured graphs, revealing hidden entities and relationships.
Frontend: A React app using Neo4j’s design system and visualization library.
Backend: Python-based (FastAPI) with LangChain integration, running on Google Cloud Run.
NeoConverse: Translates natural language queries into Cypher for graph-based responses. Workflow:
User selects a dataset and response format (text/graph).
The system extracts the database schema, combines it with the query, and generates a Cypher query via LLM.
Results are validated and used to generate a response.
GenAI Framework Integrations:
Supports Python, JavaScript, and Java.
Works with LangChain (vector/graph search, text-to-graph, advanced RAG), LlamaIndex (Cypher/vector search, knowledge graph construction), Spring AI, and DSPy.
Benefits
Enhanced Accuracy: Reduces LLM hallucinations with structured context.
Faster Development: Pre-built tools for quick integration or customization.
Scalability: Handles billions of data connections for fraud detection, customer 360, IoT, and more.
Explore Neo4j’s GenAI ecosystem for embeddings, vector search, and cloud-native integrations (Google Vertex AI, AWS Bedrock, Azure OpenAI). The platform unlocks hidden patterns across industries, delivering actionable insights.