Ground LLMS and Reduce Hallucinations

Unlock Enterprise Data:
LLMs + Knowledge Graphs

For enterprises to confidently adopt Large Language Models (LLMs), results need to reference factual data consistently, ensuring context, accuracy, and explainability in every outcome.

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Generative AI Tailored to
Your Organization

By capturing the context of your enterprise data, knowledge graphs enable AI systems to reason, infer, and retrieve information. Power generative AI with a Neo4j knowledge graph for increased accuracy and explainability, while maintaining enterprise privacy controls.

Deliver Responses Specific to Your Enterprise

Find answers about your organization that are backed by institutional knowledge, including documents, internal wikis, and data stores.

Reduce LLM Hallucinations

Combine the power of your generative model with the stored data of your knowledge graph for more accurate responses.

Increase Explainability

Verify the enriched responses from your LLM using the relationships in your enterprise knowledge graph.


Leading Use Cases for LLMs and Knowledge Graphs

Neo4j knowledge graphs and LLMs are a powerful combination. Whether using natural language to query your enterprise graph or turning unstructured data into a knowledge graph, LLMs and knowledge graphs are perfect together.

Learn more about use cases

Natural Language Query

Enable all users to query your knowledge graph by asking questions in plain language.

Jumpstart Knowledge Graph

Build graph models faster by rapidly generating a knowledge graph from unstructured data.

Optimized Search

Combine vector embeddings with node filtering to quickly retrieve similar items at a much lower cost.

Deep Partnerships with Generative AI Providers

Neo4j Integrates with LLMs and Data Ecosystems

Neo4j’s scalable and flexible database and analytics technologies seamlessly integrate with generative AI frameworks like LangChain, Vertex AI, OpenAI, and beyond. This unlocks a universe of information, while setting a new standard for AI accuracy, transparency, and explainability.

How It Works:

Go beyond similarity search. Utilize the built-in user defined procedures (APOC-library) to integrate with popular LLM-APIs (Vertex AI & OpenAI), compute similarities, and persist nearest neighbor relationships with Neo4j Graph Data Science

Use popular language model APIs directly from your graph database queries

Generate text embeddings from nodes and their neighboring context

Turn user questions into embeddings by calling the API

Use vector similarity functions to determine best matches from the knowledge graph

Enrich user questions with context from these relevant subgraphs to ensure reliable LLM answers

Use the completion or chat completion APIs to generate rich responses based on question and context


    WITH "A movie from about a haunted house and ghosts" as question

    CALL[question],$apiToken, $project) 
    YIELD embedding

    MATCH (m:Movie) 
    WITH m, gds.similarity.cosine(m.embedding, embedding) AS similarity
    ORDER BY similarity DESC LIMIT 5

    MATCH (g:Genre)<-[:IN_GENRE]-(m)<-[:ACTED_IN|DIRECTED]-(p:Person)
    RETURN m.title, m.year, m.plot,, collect( as cast

The APOC library provides access to user-defined procedures and functions which extend the use of the Cypher query language into areas such as data integration, graph algorithms, and data conversion.

Neo4j Graph Data Science is an analytics and ML solution that uses the relationships in your data to discover fast, actionable insights and improve predictions. Explore billions of data points in seconds.

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