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.
POWER LLMS WITH ENTERPRISE KNOWLEDGE
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.
KNOWLEDGE GRAPHS AND LLMS ARE A PERFECT PAIR
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.
Neo4j Integrates with LLMs and Data Ecosystems
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 apoc.ml.vertextai.embedding([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, g.name, collect(p.name) 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.
