Knowledge Graph vs. Vector Database for Grounding Your LLM

Explore the benefits of grounding Large Language Models (LLMs) in knowledge graphs vs. vector databases for reliable and accurate data.

Knowledge graph vs. vector database for grounding your LLM Skip to content Neo4j to acquire GraphAware, launch new open-standards intelligence analysis solutions | Read more Menu Search Close Menu Products FULLY-MANAGED AuraDB Store and query connected data at scale Virtual Graph Create and query a knowledge graph on existing data Aura Graph Analytics Run graph algorithms on any data, any cloud Aura Agent Build and deploy context-aware agents fast SELF-MANAGED Graph Database Store connected data with a graph database Graph Data Science Run graph algorithms on connected data Enterprise Studio S

related reading

• The GraphRAG manifesto: Adding knowledge to GenAIneo4j.com
• The vector database to build knowledgeable AI | Pineconepinecone.io
• Fine-tuning vs. RAG for LLMsneo4j.com
• llm-wikigist.github.com
• GenAI GraphRAG and AI agents using Vertex AI Reasoning Engine with LangChain and Neo4j - Community Articles - Google Developer forumsgooglecloudcommunity.com
• Voyage AI | Homevoyageai.com
• Datacurve | The data engine for frontier AIdatacurve.ai
• Andrej Karpathy on X: "LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating" / Xx.com
• Graph Enabled Llama Index - siwei.iosiwei.io
• Vector databases explained | Lantern Bloglantern.dev
• GraphRAG: New tool for complex data discovery now on GitHub - Microsoft Researchmicrosoft.com
• From prototype to production: Vector databases in generative AI applications - Stack Overflowstackoverflow.blog