GraphAI
  • Abstract
  • Unleashing the Power of Blockchain Data for AI
  • The GraphAI Manifesto
  • Core Components of GraphAI
    • Blockchain Data Indexing
    • Customizable Sub-Indexes
    • Vector Database Integration
    • Knowledge Graph Construction
    • Contextualizing Blockchain Data
    • Enhancing AI Model Understanding
  • Enhancing AI-Driven Blockchain Applications
    • Empowering LLM-Based dApps
    • Expanding Application Horizons
  • System Architecture: Scalability and Flexibility
  • Advantages of GraphAI
    • Accelerated Development of AI-Driven dApps
    • Improved AI Model Performance
    • Contextual Richness through GraphRAG
  • GraphRAG Lifecycle in GraphAI
  • Future Developments
Powered by GitBook
On this page
  1. Core Components of GraphAI

Vector Database Integration

Sub-indexes are stored in high-performance vector databases, optimizing them for AI and machine learning operations. This storage method allows for rapid similarity searches and efficient processing of high-dimensional data, crucial for many AI applications.

PreviousCustomizable Sub-IndexesNextKnowledge Graph Construction

Last updated 5 months ago