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
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Core Components of GraphAI

Enhancing Blockchain Data Context and Relationships

PreviousThe GraphAI ManifestoNextBlockchain Data Indexing

Last updated 3 months ago

The Graphai workflow consists of several key stages:

  1. Data Ingestion: Continuous indexing of blockchain data

  2. Sub-Index Creation: User-defined creation of specialized data subsets

  3. Vector Database Storage: Efficient storage of sub-indexes for AI operations

  4. Knowledge Graph Integration: Mapping relationships and context within the data

  5. API Access: Enabling developers to query and utilize the processed data

  6. AI/LLM Application Development: Creation of intelligent dApps leveraging Graphai's capabilities