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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GraphRAG Lifecycle in GraphAI

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Last updated 5 months ago

The GraphRAG component of Graphai follows a continuous lifecycle to maintain an up-to-date and comprehensive knowledge graph:

  1. Data Ingestion: Continuously indexing new blockchain data.

  2. Relationship Mapping: Identifying and establishing connections between data points.

  3. Context Building: Aggregating related information to provide a fuller picture of each entity or transaction.

  4. Query Optimization: Utilizing the graph structure to enhance the speed and accuracy of complex queries.

  5. AI Model Integration: Providing contextual information to AI models for improved understanding and decision-making.

  6. Feedback Loop: Continuously refining the graph based on new data and AI model interactions.

This iterative process ensures that the knowledge graph remains a dynamic and valuable resource for AI-driven blockchain applications.