GraphRAG Lifecycle in GraphAI
The GraphRAG component of GraphAI follows a continuous lifecycle to maintain an up-to-date and comprehensive knowledge graph:
Data Ingestion: Continuously indexing new blockchain data.
Relationship Mapping: Identifying and establishing connections between data points.
Context Building: Aggregating related information to provide a fuller picture of each entity or transaction.
Query Optimization: Utilizing the graph structure to enhance the speed and accuracy of complex queries.
AI Model Integration: Providing contextual information to AI models for improved understanding and decision-making.
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.
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