UATU - Query on-chain data in natural language
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  • Problems UATU is solving
  • White Paper
    • Abstract
    • Introduction
      • Background and motivation
      • Objectives of UATU
      • Scope and limitations
    • UATU Architecture
      • Blockchain Nodes & Knowledge Database
      • UATU Services and Service-Specific Databases
      • UATU API Cluster
      • Natural Language Processing (NLP) module
      • UATU Query Language (UATU QL)
      • Data extraction and presentation
  • UATU Services
    • On-chain data services
      • Implementation details and data sources
    • Integration with third-party APIs (where applicable)
  • User Interface, API, and NPM Library
    • Playground for direct user queries
    • API for developers and organisations
    • UATU libraries
      • Installation and usage
      • Library features and functions
    • Security and access control mechanisms
  • Use Cases and Applications
    • End-user scenarios
    • Developer and organisation scenarios
  • Evaluation and Performance Metrics
  • Future Work and Enhancements
  • Conclusion
  • Appendix
    • UATU QL Syntax and Examples
    • Detailed Service Descriptions
    • API Documentation and Usage Examples
    • UATU Library Documentation and Usage Examples
  • Tokenomics
    • Distribution
    • Token Sale Rounds
    • User Metrics & Trade Data
    • Burning
    • Milestones
  • FAQs
    • AI Model & NLP
    • UATU QL
    • UATU Library
    • UATU APIs
    • UATU Services
      • Wallet
      • Ticker
  • Links & Social
    • Uatu Playground
    • UATU Dev Dashboard
    • Twitter
    • Telegram Community
    • LinkedIn
    • Discord
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  1. User Interface, API, and NPM Library
  2. UATU libraries

Library features and functions

The UATU Library offers a wide range of features and functions that cater to different use cases and requirements. Some of these include:

  • Query execution: Execute natural language queries and obtain results in a structured format, enabling seamless integration with existing data processing pipelines.

  • Pagination and filtering: Easily navigate and filter large datasets, allowing for efficient data retrieval and analysis.

  • Caching and performance optimizations: Benefit from built-in caching and performance optimizations to reduce latency and improve application responsiveness.

  • Error handling and debugging: Utilize robust error handling and debugging tools to ensure smooth and reliable application development.

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Last updated 2 years ago

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