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. White Paper
  2. UATU Architecture

Natural Language Processing (NLP) module

The NLP module serves as an interface between users and the UATU system, allowing them to submit queries in natural language. This module processes user queries, understands the context, and converts them into UATU Query Language (UATU QL) statements. The NLP module employs advanced machine learning algorithms and language models to ensure a high degree of accuracy in query interpretation and context understanding.

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Last updated 2 years ago

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