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AI Agent Support

QuantDB is designed to work seamlessly with AI coding assistants and automated development tools. We provide specialized documentation and structured schemas to help AI agents understand and use QuantDB effectively.

Why AI Agent Support Matters

Modern development increasingly relies on AI-powered tools like:

  • Coding Assistants: GitHub Copilot, Claude, ChatGPT, and other AI coding helpers
  • Documentation Generators: Automated API documentation tools
  • Code Analysis Tools: Static analysis and code quality tools
  • Testing Frameworks: Automated test generation systems

QuantDB's AI agent support ensures these tools can:

✅ Generate accurate code examples
✅ Validate function calls and parameters
✅ Provide better error handling
✅ Understand package structure and conventions

Key Features

🤖 Structured API Schema

Machine-readable JSON schema defining all QuantDB functions, parameters, and return types in a format optimized for AI consumption.

📚 AI-Friendly Documentation

Comprehensive documentation following Google-style docstring standards with detailed type information and usage examples.

🔧 Best Practices Guide

Guidelines for AI agents on code generation, error handling patterns, and QuantDB conventions.

âš¡ Performance Optimization

Documentation includes caching strategies and performance tips specifically for AI-generated code.

Getting Started

For AI Tool Developers

If you're building tools that integrate with QuantDB:

  1. Review the Developer Guide for comprehensive documentation standards
  2. Use the API Schema for structured function definitions
  3. Follow our best practices for code generation and error handling

For Users with AI Assistants

When working with AI coding assistants:

# AI assistants can now generate accurate QuantDB code
import qdb

# Get stock data with proper error handling
try:
    data = qdb.get_stock_data("600000", days=30)
    print(f"Retrieved {len(data)} records for stock 600000")
except Exception as e:
    print(f"Error fetching data: {e}")

Technical Highlights

Comprehensive Type Information

def get_stock_data(
    symbol: str,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    days: Optional[int] = None,
    adjust: str = ""
) -> pd.DataFrame:

Detailed Parameter Validation

  • Stock symbol format validation (6-digit format)
  • Date format requirements (YYYYMMDD)
  • Range constraints (days: 1-1000)
  • Enum options for adjustment types

Error Handling Patterns

  • Specific exception types for different error conditions
  • Retry mechanisms for network failures
  • Cache validation and fallback strategies

Benefits for Development Teams

Faster Development

AI assistants can generate more accurate QuantDB code, reducing development time and debugging effort.

Better Code Quality

Structured schemas ensure generated code follows best practices and handles edge cases properly.

Consistent Documentation

AI tools can generate consistent documentation that follows QuantDB conventions.

Reduced Learning Curve

New team members can leverage AI assistants to quickly understand and use QuantDB effectively.

Next Steps

QuantDB's AI agent support is continuously evolving. We welcome feedback and contributions to improve AI tool integration.