AST-Based Metadata Extraction Pipeline

[FredericoPerimLopes]

🎯 Objective

Implement comprehensive AST-based metadata extraction to capture rich semantic information for improved code retrieval.

📋 Current State

• Tree-sitter AST parsing for basic semantic type detection
• Limited metadata extraction (only semantic_type)
• No function signature or dependency analysis

🚀 Implementation Plan

Core Metadata Extraction Features

1. Function/Method Analysis

def extract_function_metadata(node: Node) -> dict:
    return {
        'function_name': extract_function_name(node),
        'parameters': extract_parameters_with_types(node),
        'return_type': extract_return_type_annotation(node),
        'decorators': extract_decorators(node),
        'docstring': extract_docstring(node),
        'complexity_score': calculate_cyclomatic_complexity(node)
    }

2. Class Hierarchy Analysis

def extract_class_metadata(node: Node) -> dict:
    return {
        'class_name': extract_class_name(node),
        'inheritance_chain': extract_base_classes(node),
        'interfaces': extract_implemented_interfaces(node),
        'methods': extract_class_methods(node),
        'properties': extract_class_properties(node)
    }

3. Import Dependency Mapping

def extract_import_metadata(file_content: str) -> dict:
    return {
        'direct_imports': extract_import_statements(content),
        'from_imports': extract_from_imports(content),
        'dependencies': resolve_cross_file_dependencies(content),
        'external_packages': identify_external_dependencies(content)
    }

🔧 Technical Implementation

Language-Specific Extractors

• Python: ast + tree-sitter for comprehensive analysis
• JavaScript/TypeScript: TypeScript compiler API integration
• Java: JavaParser integration for full metadata
• Go/Rust: Language-specific AST libraries

Performance Optimizations

• Parallel processing for large files
• Caching of complex AST operations
• Incremental metadata updates
• Memory-efficient AST traversal

✅ Success Criteria

• Function signature extraction for all supported languages
• Class inheritance chain mapping
• Import dependency analysis
• Docstring/comment extraction
• Complexity metrics calculation
• 95%+ accuracy for metadata extraction
• <50ms processing time per file
• Comprehensive test coverage

🔗 Dependencies

• None (can start immediately, uses existing Tree-sitter)

🔄 Parallel Tasks

• Can run parallel with Issues Enhanced CodeChunk Model with Rich Metadata #8 (Enhanced CodeChunk Model) and AST-Based Metadata Extraction Pipeline #9 (BM25 Search Engine)

📅 Estimated Timeline

3-4 days