# Semantic Search Implementation Complete
## Summary
Successfully implemented semantic search functionality for all 6 language plugins in the MCP server:
### ✅ Completed Tasks
1. **Python Plugin** - Fully functional with semantic search
- Uses AST parsing for accurate symbol extraction
- Supports functions, classes, and methods
- Integrates with Voyage AI for embeddings
2. **JavaScript Plugin** - Semantic search enabled
- Simplified implementation without tree-sitter
- Supports functions, classes, and arrow functions
- Works with .js, .jsx, .ts, .tsx files
3. **C Plugin** - Semantic capabilities added
- Extracts functions, structs, and typedefs
- Basic symbol detection using pattern matching
4. **C++ Plugin** - Semantic search working
- Supports classes, templates, and methods
- Handles C++ specific constructs
5. **Dart Plugin** - Semantic features implemented
- Detects classes, functions, and async methods
- Supports Dart-specific syntax
6. **HTML/CSS Plugin** - Semantic search enabled
- Extracts HTML IDs and classes
- Identifies CSS selectors
- Works with multiple file types (.html, .css, .scss, etc.)
### Technical Implementation
- **Base Class**: `PluginWithSemanticSearch` provides semantic capabilities
- **Embeddings**: Voyage AI's `voyage-code-3` model for code embeddings
- **Vector Storage**: Qdrant (supports both HTTP and in-memory modes)
- **Integration**: Seamless fallback to traditional search when semantic is disabled
### Configuration
To enable semantic search:
1. Set environment variables:
```bash
SEMANTIC_SEARCH_ENABLED=true
VOYAGE_API_KEY=your-api-key
QDRANT_HOST=localhost:6333 # or :memory: for in-memory mode
```
2. Each plugin automatically uses its semantic version when enabled
3. Search with semantic flag:
```python
results = plugin.search("find async data fetching functions", {"semantic": True})
```
### Architecture
```
Plugin → PluginWithSemanticSearch → SemanticIndexer → Voyage AI
↓
Qdrant DB
```
All plugins now inherit from `PluginWithSemanticSearch` when semantic search is enabled, providing:
- Automatic embedding generation during indexing
- Semantic search capabilities
- Fallback to traditional fuzzy search
- Unified interface across all languages
### Testing
All plugins tested and verified with realistic code examples:
- Python: Prime checking and factorial functions
- JavaScript: Async API calls and data processing
- C: Linked list implementation
- C++: Template containers and classes
- Dart: Async services and data models
- HTML/CSS: Web page structure and styling
## Next Steps
The semantic search implementation is complete and ready for production use. Consider:
- Performance optimization for large codebases
- Caching frequently accessed embeddings
- Fine-tuning similarity thresholds
- Adding more sophisticated symbol extraction
