# AI Integration Rules for Code-Index-MCP
## Overview
This file defines rules and guidelines for AI integration within the Code-Index-MCP project, including embedding services, language models, and semantic search capabilities.
## Embedding Services
### Voyage AI Integration
- **Purpose**: Generate code embeddings for semantic search
- **Configuration**: API key required in environment variables
- **Fallback**: Local embedding generation if API unavailable
- **Caching**: Embeddings should be cached locally to reduce API calls
### Implementation Guidelines
```python
# Use the semantic_indexer utility
from mcp_server.utils.semantic_indexer import SemanticIndexer
# Initialize with API key
indexer = SemanticIndexer(api_key=os.getenv("VOYAGE_AI_API_KEY"))
# Generate embeddings with caching
embeddings = indexer.get_embeddings(code_snippet, use_cache=True)
```
## Language Model Integration
### MCP Protocol
- **Purpose**: Provide code intelligence to Claude and other LLMs
- **Tools**: Symbol lookup, code search, definition finding
- **Response Format**: Structured JSON with location references
### Best Practices
1. **Batch Processing**: Group embedding requests to minimize API calls
2. **Error Handling**: Gracefully fall back to fuzzy search on API failures
3. **Rate Limiting**: Respect API rate limits with exponential backoff
4. **Security**: Never log or expose API keys
## Semantic Search
### Hybrid Approach
- Combine lexical search (fuzzy matching) with semantic search
- Weight results based on confidence scores
- Provide context-aware code suggestions
### Query Processing
```python
# Example hybrid search implementation
def hybrid_search(query: str, weights: Dict[str, float] = None):
weights = weights or {"lexical": 0.4, "semantic": 0.6}
# Lexical search using fuzzy indexer
lexical_results = fuzzy_indexer.search(query)
# Semantic search using embeddings
semantic_results = semantic_indexer.search(query)
# Combine and rank results
return combine_results(lexical_results, semantic_results, weights)
```
## Future AI Integrations
### Planned Features
1. **Code Completion**: Context-aware suggestions
2. **Code Review**: Automated quality checks
3. **Documentation Generation**: AI-powered docs
4. **Refactoring Suggestions**: Intelligent code improvements
### Integration Points
- Plugin system for AI providers
- Standardized API for different models
- Configurable model selection
- Local model support (future)
## Security Considerations
### API Key Management
- Store keys in environment variables
- Use secure key rotation
- Implement key encryption at rest
- Monitor API usage for anomalies
### Data Privacy
- Option to disable cloud features
- Local-only mode for sensitive code
- Data retention policies
- Audit logging for API calls
## Performance Guidelines
### Optimization Strategies
1. **Caching**: Cache embeddings and results
2. **Batching**: Process multiple files together
3. **Async Processing**: Non-blocking API calls
4. **Resource Limits**: Cap memory and CPU usage
### Monitoring
- Track API response times
- Monitor embedding quality
- Log cache hit rates
- Alert on degraded performance
