# Crackerjack Semantic Search & Vector Store Implementation Plan

## Executive Summary

This document outlines the implementation of a semantic search and vector embedding system for crackerjack, inspired by simple-files-vectorstore but designed as a native crackerjack feature. This will enhance our 9 AI agents with intelligent codebase understanding and provide semantic search capabilities.

## Architecture Overview

### Core Components

1. **Vector Store Service** (`crackerjack/services/vector_store.py`)

   - File content embedding generation
   - Similarity search functionality
   - Incremental indexing with file watching
   - Cache management and persistence

1. **Semantic Search Agent** (new 10th agent: `crackerjack/agents/semantic_search_agent.py`)

   - Context-aware code pattern finding
   - Related file discovery
   - Documentation search and correlation

1. **MCP Integration** (`crackerjack/mcp/tools/semantic_tools.py`)

   - `semantic_search_codebase` tool
   - `index_project_files` tool
   - `get_semantic_stats` tool

1. **Enhanced AI Agent Integration**

   - Modify existing 9 agents to leverage semantic search
   - Context injection for better fixing decisions
   - Pattern recognition across similar codebases

## Implementation Phases

### Phase 1: Core Vector Store Infrastructure (Week 1)

**Files to Create:**

- `crackerjack/services/vector_store.py` - Main vector store service
- `crackerjack/services/embeddings.py` - Embedding generation (sentence-transformers)
- `crackerjack/models/semantic_models.py` - Data models for search results
- `tests/test_vector_store.py` - Comprehensive test suite

**Features:**

- File content chunking and embedding
- SQLite/JSON persistence for embeddings
- Incremental file indexing
- Basic similarity search

### Phase 2: MCP Tool Integration (Week 2)

**Files to Create:**

- `crackerjack/mcp/tools/semantic_tools.py` - MCP semantic search tools
- `crackerjack/cli/semantic_commands.py` - CLI commands for indexing/search

**Features:**

- `/crackerjack:index` - Create/update semantic index
- `/crackerjack:search` - Semantic search across codebase
- Integration with existing MCP server architecture

### Phase 3: AI Agent Enhancement (Week 3)

**Files to Modify:**

- All 9 existing agents in `crackerjack/agents/`
- `crackerjack/intelligence/agent_orchestrator.py`
- `crackerjack/intelligence/agent_selector.py`

**Files to Create:**

- `crackerjack/agents/semantic_search_agent.py` - New 10th agent
- `crackerjack/intelligence/semantic_context.py` - Context injection system

**Features:**

- Semantic context for refactoring decisions
- Related test file discovery
- Pattern-based security vulnerability detection
- Documentation correlation and updates

### Phase 4: Session Management Integration (Week 4)

**Files to Modify:**

- Session-mgmt integration for conversation indexing
- Quality intelligence with semantic pattern recognition

**Features:**

- Index previous conversation patterns
- Learn from semantic fix patterns
- Cross-session knowledge retention

## Technical Specifications

### Dependencies

**New Dependencies (add to pyproject.toml):**

```toml
"sentence-transformers>=3.3.1",  # For embeddings
"numpy>=2.2.1",                  # Vector operations
"scikit-learn>=1.6.1",          # Similarity calculations
"nltk>=3.10",                   # Text preprocessing
```

### CLI Interface

**New Commands:**

```bash
# Index entire codebase
python -m crackerjack --index-codebase

# Semantic search
python -m crackerjack --search "async exception handling patterns"

# AI fixing with semantic context
python -m crackerjack --ai-fix --with-semantic-context

# Statistics and management
python -m crackerjack --semantic-stats
python -m crackerjack --clear-semantic-cache
```

### File Structure Changes

**New Directories:**

```
crackerjack/
├── services/
│   ├── vector_store.py           # Core vector store
│   ├── embeddings.py             # Embedding generation
│   └── semantic_indexer.py       # File indexing logic
├── agents/
│   └── semantic_search_agent.py  # New 10th agent
├── mcp/tools/
│   └── semantic_tools.py         # MCP integration
└── intelligence/
    └── semantic_context.py       # Context injection
```

## Integration Points

### 1. Existing AI Agents Enhancement

**RefactoringAgent:**

- Find similar code patterns before refactoring
- Suggest consistent naming across codebase

**SecurityAgent:**

- Detect similar security patterns across files
- Find related vulnerabilities

**TestCreationAgent:**

- Discover related test files and patterns
- Suggest test cases based on similar functions

### 2. Quality Intelligence Integration

- Semantic pattern recognition for quality trends
- Cross-project learning and recommendations
- Intelligent hook selection based on file similarity

### 3. Session Management Synergy

- Index conversation history for pattern recognition
- Semantic search across previous fixes and discussions
- Context-aware session restoration

## Performance Considerations

### Efficiency Measures

1. **Incremental Indexing**: Only re-index changed files
1. **Lazy Loading**: Load embeddings on-demand
1. **Caching Strategy**: Cache frequent searches
1. **Batch Processing**: Bulk operations for initial indexing

### Resource Management

- **Memory**: Use memory-mapped files for large indexes
- **Disk**: Configurable cache size with LRU eviction
- **CPU**: Background indexing with configurable workers

## Testing Strategy

### Test Coverage Areas

1. **Unit Tests**: Vector store operations, embedding generation
1. **Integration Tests**: MCP tool functionality, agent enhancements
1. **Performance Tests**: Large codebase indexing, search speed
1. **End-to-End Tests**: Full workflow with semantic context

### Test Data

- Sample codebases with known patterns
- Synthetic similarity test cases
- Real-world crackerjack codebase as test subject

## Security & Privacy

### Data Protection

- Local-only processing (no external API calls)
- Encrypted embedding storage option
- Configurable file exclusion patterns (secrets, configs)
- Respect .gitignore patterns automatically

### Input Validation

- Sanitize all search queries
- Validate file paths and content
- Rate limiting for expensive operations

## Success Metrics

### Quantitative Metrics

1. **Search Accuracy**: >85% relevant results in top 5
1. **Index Performance**: \<1 minute for 10k files
1. **Memory Usage**: \<200MB for typical project
1. **Agent Enhancement**: 20% reduction in fix iterations

### Qualitative Metrics

1. **Developer Experience**: Easier pattern discovery
1. **Code Quality**: Better context-aware fixes
1. **Documentation**: Improved correlation and updates
1. **Learning**: Cross-session knowledge retention

## Risk Mitigation

### Technical Risks

- **Large Codebase Performance**: Implement chunking and pagination
- **Memory Constraints**: Use efficient storage formats
- **Search Quality**: Multiple embedding models, tunable similarity thresholds

### Integration Risks

- **Agent Complexity**: Gradual rollout, feature flags
- **MCP Compatibility**: Backward compatibility maintained
- **Session Management**: Graceful degradation if unavailable

## Timeline & Milestones

**Week 1**: Core vector store infrastructure
**Week 2**: MCP tool integration and CLI commands
**Week 3**: AI agent enhancement and semantic search agent
**Week 4**: Session management integration and optimization

**Final Deliverable**: A semantic-search-enabled crackerjack that provides intelligent codebase understanding while maintaining all existing functionality and performance standards.

## Future Enhancements

### Advanced Features (Post-MVP)

1. **Multi-language Support**: Different embeddings for different file types
1. **Semantic Code Metrics**: Complexity scoring based on semantic similarity
1. **Cross-Project Learning**: Pattern recognition across multiple projects
1. **Visual Search Interface**: Web UI for exploring semantic relationships

This implementation will position crackerjack as not just a quality tool, but an intelligent development assistant that understands and learns from codebases.

## Implementation Status

- [x] Implementation plan document created
- [ ] Phase 1: Core vector store infrastructure
- [ ] Phase 2: MCP tool integration
- [ ] Phase 3: AI agent enhancement
- [ ] Phase 4: Session management integration
- [ ] Dependencies updated
- [ ] Quality checks and testing completed

## Notes

This feature represents a significant evolution of crackerjack from a quality enforcement tool to an intelligent development assistant that understands and learns from codebases. The semantic search capabilities will enhance all existing AI agents while maintaining the clean code philosophy and zero-configuration approach that makes crackerjack unique.
