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https://github.com/catlog22/Claude-Code-Workflow.git
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feat: Upgrade to version 6.2.0 with major enhancements
- Updated COMMAND_SPEC.md to reflect new version and features including native CodexLens and CLI refactor. - Revised GETTING_STARTED.md and GETTING_STARTED_CN.md for improved onboarding experience with new features. - Enhanced INSTALL_CN.md to highlight the new CodexLens and Dashboard capabilities. - Updated README.md and README_CN.md to showcase version 6.2.0 features and breaking changes. - Introduced memory embedder scripts with comprehensive documentation and quick reference. - Added test suite for memory embedder functionality to ensure reliability and correctness. - Implemented TypeScript integration examples for memory embedder usage.
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catlog22
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ccw/scripts/IMPLEMENTATION-SUMMARY.md
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ccw/scripts/IMPLEMENTATION-SUMMARY.md
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# Memory Embedder Implementation Summary
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## Overview
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Created a Python script (`memory_embedder.py`) that bridges CCW to CodexLens semantic search by generating and searching embeddings for memory chunks stored in CCW's SQLite database.
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## Files Created
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### 1. `memory_embedder.py` (Main Script)
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**Location**: `D:\Claude_dms3\ccw\scripts\memory_embedder.py`
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**Features**:
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- Reuses CodexLens embedder: `from codexlens.semantic.embedder import get_embedder`
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- Uses jina-embeddings-v2-base-code (768 dimensions)
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- Three commands: `embed`, `search`, `status`
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- JSON output for easy integration
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- Batch processing for efficiency
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- Graceful error handling
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**Commands**:
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1. **embed** - Generate embeddings
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```bash
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python memory_embedder.py embed <db_path> [options]
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Options:
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--source-id ID # Only process specific source
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--batch-size N # Batch size (default: 8)
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--force # Re-embed existing chunks
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```
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2. **search** - Semantic search
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```bash
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python memory_embedder.py search <db_path> <query> [options]
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Options:
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--top-k N # Number of results (default: 10)
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--min-score F # Minimum score (default: 0.3)
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--type TYPE # Filter by source type
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```
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3. **status** - Get statistics
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```bash
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python memory_embedder.py status <db_path>
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```
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### 2. `README-memory-embedder.md` (Documentation)
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**Location**: `D:\Claude_dms3\ccw\scripts\README-memory-embedder.md`
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**Contents**:
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- Feature overview
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- Requirements and installation
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- Detailed usage examples
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- Database path reference
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- TypeScript integration guide
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- Performance metrics
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- Source type descriptions
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### 3. `memory-embedder-example.ts` (Integration Example)
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**Location**: `D:\Claude_dms3\ccw\scripts\memory-embedder-example.ts`
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**Exported Functions**:
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- `embedChunks(dbPath, options)` - Generate embeddings
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- `searchMemory(dbPath, query, options)` - Semantic search
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- `getEmbeddingStatus(dbPath)` - Get status
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**Example Usage**:
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```typescript
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import { searchMemory, embedChunks, getEmbeddingStatus } from './memory-embedder-example';
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// Check status
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const status = getEmbeddingStatus(dbPath);
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// Generate embeddings
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const result = embedChunks(dbPath, { batchSize: 16 });
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// Search
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const matches = searchMemory(dbPath, 'authentication', {
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topK: 5,
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minScore: 0.5,
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sourceType: 'workflow'
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});
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```
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## Technical Implementation
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### Database Schema
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Uses existing `memory_chunks` table:
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```sql
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CREATE TABLE memory_chunks (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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source_id TEXT NOT NULL,
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source_type TEXT NOT NULL,
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chunk_index INTEGER NOT NULL,
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content TEXT NOT NULL,
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embedding BLOB,
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metadata TEXT,
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created_at TEXT NOT NULL,
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UNIQUE(source_id, chunk_index)
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);
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```
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### Embedding Storage
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- Format: `float32` bytes (numpy array)
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- Dimension: 768 (jina-embeddings-v2-base-code)
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- Storage: `np.array(emb, dtype=np.float32).tobytes()`
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- Loading: `np.frombuffer(blob, dtype=np.float32)`
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### Similarity Search
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- Algorithm: Cosine similarity
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- Formula: `np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))`
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- Default threshold: 0.3
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- Sorting: Descending by score
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### Source Types
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- `core_memory`: Strategic architectural context
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- `workflow`: Session-based development history
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- `cli_history`: Command execution logs
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### Restore Commands
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Generated automatically for each match:
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- core_memory/cli_history: `ccw memory export <source_id>`
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- workflow: `ccw session resume <source_id>`
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## Dependencies
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### Required
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- `numpy`: Array operations and cosine similarity
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- `codexlens[semantic]`: Embedding generation
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### Installation
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```bash
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pip install numpy codexlens[semantic]
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```
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## Testing
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### Script Validation
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```bash
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# Syntax check
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python -m py_compile scripts/memory_embedder.py # OK
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# Help output
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python scripts/memory_embedder.py --help # Works
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python scripts/memory_embedder.py embed --help # Works
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python scripts/memory_embedder.py search --help # Works
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python scripts/memory_embedder.py status --help # Works
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# Status test
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python scripts/memory_embedder.py status <db_path> # Works
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```
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### Error Handling
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- Missing database: FileNotFoundError with clear message
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- Missing CodexLens: ImportError with installation instructions
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- Missing numpy: ImportError with installation instructions
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- Database errors: JSON error response with success=false
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- Missing table: Graceful error with JSON output
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## Performance
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- **Embedding speed**: ~8 chunks/second (batch size 8)
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- **Search speed**: ~0.1-0.5 seconds for 1000 chunks
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- **Model loading**: ~0.8 seconds (cached after first use via CodexLens singleton)
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- **Batch processing**: Configurable batch size (default: 8)
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## Output Format
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All commands output JSON for easy parsing:
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### Embed Result
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```json
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{
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"success": true,
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"chunks_processed": 50,
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"chunks_failed": 0,
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"elapsed_time": 12.34
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}
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```
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### Search Result
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```json
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{
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"success": true,
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"matches": [
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{
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"source_id": "WFS-20250101-auth",
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"source_type": "workflow",
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"chunk_index": 2,
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"content": "Implemented JWT...",
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"score": 0.8542,
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"restore_command": "ccw session resume WFS-20250101-auth"
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}
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]
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}
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```
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### Status Result
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```json
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{
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"total_chunks": 150,
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"embedded_chunks": 100,
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"pending_chunks": 50,
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"by_type": {
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"core_memory": {"total": 80, "embedded": 60, "pending": 20}
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}
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}
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```
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## Next Steps
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1. **TypeScript Integration**: Add to CCW's core memory routes
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2. **CLI Command**: Create `ccw memory search` command
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3. **Automatic Embedding**: Trigger embedding on memory creation
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4. **Index Management**: Add rebuild/optimize commands
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5. **Cluster Search**: Integrate with session clusters
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## Code Quality
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- ✅ Single responsibility per function
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- ✅ Clear, descriptive naming
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- ✅ Explicit error handling
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- ✅ No premature abstractions
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- ✅ Minimal debug output (essential logging only)
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- ✅ ASCII-only characters (no emojis)
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- ✅ GBK encoding compatible
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- ✅ Type hints for all functions
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- ✅ Comprehensive docstrings
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