mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-06 01:54:11 +08:00
4458af83d8
- 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.
136 lines
2.3 KiB
Markdown
136 lines
2.3 KiB
Markdown
# Memory Embedder - Quick Reference
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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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## Commands
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### Status
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```bash
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python scripts/memory_embedder.py status <db_path>
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```
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### Embed All
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```bash
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python scripts/memory_embedder.py embed <db_path>
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```
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### Embed Specific Source
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```bash
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python scripts/memory_embedder.py embed <db_path> --source-id CMEM-20250101-120000
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```
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### Re-embed (Force)
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```bash
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python scripts/memory_embedder.py embed <db_path> --force
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```
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### Search
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```bash
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python scripts/memory_embedder.py search <db_path> "authentication flow"
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```
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### Advanced Search
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```bash
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python scripts/memory_embedder.py search <db_path> "rate limiting" \
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--top-k 5 \
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--min-score 0.5 \
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--type workflow
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```
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## Database Path
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Find your database:
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```bash
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# Linux/Mac
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~/.ccw/projects/<project-id>/core-memory/core_memory.db
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# Windows
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%USERPROFILE%\.ccw\projects\<project-id>\core-memory\core_memory.db
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```
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## TypeScript Integration
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```typescript
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import { execSync } from 'child_process';
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// Status
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const status = JSON.parse(
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execSync(`python scripts/memory_embedder.py status "${dbPath}"`, {
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encoding: 'utf-8'
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})
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);
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// Embed
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const result = JSON.parse(
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execSync(`python scripts/memory_embedder.py embed "${dbPath}"`, {
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encoding: 'utf-8'
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})
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);
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// Search
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const matches = JSON.parse(
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execSync(
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`python scripts/memory_embedder.py search "${dbPath}" "query"`,
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{ encoding: 'utf-8' }
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)
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);
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```
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## Output Examples
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### Status
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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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### Embed
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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
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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 authentication...",
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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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## 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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## Performance
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- Embedding: ~8 chunks/second
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- Search: ~0.1-0.5s for 1000 chunks
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- Model load: ~0.8s (cached)
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- Batch size: 8 (default, configurable)
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