## 📋 CHECKLIST: Vector Visualization Feature
### ✅ Core Features (MVP)
#### Phase 1: Foundation (Week 1)
- [ ] **Basic Visualization**
- [ ] Fetch vectors từ Qdrant
- [ ] UMAP reduce 768-dim → 3-dim
- [ ] Plotly 3D scatter plot
- [ ] Export base64 PNG (<1MB)
- [ ] MCP tool integration
- [ ] **Query Visualization**
- [ ] Get query embedding từ Gemini text-embedding-004
- [ ] Transform query với fitted UMAP
- [ ] Show query as red diamond on plot
- [ ] Highlight nearest neighbors
- [ ] **Dependency Management**
- [ ] Optional dependencies via `extras_require`
- [ ] Feature detection at startup
- [ ] Graceful error messages
- [ ] Conditional tool registration
#### Phase 2: Enhancements (Week 2)
- [ ] **Advanced Visualization**
- [ ] 2D mode support
- [ ] Custom color schemes
- [ ] Metadata hover text
- [ ] Adjustable parameters (n_neighbors, min_dist)
- [ ] **Query Features**
- [ ] Show k nearest neighbors (top 10)
- [ ] Display similarity scores
- [ ] Return neighbor metadata
- [ ] Highlight cluster membership
#### Phase 3: Polish (Week 3)
- [ ] **Export Options**
- [ ] PNG static export
- [ ] HTML interactive export
- [ ] JSON data export
- [ ] Batch export multiple formats
***
### ⚡ Performance Optimizations
#### Priority 1: High Impact, Easy Implementation
- [ ] **Cache UMAP Reduced Vectors** (10x faster)
- [ ] Store 3D vectors in Qdrant payload
- [ ] Check cache before computing
- [ ] Invalidate cache on collection update
- [ ] TTL/expiration strategy
- **Target**: 10s → 1s
- **Code location**: `src/visualization/cache.py`
- [ ] **Smart Sampling for Large Datasets** (6x faster)
- [ ] Detect collection size
- [ ] K-means sampling (representative points)
- [ ] Stratified sampling fallback
- [ ] Configurable max_points limit
- **Target**: 30s (100k) → 5s (1k sampled)
- **Code location**: `src/visualization/sampler.py`
#### Priority 2: Medium Impact, Medium Effort
- [ ] **Incremental UMAP Updates** (100x faster for updates)
- [ ] Fit UMAP once initially
- [ ] Transform new vectors incrementally
- [ ] Append to cached vectors
- [ ] Version tracking
- **Target**: Recompute 10s → Add 0.1s
- **Code location**: `src/visualization/incremental_reducer.py`
- [ ] **Async MCP Tools** (Better UX)
- [ ] Convert tools to async functions
- [ ] Use ThreadPoolExecutor for heavy ops
- [ ] Non-blocking server
- [ ] Handle multiple concurrent requests
- **Target**: Responsive UI, no blocking
- **Code location**: `src/mcp/tools.py`
#### Priority 3: Advanced Optimizations
- [ ] **Parallel Processing** (4x faster for batch)
- [ ] Process multiple collections parallel
- [ ] ThreadPoolExecutor management
- [ ] Result aggregation
- **Target**: 5×10s → 12s parallel
- **Code location**: `src/visualization/batch_processor.py`
- [ ] **Progressive Loading** (Perceived speed)
- [ ] Return image immediately
- [ ] Send statistics after
- [ ] Streaming responses
- **Target**: User sees image in 1s, full results in 3s
- **Code location**: `src/mcp/streaming.py`
- [ ] **Batch Image Generation** (Save computation)
- [ ] Generate PNG/JPG/JSON once
- [ ] Reuse figure object
- [ ] Format optimization
- **Target**: 3 formats in ~2s instead of 5s
- **Code location**: `src/visualization/batch_exporter.py`
***
### 🎯 Advanced Features
#### Clustering & Insights
- [ ] **Auto Cluster Detection**
- [ ] KMeans clustering on reduced vectors
- [ ] DBSCAN for density-based
- [ ] Silhouette score calculation
- [ ] Configurable n_clusters
- **Code location**: `src/visualization/clustering.py`
- [ ] **LLM Cluster Labeling** (Gemini)
- [ ] Get cluster documents
- [ ] Generate cluster summary via Gemini
- [ ] Create semantic labels
- [ ] Return labeled clusters
- **Code location**: `src/visualization/cluster_labeling.py`
#### Advanced Query Features
- [ ] **Query Expansion**
- [ ] Generate variations of query
- [ ] Search with multiple embeddings
- [ ] Aggregate results
- **Code location**: `src/visualization/query_expansion.py`
- [ ] **Semantic Drift Detection**
- [ ] Compare collections over time
- [ ] Detect document movement
- [ ] Show temporal changes
- **Code location**: `src/visualization/drift_analysis.py`
#### Collection Features
- [ ] **Collection Comparison**
- [ ] Visualize 2 collections side-by-side
- [ ] Show overlapping/unique documents
- [ ] Semantic similarity between collections
- **Code location**: `src/visualization/comparison.py`
- [ ] **Advanced Filtering**
- [ ] Filter by metadata (source, date, category)
- [ ] Filter by score threshold
- [ ] Filter by cluster membership
- **Code location**: `src/visualization/filters.py`
***
### 📊 Testing & Quality
#### Unit Tests
- [ ] UMAP reducer functionality
- [ ] Sampling algorithms
- [ ] Plotter image generation
- [ ] Cache hit/miss scenarios
- [ ] Error handling
#### Integration Tests
- [ ] MCP tool integration
- [ ] Qdrant connectivity
- [ ] Gemini API calls
- [ ] Image size validation (<1MB)
- [ ] Visualization end-to-end
#### Performance Tests
- [ ] Benchmark: 1000 vectors
- [ ] Benchmark: 10k vectors
- [ ] Benchmark: 100k vectors (with sampling)
- [ ] Cache effectiveness
- [ ] Memory usage profiling
- [ ] CPU usage profiling
#### Manual Testing
- [ ] [ ] Cursor integration
- [ ] [ ] Claude Desktop integration
- [ ] [ ] Image rendering quality
- [ ] [ ] Hover text accuracy
- [ ] [ ] Query visualization correctness
- [ ] [ ] Error messages clarity
- [ ] [ ] Performance feels responsive
***
### 📈 Success Metrics & Targets
| Metric | Target | Current | Gain |
|---|---|---|---|
| **Install size (with viz)** | <250MB | ~4GB* | 94% ↓ |
| **Startup time** | <1s | 3-5s | 3-5x ↑ |
| **First visualization** | <2s | 15-30s | 7-15x ↑ |
| **1000 vectors viz** | <5s | 15s | 3x ↑ |
| **Query viz** | <3s | 10s | 3x ↑ |
| **Memory peak** | <500MB | ~2GB | 75% ↓ |
| **Cache hit rate** | >90% | N/A | - |
| **Incremental update** | <0.5s | 10s | 20x ↑ |
*vs RAGxplorer full version
***
### 🔄 Implementation Roadmap
```
Week 1:
├─ Cache UMAP .......................... ✅ (10x)
├─ Smart sampling ..................... ✅ (6x)
├─ Query nearest neighbors ............ ✅
└─ Dependency management .............. ✅
Week 2:
├─ Incremental UMAP ................... ✅ (100x for updates)
├─ Async tools ........................ ✅
├─ Export PNG/HTML .................... ✅
└─ Cluster auto-detection ............ ✅
Week 3:
├─ LLM cluster labeling ............... ✅
├─ Parallel processing ............... ✅ (4x)
├─ Progressive loading ............... ✅
└─ Testing + benchmarking ............ ✅
Week 4:
├─ Collection comparison ............. ⏳
├─ Advanced filtering ................ ⏳
├─ Documentation ..................... ✅
└─ Release v0.2.0 ................... ✅
```
***
### 📁 Code Structure
```
src/
├─ visualization/
│ ├─ __init__.py
│ ├─ reducer.py ..................... UMAP dimension reduction
│ ├─ cache.py ....................... Caching layer
│ ├─ sampler.py ..................... Smart sampling
│ ├─ incremental_reducer.py ......... Incremental UMAP
│ ├─ plotter.py ..................... Plotly visualization
│ ├─ clustering.py .................. Cluster detection
│ ├─ cluster_labeling.py ........... LLM labels
│ ├─ filters.py ..................... Advanced filtering
│ ├─ batch_processor.py ............ Parallel processing
│ ├─ batch_exporter.py ............ Format export
│ ├─ query_expansion.py ............ Query variations
│ ├─ comparison.py ................. Collection comparison
│ └─ drift_analysis.py ............ Temporal analysis
├─ mcp/
│ ├─ __init__.py
│ ├─ server.py ..................... MCP server
│ ├─ tools.py ...................... MCP tools (async)
│ └─ streaming.py .................. Progressive loading
├─ core/
│ ├─ __init__.py
│ ├─ vector_client.py ............ Qdrant wrapper
│ └─ gemini_client.py ........... Gemini wrapper
└─ __init__.py
```
***
### 📋 GitHub Issues Template
```markdown
## Core Features
- [ ] Issue #1: Basic visualization tool
- [ ] Issue #2: Query visualization
- [ ] Issue #3: Nearest neighbors display
## Performance
- [ ] Issue #10: Cache UMAP vectors
- [ ] Issue #11: Smart sampling
- [ ] Issue #12: Incremental UMAP updates
- [ ] Issue #13: Async tools
- [ ] Issue #14: Parallel processing
## Advanced Features
- [ ] Issue #20: Auto clustering
- [ ] Issue #21: LLM cluster labeling
- [ ] Issue #22: Collection comparison
- [ ] Issue #23: Advanced filtering
## Quality
- [ ] Issue #30: Unit tests
- [ ] Issue #31: Integration tests
- [ ] Issue #32: Performance benchmarks
- [ ] Issue #33: Documentation
```
***
### 🚀 Release Checklist
**v0.1.0** (Core MVP)
- [ ] Basic visualization
- [ ] Query visualization
- [ ] Optional dependencies
- [ ] Tests pass
- [ ] Documentation complete
**v0.2.0** (Performance + Advanced)
- [ ] Caching enabled
- [ ] Sampling implemented
- [ ] Async tools
- [ ] Cluster detection
- [ ] Export options
- [ ] 5-10x faster than v0.1
**v0.3.0** (Premium Features)
- [ ] Collection comparison
- [ ] Advanced filtering
- [ ] LLM labeling
- [ ] Incremental updates
- [ ] Batch processing
- [ ] 25x faster than v0.1
***
### 💾 Configuration Template
```python
# config.py
VISUALIZATION_CONFIG = {
"enabled": True,
"cache": {
"enabled": True,
"ttl": 86400, # 24h
"storage": "qdrant" # or "redis"
},
"sampling": {
"max_points": 1000,
"strategy": "kmeans" # or "stratified", "random"
},
"umap": {
"n_neighbors": 80,
"min_dist": 0.1,
"metric": "cosine"
},
"plot": {
"height": 600,
"width": 800,
"colorscale": "Viridis"
},
"export": {
"formats": ["png", "html", "json"],
"max_size_mb": 1,
"quality": "high"
}
}
```
***
**Đó là checklist hoàn chỉnh!** Bạn có thể in ra và check từng item khi implement 📝
