# Changelog #96 - Multi-Embedding Workflow System

**Date:** 2025-12-07
**Task:** [TASK-050](../_TASKS/TASK-050_Multi_Embedding_Workflow_System.md)
**Type:** Enhancement

---

## Summary

Implemented multi-embedding system for workflow semantic matching. Instead of combining all texts into a single embedding (which diluted semantic similarity), the system now stores separate embeddings for each sample prompt, keyword, description, and name with weighted scoring.

## Problem Solved

**Before (diluted embeddings):**
```
Query: "create a picnic table"
Combined text: "create a picnic table make outdoor table wood bench..."
Similarity: 0.68 (too low for exact match)
```

**After (multi-embedding):**
```
Query: "create a picnic table"
Match: "create a picnic table" (sample_prompt, weight=1.0)
Similarity: ~1.0 (correct match!)
```

## Changes

### New Files
- `server/router/infrastructure/language_detector.py` - Lightweight language detection (en, pl, de, fr, es, ru)

### Modified Files
- `server/router/domain/interfaces/i_vector_store.py`
  - Added `WorkflowEmbeddingRecord` dataclass with source metadata
  - Added `WeightedSearchResult` dataclass for weighted scoring
  - Added interface methods: `search_workflows_weighted()`, `get_workflow_embedding_count()`, `get_unique_workflow_count()`

- `server/router/infrastructure/vector_store/lance_store.py`
  - Implemented `search_workflows_weighted()` with metadata extraction
  - Implemented fallback method for in-memory store
  - Added unique workflow counting methods

- `server/router/application/classifier/workflow_intent_classifier.py`
  - Added `SOURCE_WEIGHTS` and `CONFIDENCE_THRESHOLDS` constants
  - Refactored `_extract_texts_with_metadata()` for multi-embedding
  - Refactored `_compute_and_store_embeddings()` to create separate embeddings
  - Added `find_best_match_with_confidence()` method
  - Added `get_confidence_level()` utility method

- `server/scripts/precompute_embeddings.py`
  - Updated to show multi-embedding statistics
  - Clears cache before fresh embedding generation

- `server/adapters/mcp/areas/vector_db.py`
  - `_action_stats()` now shows multi-embedding info
  - `_action_search_test()` uses weighted search for workflows
  - `_action_add_workflow()` creates multi-embeddings
  - `_action_remove()` handles multi-embedding record deletion

## Architecture

### Scoring Formula
```
final_score = raw_cosine_score * source_weight * language_boost
```

### Source Weights
| Source Type | Weight |
|-------------|--------|
| sample_prompt | 1.0 |
| trigger_keyword | 0.8 |
| description | 0.6 |
| name | 0.5 |

### Confidence Levels
| Level | Threshold | Action |
|-------|-----------|--------|
| HIGH | >= 0.90 | Direct match, high confidence |
| MEDIUM | >= 0.75 | Likely match, medium confidence |
| LOW | >= 0.60 | Possible match, provide fallback candidates |
| NONE | < 0.60 | No match, use generalization |

### Language Boost
- Same language: 1.0
- Different language: 0.9

## Testing

```bash
PYTHONPATH=. poetry run python -c "
from server.router.infrastructure.language_detector import detect_language
print(detect_language('create a picnic table'))  # 'en'
print(detect_language('stwórz stół piknikowy'))  # 'pl'
"
```

## Impact

- Workflow semantic matching now achieves ~1.0 similarity for exact phrase matches
- Language-aware scoring improves multilingual support
- Confidence levels enable graceful fallback to generalization
- MCP tools updated to expose multi-embedding functionality

---

*Generated by Claude Code*
