# Brain Query Interface — Phase 6.3 Complete

## Summary

Built the Brain Query Interface for natural language search across family documents with temporal weighting.

## Files Created

### Core Module (`brain/`)

```
brain/
├── __init__.py          # Module exports
├── embeddings.py        # Document chunking and Ollama embeddings
├── query.py             # Pattern detection and temporal scoring
├── store.py             # ChromaDB integration
├── router.py            # FastAPI endpoints
├── seed.py              # Test data seeding for Miller family
└── test_brain.py        # Unit tests
```

## Architecture

```
User query → Pattern detection → Embedding → ChromaDB search → Temporal re-rank → Synthesize answer
```

### Components

1. **Pattern Detection** (`query.py`)
   - `LOGISTICAL`: Time-sensitive (schedules, current events)
   - `ENTITY`: Fact lookup (sizes, dates, values)
   - `HISTORICAL`: Past events (roofer names, old invoices)

2. **Temporal Weighting** (`query.py`)
   - Logistical: Exponential decay (7-day half-life)
   - Entity: Linear decay (90-day half-life)
   - Historical: No decay

3. **Vector Search** (`store.py`)
   - ChromaDB with `nomic-embed-text` embeddings via Ollama
   - Combined scoring: 70% semantic + 30% temporal

4. **API** (`router.py`)
   - `POST /api/brain/query` — Natural language query
   - `GET /api/brain/stats` — Collection statistics
   - `GET /api/brain/health` — Health check
   - `GET /api/brain/patterns` — Available patterns
   - `POST /api/brain/admin/documents` — Add documents

## Test Data

Seeded with 7 Miller family documents:
- `newsletter_march_2026` — School newsletter
- `email_principal_march_2026` — Schedule update email
- `invoice_hvac_2026` — HVAC service
- `invoice_dance_2026` — Dance academy
- `receipt_vet_2026` — Vet visit receipt
- `roofer_invoice_2024` — Historical roofing
- `calendar_2025_2026` — School calendar

## Integration

Updated `main_v2.py`:
```python
from brain import brain_router as brain_query_router
family_app.include_router(brain_query_router, tags=["brain-query"])
```

## Verification

All tests pass:
```bash
pytest brain/test_brain.py -v
# 16 passed, 1 deselected
```

Example queries work:
- "Is Friday a half-day?" → Returns March email (logistical pattern)
- "What size is our HVAC filter?" → Returns 16x25x4 (entity pattern)
- "What was the roofer's name?" → Returns Madison Roofing Co. (historical pattern)

## Dependencies

Added to `requirements.txt`:
- `chromadb>=1.5.0`

## Next Steps (Phase 7)

- LLM answer synthesis (instead of simple text extraction)
- Telegram bot integration
- Document ingestion pipeline
- Optimize chunking