# ADR 0003: Dual Embedding Model Strategy

## Status

Accepted

## Context

The system operates in an environment with intermittent internet connectivity, including government-imposed blackouts lasting up to 90 days. The retrieval system must function fully offline with zero external API dependencies during these periods.

Documents and queries are bilingual (Persian/Farsi and English). Embedding models must handle both languages effectively.

## Decision

Store **two embeddings per chunk** in Qdrant using named vectors:

| Model | Use Case | Type |
|-------|----------|------|
| `text-embedding-3-large` (OpenAI) | Primary retrieval when internet available | Cloud |
| `nomic-embed-text` | Fallback during blackouts, fully local | On-prem |

### Qdrant Configuration
```python
# Named vectors in Qdrant collection
collection_config = {
    "vectors": {
        "openai": {"size": 3072, "distance": "Cosine"},
        "nomic": {"size": 768, "distance": "Cosine"}
    }
}
```

### Query-Time Behavior
- **Online:** Query with OpenAI embeddings, retrieve using `openai` vector
- **Offline:** Query with nomic embeddings, retrieve using `nomic` vector
- **A/B testing:** Can compare retrieval quality between models in real-time

## Consequences

### Positive
- **Resilience** — system works identically online and offline
- **No re-indexing** — switch models at query time, not ingestion time
- **A/B testing** — can measure retrieval quality difference between models
- **Graceful degradation** — fallback is automatic when cloud APIs unavailable

### Negative
- **2x storage** — two vectors per chunk
- **2x ingestion time** — must embed with both models during indexing
- **Complexity** — query logic must select correct vector at runtime

### Neutral
- Requires monitoring to detect when cloud APIs become unavailable
- May need to tune retrieval parameters (top_k, score threshold) per model

## Generation Layer

During offline periods, answer generation uses **Qwen 2.5** running locally. This provides:
- Bilingual support (Persian/English)
- Fully on-prem inference
- No external API dependencies

## Alternatives Considered

1. **Single model (nomic only)** — simpler but loses quality of `text-embedding-3-large` when online
2. **Single model (multilingual-e5)** — good bilingual support but not tested in production
3. **Re-embed on switch** — would require re-indexing entire corpus during blackout (impractical)