docs(rag): add domain model and original research input

chunking/docs/adr/0003-dual-embedding-strategy.md

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+# 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)