docs(research): add farsi embedding model research knowledge base

Body:
        Why:
        - Insurance RAG pipeline needs Farsi-optimized embeddings
        - Current local baseline (Nomic Embed) is English-only

        Changes:
        - 8 cloud model profiles with curl + python test commands
        - 5 local model profiles with VRAM, serving platform notes
        - Persian benchmark landscape (MIRACL-Fa, mMARCO, FaMTE, MTEB)
        - Decision report with ranked options and recommendations
        - Full comparison table (15 models × 10 columns)

        Impact:
        - Knowledge base for embedding model selection
        - Top picks: BGE-M3 (local), Cohere embed-v4.0 (cloud)
        - Critical: Nomic Embed must be replaced (English-only)

embedding_models/voyage.md

@@ -0,0 +1,97 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, voyage-ai, cloud, farsi, insurance, rag, free-tier]
+source: import-knowledge
+---
+
+# Voyage AI — voyage-4-large & voyage-4
+
+## Overview
+
+Voyage AI's latest embedding models. Founded by OpenAI alumni. 200M free tokens — risk-free testing.
+
+## voyage-4-large *(recommended)*
+
+- **Type**: Cloud
+- **Provider**: Voyage AI
+- **Price**: $0.12 per 1M input tokens (200M free tokens included) ([source](https://docs.voyageai.com/docs/pricing))
+- **Max input tokens**: 32,000
+- **Embedding dimensions**: 1024
+- **Language coverage**: Multilingual
+- **Persian benchmark**: No public Persian-specific scores. MTEB multilingual aggregate available.
+- **Notes**: Slightly cheaper than OpenAI text-embedding-3-large ($0.12 vs $0.13). 32K context — 4× OpenAI's 8K. voyage-3-large and voyage-3 are now legacy.
+
+## voyage-4 *(mid-tier)*
+
+- **Type**: Cloud
+- **Provider**: Voyage AI
+- **Price**: $0.06 per 1M input tokens (200M free tokens included) ([source](https://docs.voyageai.com/docs/pricing))
+- **Max input tokens**: 32,000
+- **Embedding dimensions**: 1024
+- **Language coverage**: Multilingual
+- **Persian benchmark**: No public Persian-specific scores.
+
+## Pros
+
+- $0.12/1M tokens — slightly cheaper than OpenAI
+- 200M tokens free — enough for testing + months of low-volume traffic
+- 32K context — 4× OpenAI's context window
+- Strong MTEB multilingual scores
+- Simple API (OpenAI-compatible format)
+
+## Cons
+
+- No published Farsi-specific scores
+- Smaller company — less battle-tested at enterprise scale
+- Requires separate API key management
+
+## API Test — Curl
+
+**Endpoint**: `POST https://api.voyageai.com/v1/embeddings`
+**Auth**: `Authorization: Bearer $VOYAGE_API_KEY`
+**Get key**: https://voyage.ai/api-keys
+**Free tier**: 200M tokens
+
+### voyage-4-large
+
+```bash
+curl -s https://api.voyageai.com/v1/embeddings   -H "Content-Type: application/json"   -H "Authorization: Bearer $VOYAGE_API_KEY"   -d '{
+    "model": "voyage-4-large",
+    "input": ["بیمه نامه شخص ثالث چیست؟"]
+  }' | python3 -m json.tool
+```
+
+### voyage-4
+
+```bash
+curl -s https://api.voyageai.com/v1/embeddings   -H "Content-Type: application/json"   -H "Authorization: Bearer $VOYAGE_API_KEY"   -d '{
+    "model": "voyage-4",
+    "input": ["بیمه نامه شخص ثالث چیست؟"]
+  }' | python3 -m json.tool
+```
+
+**Response shape**: `data[0].embedding` = float array, `usage.total_tokens` = token count
+
+## API Test — Python
+
+```python
+import voyageai
+
+vo = voyageai.Client(api_key="YOUR_VOYAGE_API_KEY")  # or uses VOYAGE_API_KEY env var
+
+result = vo.embed(
+    ["بیمه نامه شخص ثالث چیست؟"],
+    model="voyage-4-large",
+    input_type="document"
+)
+
+embedding = result.embeddings[0]
+print(f"Dimensions: {len(embedding)}, Tokens: {result.total_tokens}")
+```
+
+## TODO
+
+- [ ] Run 100-question eval set against voyage-4-large (200M free tokens)
+- [ ] Benchmark on MIRACL-Farsi subset
+- [ ] Compare with OpenAI text-embedding-3-large on Farsi queries
+- [ ] Calculate cost at production volume