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/openai.md

@@ -0,0 +1,122 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, openai, cloud, farsi, insurance, rag]
+source: import-knowledge
+---
+
+# OpenAI — text-embedding-3-large & text-embedding-3-small
+
+## Overview
+
+OpenAI's embedding API is the current baseline for our RAG pipeline. Two models available: text-embedding-3-large (higher quality) and text-embedding-3-small (cheaper).
+
+## text-embedding-3-large *(current baseline)*
+
+- **Type**: Cloud
+- **Provider**: OpenAI
+- **Price**: $0.13 per 1M input tokens ([source](https://openai.com/api/pricing/))
+- **Max input tokens**: 8,191
+- **Embedding dimensions**: 256, 1024, or 3072 (configurable via `dimensions` param)
+- **Language coverage**: Multilingual (trained on multilingual corpus including Farsi)
+- **Persian benchmark**: No public Persian-specific retrieval benchmark score published by OpenAI. MTEB aggregate available; MIRACL-Farsi subtask scores exist on the MTEB leaderboard but not officially published by OpenAI.
+- **Notes**: Good general multilingual performance but not specifically optimized for Farsi. Pricing is mid-range among cloud options. Already integrated — zero migration cost.
+
+## text-embedding-3-small *(cheaper alternative)*
+
+- **Type**: Cloud
+- **Provider**: OpenAI
+- **Price**: $0.02 per 1M input tokens ([source](https://openai.com/api/pricing/))
+- **Max input tokens**: 8,191
+- **Embedding dimensions**: 512 or 1536 (configurable)
+- **Language coverage**: Multilingual
+- **Persian benchmark**: No public Persian-specific scores.
+- **Notes**: 6.5× cheaper than text-embedding-3-large but generally lower retrieval quality. Useful as a cost-optimized fallback, not as a quality upgrade.
+
+## API Test — Curl
+
+**Endpoint**: `POST https://api.openai.com/v1/embeddings`
+**Auth**: `Authorization: Bearer $OPENAI_API_KEY`
+**Get key**: https://platform.openai.com/api-keys
+
+### text-embedding-3-large
+
+```bash
+curl -s https://api.openai.com/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $OPENAI_API_KEY" \
+  -d '{
+    "model": "text-embedding-3-large",
+    "input": ["بیمه نامه شخص ثالث چیست؟"]
+  }' | python3 -m json.tool
+```
+
+### text-embedding-3-small
+
+```bash
+curl -s https://api.openai.com/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $OPENAI_API_KEY" \
+  -d '{
+    "model": "text-embedding-3-small",
+    "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
+from openai import OpenAI
+
+client = OpenAI()  # uses OPENAI_API_KEY env var
+
+# Test with a Farsi insurance query
+response = client.embeddings.create(
+    model="text-embedding-3-large",
+    input=["بیمه نامه شخص ثالث چیست؟"]
+)
+
+embedding = response.data[0].embedding
+tokens = response.usage.total_tokens
+print(f"Dimensions: {len(embedding)}, Tokens: {tokens}")
+# Expected: Dimensions: 3072, Tokens: 6
+```
+
+## Python Test — Compare Both Models
+
+```python
+from openai import OpenAI
+import time
+
+client = OpenAI()
+query = "شرایط لغو بیمه نامه چیست؟"
+
+for model in ["text-embedding-3-large", "text-embedding-3-small"]:
+    start = time.time()
+    resp = client.embeddings.create(model=model, input=[query])
+    elapsed = time.time() - start
+    dims = len(resp.data[0].embedding)
+    tokens = resp.usage.total_tokens
+    print(f"{model}: dims={dims}, tokens={tokens}, time={elapsed:.2f}s")
+```
+
+## Response Format
+
+| Field | Path |
+|-------|------|
+| Embedding vector | `data[0].embedding` |
+| Token count | `usage.total_tokens` |
+
+## Key Differences from Other Providers
+
+- Uses standard `input` field (string or array)
+- Configurable dimensions — can reduce from 3072 to 1024 or 256 to save storage/latency
+- 8K context limit — may require aggressive chunking for long insurance documents
+- Data sent to US servers — not on-premises
+
+## TODO
+
+- [ ] Benchmark on MIRACL-Farsi subset
+- [ ] Test dimension reduction (3072 → 1024) impact on retrieval quality
+- [ ] Calculate monthly cost at production volume