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/google.md

@@ -0,0 +1,99 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, google, vertex-ai, cloud, farsi, insurance, rag]
+source: import-knowledge
+---
+
+# Google Vertex AI — Embedding Models
+
+## Models
+
+### Gemini Embedding *(newest)*
+- **Type**: Cloud
+- **Provider**: Google Vertex AI
+- **Price**: $0.15 per 1M input tokens (online) / $0.12 per 1M (batch) ([source](https://cloud.google.com/vertex-ai/generative-ai/pricing))
+- **Max input tokens**: 8,192
+- **Embedding dimensions**: 768
+- **Language coverage**: Multilingual (Gemini family)
+
+### text-embedding-004
+- **Price**: ~$0.10 per 1M tokens equivalent ($0.025 per 1,000 characters) ([source](https://cloud.google.com/vertex-ai/generative-ai/pricing))
+- **Language coverage**: English-primary with some multilingual
+
+### text-multilingual-embedding-002 *(Farsi-relevant)*
+- **Price**: ~$0.10 per 1M tokens equivalent (same pricing as above)
+- **Language coverage**: Multilingual — this is the relevant variant for Farsi
+
+**Note**: Legacy Google embedding models priced per character, not per token (~4 chars/token).
+
+## Pros
+
+- Google ecosystem integration
+- Batch pricing available (20% discount)
+- Gemini Embedding is Google's latest
+
+## Cons
+
+- Most complex setup (requires GCP project, billing, Vertex AI API)
+- No Farsi-specific scores published
+- Per-character pricing for legacy models (confusing)
+- $0.15/1M tokens for Gemini Embedding — most expensive option
+
+## API Test — Curl
+
+**Auth**: `gcloud auth print-access-token` or service account JSON
+**Setup**: Requires GCP project with Vertex AI API enabled
+
+### Gemini Embedding
+
+```bash
+export GCP_TOKEN=$(gcloud auth print-access-token)
+
+curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/us-central1/publishers/google/models/gemini-embedding-exp-03-07:predict"   -H "Content-Type: application/json"   -H "Authorization: Bearer $GCP_TOKEN"   -d '{
+    "instances": [
+      {"content": "بیمه نامه شخص ثالث چیست؟"}
+    ]
+  }' | python3 -m json.tool
+```
+
+### text-multilingual-embedding-002
+
+```bash
+curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/us-central1/publishers/google/models/text-multilingual-embedding-002:predict"   -H "Content-Type: application/json"   -H "Authorization: Bearer $GCP_TOKEN"   -d '{
+    "instances": [
+      {"content": "بیمه نامه شخص ثالث چیست؟"}
+    ]
+  }' | python3 -m json.tool
+```
+
+### text-embedding-004
+
+```bash
+curl -s "https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/us-central1/publishers/google/models/text-embedding-004:predict"   -H "Content-Type: application/json"   -H "Authorization: Bearer $GCP_TOKEN"   -d '{
+    "instances": [
+      {"content": "بیمه نامه شخص ثالث چیست؟"}
+    ]
+  }' | python3 -m json.tool
+```
+
+**Response shape**: `predictions[0].embeddings.values` = float array (different from OpenAI!)
+
+## API Test — Python
+
+```python
+from google.cloud import aiplatform
+
+aiplatform.init(project="YOUR_PROJECT_ID", location="us-central1")
+
+model = aiplatform.Endpoint("projects/YOUR_PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID")
+
+response = model.predict(instances=[{"content": "بیمه نامه شخص ثالث چیست؟"}])
+embedding = response.predictions[0]["embeddings"]["values"]
+print(f"Dimensions: {len(embedding)}")
+```
+
+## TODO
+
+- [ ] Set up GCP project if not already available
+- [ ] Benchmark text-multilingual-embedding-002 on Farsi queries
+- [ ] Compare with OpenAI on Farsi retrieval quality