Mahdi Bazrafshan
6c9cebaaa0
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)
112 lines
3.5 KiB
Markdown
112 lines
3.5 KiB
Markdown
---
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last_updated: 2026-07-20
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tags: [embedding, jina, cloud, local, farsi, insurance, rag, cheap]
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source: import-knowledge
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---
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# Jina AI — jina-embeddings-v3
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## Overview
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Available as both cloud API and local model. Cheapest cloud option with explicit Farsi support. **⚠️ Local model has non-commercial license (CC-BY-NC-4.0).**
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## Cloud API
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- **Type**: Cloud
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- **Provider**: Jina AI
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- **Price**: ~$0.02 per 1M tokens ([source](https://jina.ai/pricing/))
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- **Max input tokens**: 8,192
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- **Embedding dimensions**: 1024 (Matryoshka — resizable)
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- **Language coverage**: 89 languages including Farsi
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- **Persian benchmark**: No public Persian-specific scores. Strong MTEB multilingual.
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## Local Model
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- **Parameters**: ~568M
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- **VRAM (fp16)**: ~1.1 GB
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- **License**: CC-BY-NC-4.0 ⚠️ (non-commercial!) — **verify for enterprise use**
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- **Model card**: https://huggingface.co/jinaai/jina-embeddings-v3
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## Pros
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- Cheapest cloud option: ~$0.02/1M tokens
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- 89 languages including Farsi
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- Task-specific adapters (retrieval, clustering, classification)
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- Matryoshka support (resizable dimensions)
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- 8K context
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- ColBERT/late interaction support
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## Cons
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- Local model is CC-BY-NC (non-commercial) — verify for enterprise
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- Smaller company — less enterprise track record
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- Pricing page not transparent (SPA, not standard display)
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## API Test — Curl
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**Endpoint**: `POST https://api.jina.ai/v1/embeddings`
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**Auth**: `Authorization: Bearer $JINA_API_KEY`
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**Get key**: https://jina.ai/api-keys
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### For queries
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```bash
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curl -s https://api.jina.ai/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $JINA_API_KEY" -d '{
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"model": "jina-embeddings-v3",
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"input": ["بیمه نامه شخص ثالث چیست؟"],
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"task": "retrieval.query"
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}' | python3 -m json.tool
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```
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### For document chunks (indexing)
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```bash
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curl -s https://api.jina.ai/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $JINA_API_KEY" -d '{
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"model": "jina-embeddings-v3",
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"input": ["بیمه نامه شخص ثالث شامل پوشش خسارات مالی و جانی است."],
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"task": "retrieval.passage"
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}' | python3 -m json.tool
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```
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### With Matryoshka dimension reduction
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```bash
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curl -s https://api.jina.ai/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $JINA_API_KEY" -d '{
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"model": "jina-embeddings-v3",
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"input": ["بیمه نامه شخص ثالث چیست؟"],
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"task": "retrieval.query",
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"dimensions": 512
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}' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["data"][0]["embedding"])}')"
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```
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**Key differences from OpenAI**:
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- Use `task: "retrieval.query"` for queries, `task: "retrieval.passage"` for chunks
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- Optional `dimensions` param for Matryoshka truncation
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- Optional `late_chunking: true` for late-interaction-style results
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**Response shape**: `data[0].embedding` = float array, `usage.total_tokens` = token count
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## API Test — Python
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```python
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from jina import Client
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client = Client(api_key="YOUR_JINA_API_KEY") # or uses JINA_API_KEY env var
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result = client.embed(
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inputs=["بیمه نامه شخص ثالث چیست?"],
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model="jina-embeddings-v3",
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task="retrieval.query"
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)
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embedding = result.embeddings[0].embedding
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print(f"Dimensions: {len(embedding)}")
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```
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## TODO
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- [ ] Verify enterprise licensing terms for local model (CC-BY-NC)
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- [ ] Test cloud API with 100-question eval set
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- [ ] Benchmark on MIRACL-Farsi subset
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- [ ] Compare with OpenAI and Cohere on Farsi queries
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