---
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