Generate vector embeddings for text using open-source embedding models. The API is OpenAI-compatible—use the same `client.embeddings.create` interface.

## Endpoint

```plaintext
POST https://api.parasail.io/v1/embeddings
```

## Authentication

Pass your API key in the `Authorization` header:

```plaintext
Authorization: Bearer <PARASAIL_API_KEY>
```

The OpenAI SDK reads this from the `api_key` argument. See [Authentication](https://docs.parasail.io/parasail-docs/api-reference/authentication) for how to create and store a key.

## Request

### Python (OpenAI SDK)

```python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["PARASAIL_API_KEY"],
)

response = client.embeddings.create(
    model="parasail-ai/GritLM-7B-vllm",
    input="Parasail provides affordable cloud GPUs for AI inference.",
    encoding_format="base64"
)

print(response.data[0].embedding[:5])
```

### cURL

```bash
curl https://api.parasail.io/v1/embeddings \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "parasail-ai/GritLM-7B-vllm",
    "input": "Parasail provides affordable cloud GPUs for AI inference.",
    "encoding_format": "float"
  }'
```

## Parameters

| Parameter    | Type                    | Default  | Description                                                                                   |
|--------------|-------------------------|----------|-----------------------------------------------------------------------------------------------|
| `model`     | string                  | required | Embedding model ID (for example, `parasail-ai/GritLM-7B-vllm`)                             |
| `input`     | string or array         | required | Text to embed. Pass an array to embed multiple inputs in one request                         |
| `encoding_format` | string            | "float" | Either "float" or "base64" (recommended for smaller payloads)                          |

These fields follow the OpenAI schema. For exhaustive field semantics, see the [OpenAI embeddings reference](https://platform.openai.com/docs/api-reference/embeddings/create).

## Response

A successful request returns a list of embedding objects. With one input string, `data` contains a single embedding:

```json
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0145, "..."]
    }
  ],
  "model": "parasail-ai/GritLM-7B-vllm",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}
```

### Field Descriptions

- `data[].embedding` - The vector (array of floats, or a base64 string when `encoding_format="base64"`)
- `data[].index` - Position of this embedding in the input array
- `usage` - Prompt and total token counts

When `input` is an array, `data` contains one object per input, ordered by `index`.

## Available embedding models

| Model                              | HuggingFace ID                  |
|------------------------------------|----------------------------------|
| GritLM                             | `parasail-ai/GritLM-7B-vllm`   |
| GTE-Qwen2-7B-Instruct              | `Alibaba-NLP/gte-Qwen2-7B-instruct` |

Any embedding model on HuggingFace can be used with dedicated or batch endpoints.

## Error responses

Errors use OpenAI-compatible status codes and a JSON body with an `error` object (`message`, `type`, `code`).

| Status Code | Description                                                                  |
|-------------|------------------------------------------------------------------------------|
| `400`      | Malformed request—invalid JSON, missing `input`, or unsupported parameter value |
| `401`      | Missing or invalid API key                                                  |
| `403`      | API key lacks access to the requested model                                  |
| `404`      | Model or endpoint not found                                                  |
| `429`      | Rate limit or quota exceeded—back off and retry                             |
| `500`      | Internal server error—retry with exponential backoff                        |

## Tips

- Use `encoding_format="base64"` to reduce output file sizes, especially for batch processing.
- For large-scale embedding generation, use [batch processing](https://docs.parasail.io/parasail-docs/products/quickstart) at 50% off serverless pricing.