> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kitefishai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> API reference for client.embeddings

## `client.embeddings.create()`

Generate embeddings for one or more input strings.

```python theme={null}
result = client.embeddings.create(
    model="minnow-em-v1",
    input="query: what is DPDP?",
)
```

### Parameters

<ParamField path="model" type="str" required>
  Embedding model ID. Example: `"minnow-em-v1"`.
</ParamField>

<ParamField path="input" type="str | List[str]" required>
  A single string or a list of strings to embed.
</ParamField>

<ParamField path="dimensions" type="int" optional>
  Request a specific MRL dimension. Supported values: `896`, `512`, `256`, `128`, `64`.
  Defaults to the model's native dimension (`896` for `minnow-em-v1`).
</ParamField>

<ParamField path="encoding_format" type="str" optional>
  `"float"` (default) or `"base64"`.
</ParamField>

<ParamField path="extra" type="dict" optional>
  Any additional parameters passed through to the API.
</ParamField>

### Returns: `EmbeddingResponse`

| Field   | Type              | Description               |
| ------- | ----------------- | ------------------------- |
| `model` | `str`             | Model used                |
| `data`  | `List[Embedding]` | One item per input string |
| `usage` | `Usage` or None   | Token counts              |

Each `Embedding`:

| Field       | Type          | Description                |
| ----------- | ------------- | -------------------------- |
| `index`     | `int`         | Position in the input list |
| `embedding` | `List[float]` | The dense embedding vector |
| `object`    | `str`         | Always `"embedding"`       |

### Example — batch with MRL

```python theme={null}
result = client.embeddings.create(
    model="minnow-em-v1",
    input=[
        "query: insurance claim process",
        "passage: Claims must be filed within 30 days...",
    ],
    dimensions=256,
)

for item in result.data:
    print(f"[{item.index}] dim={len(item.embedding)}")
```