> For the complete documentation index, see [llms.txt](https://docs.mozilla.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mozilla.ai/api-reference/embedding.md).

# Embedding

The `embedding` and `aembedding` functions create vector embeddings from text using a unified interface across all providers that support embeddings.

### `any_llm.embedding()`

```
def embedding(
    model: str,
    inputs: str | list[str],
    *,
    provider: str | LLMProvider | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> CreateEmbeddingResponse
```

### `any_llm.aembedding()`

Async variant with the same parameters.

```
async def aembedding(
    model: str,
    inputs: str | list[str],
    *,
    provider: str | LLMProvider | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> CreateEmbeddingResponse
```

### Parameters

| Parameter     | Type                         | Default    | Description                                                                                                                                                                                                                                                    |
| ------------- | ---------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model`       | `str`                        | *required* | Model identifier. **Recommended**: Use with separate `provider` parameter (e.g., model='gpt-4', provider='openai'). **Alternative**: Combined format 'provider:model' (e.g., 'openai:gpt-4'). Legacy format 'provider/model' is also supported but deprecated. |
| `inputs`      | `str \| list[str]`           | *required* | The input text to embed                                                                                                                                                                                                                                        |
| `provider`    | `str \| LLMProvider \| None` | None       | **Recommended**: Provider name to use for the request (e.g., 'openai', 'mistral'). When provided, the model parameter should contain only the model name.                                                                                                      |
| `api_key`     | `str \| None`                | None       | API key for the provider                                                                                                                                                                                                                                       |
| `api_base`    | `str \| None`                | None       | Base URL for the provider API                                                                                                                                                                                                                                  |
| `client_args` | `dict[str, Any] \| None`     | None       | Additional provider-specific arguments that will be passed to the provider's client instantiation.                                                                                                                                                             |
| `**kwargs`    | `Any`                        | *required* | Additional provider-specific arguments that will be passed to the provider's API call.                                                                                                                                                                         |

### Return Value

Returns a [`CreateEmbeddingResponse`](/api-reference/completion-1.md) containing:

* `data` -- list of `Embedding` objects, each with an `embedding` vector (`list[float]`) and an `index`.
* `model` -- the model used.
* `usage` -- token usage information with `prompt_tokens` and `total_tokens`.

### Usage

#### Single text

```python
from any_llm import embedding

result = embedding(
    model="text-embedding-3-small",
    provider="openai",
    inputs="Hello, world!",
)

vector = result.data[0].embedding
print(f"Dimensions: {len(vector)}")
print(f"Tokens used: {result.usage.total_tokens}")
```

#### Batch embedding

```python
result = embedding(
    model="text-embedding-3-small",
    provider="openai",
    inputs=["First sentence", "Second sentence", "Third sentence"],
)

for item in result.data:
    print(f"Index {item.index}: {len(item.embedding)} dimensions")
```

#### Async

```python
import asyncio
from any_llm import aembedding

async def main():
    result = await aembedding(
        model="text-embedding-3-small",
        provider="openai",
        inputs="Hello, world!",
    )
    print(f"Dimensions: {len(result.data[0].embedding)}")

asyncio.run(main())
```

{% hint style="info" %}
Not all providers support embeddings. Check the [providers page](/providers.md) for support details, or query `ProviderMetadata.embedding` programmatically.
{% endhint %}


---

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