# 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 %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.mozilla.ai/api-reference/embedding.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.