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

# semantic_router.encoders.openai

## OpenAIEncoder Objects

```python theme={null}
class OpenAIEncoder(DenseEncoder)
```

OpenAI encoder class for generating embeddings using OpenAI API.

The OpenAIEncoder class is a subclass of DenseEncoder and utilizes the OpenAI API
to generate embeddings for given documents. It requires an OpenAI API key and
supports customization of the score threshold for filtering or processing the embeddings.

#### token\_limit

default value, should be replaced by config

#### \_\_init\_\_

```python theme={null}
def __init__(name: Optional[str] = None,
             openai_base_url: Optional[str] = None,
             openai_api_key: Optional[str] = None,
             openai_org_id: Optional[str] = None,
             score_threshold: Optional[float] = None,
             dimensions: Union[int, NotGiven] = NotGiven(),
             max_retries: int = 3)
```

Initialize the OpenAIEncoder.

**Arguments**:

* `name` (`str`): The name of the embedding model to use.
* `openai_base_url` (`str`): The base URL for the OpenAI API.
* `openai_api_key` (`str`): The OpenAI API key.
* `openai_org_id` (`str`): The OpenAI organization ID.
* `score_threshold` (`float`): The score threshold for the embeddings.
* `str`0 (`str`1): The dimensions of the embeddings.
* `str`2 (`str`1): The maximum number of retries for the OpenAI API call.

#### \_\_call\_\_

```python theme={null}
def __call__(docs: List[str], truncate: bool = True) -> List[List[float]]
```

Encode a list of text documents into embeddings using OpenAI API.

**Arguments**:

* `docs`: List of text documents to encode.
* `truncate`: Whether to truncate the documents to token limit. If
  False and a document exceeds the token limit, an error will be
  raised.

**Returns**:

List of embeddings for each document.

#### acall

```python theme={null}
async def acall(docs: List[str], truncate: bool = True) -> List[List[float]]
```

Encode a list of text documents into embeddings using OpenAI API asynchronously.

**Arguments**:

* `docs`: List of text documents to encode.
* `truncate`: Whether to truncate the documents to token limit. If
  False and a document exceeds the token limit, an error will be
  raised.

**Returns**:

List of embeddings for each document.