> ## 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.ollama

## OllamaEncoder Objects

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

OllamaEncoder class for generating embeddings using OLLAMA.

[https://ollama.com/search?c=embedding](https://ollama.com/search?c=embedding)

Example usage:

```python theme={null}
from semantic_router.encoders.ollama import OllamaEncoder

encoder = OllamaEncoder(base_url="http://localhost:11434")
embeddings = encoder(["document1", "document2"])
```

**Attributes**:

* `client` - An instance of the TextEmbeddingModel client.
* `type` - The type of the encoder, which is "ollama".

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

```python theme={null}
def __init__(name: Optional[str] = None,
             score_threshold: float = 0.5,
             base_url: str | None = None)
```

Initializes the OllamaEncoder.

**Arguments**:

* `name` (`str`): The name of the pre-trained model to use for embedding.
  If not provided, the default model specified in EncoderDefault will
  be used.
* `score_threshold` (`float`): The threshold for similarity scores.
* `base_url` (`str`): The API endpoint for OLLAMA.
  If not provided, it will be retrieved from the `OLLAMA_BASE_URL` environment variable.

**Raises**:

* `ValueError`: If the hosted base url is not provided properly or if the ollama
  client fails to initialize.

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

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

Generates embeddings for the given documents.

**Arguments**:

* `docs` (`List[str]`): A list of strings representing the documents to embed.

**Raises**:

* `ValueError`: If the Google AI Platform client is not initialized or if the
  API call fails.

**Returns**:

`List[List[float]]`: A list of lists, where each inner list contains the embedding values for a
document.