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OllamaEncoder Objects

class OllamaEncoder(DenseEncoder)
OllamaEncoder class for generating embeddings using OLLAMA. https://ollama.com/search?c=embedding Example usage: 
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”.

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__init__

def __init__(name: Optional[str] = None,
             score_threshold: float = 0.5,
             base_url: str | None = None)
Initializes the OllamaEncoder. Arguments:
  • model_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.

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__call__

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.