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JinaEmbedding

class JinaEmbedding:

Provides text and image embedding functionalities using Jina AI’s API.

Parameters:

  • model_type (EmbeddingModelType, optional): The model to use for embeddings. (default: :obj:JINA_EMBEDDINGS_V3)
  • api_key (Optional[str], optional): The API key for authenticating with Jina AI. (default: :obj:None)
  • dimensions (Optional[int], optional): The dimension of the output embeddings. (default: :obj:None)
  • embedding_type (Optional[str], optional): The type of embedding format to generate. Options: ‘int8’ (binary encoding with higher storage and transfer efficiency), ‘uint8’ (unsigned binary encoding with higher storage and transfer efficiency), ‘base64’ (base64 string encoding with higher transfer efficiency). (default: :obj:None)
  • task (Optional[str], optional): The type of task for text embeddings.
  • Options: retrieval.query, retrieval.passage, text-matching, classification, separation. (default: :obj:None)
  • late_chunking (bool, optional): If true, concatenates all sentences in input and treats as a single input. (default: :obj:False)
  • normalized (bool, optional): If true, embeddings are normalized to unit L2 norm. (default: :obj:False)

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init

def __init__(
    self,
    model_type: EmbeddingModelType = EmbeddingModelType.JINA_EMBEDDINGS_V3,
    api_key: Optional[str] = None,
    dimensions: Optional[int] = None,
    embedding_type: Optional[str] = None,
    task: Optional[str] = None,
    late_chunking: bool = False,
    normalized: bool = False
):

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embed_list

def embed_list(self, objs: list[Union[str, Image.Image]], **kwargs: Any):

Generates embeddings for the given texts or images.

Parameters:

  • objs (list[Union[str, Image.Image]]): The texts or images for which to generate the embeddings. **kwargs (Any): Extra kwargs passed to the embedding API. Not used in this implementation.

Returns:

list[list[float]]: A list that represents the generated embedding as a list of floating-point numbers.

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get_output_dim

def get_output_dim(self):

Returns:

int: The dimensionality of the embedding for the current model.