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Endpoint Based

EndpointEmbeddings

Bases: BaseEmbeddings

An Embeddings component that uses an OpenAI API compatible endpoint.

Attributes:

Name Type Description
endpoint_url str

The url of an OpenAI API compatible endpoint.
Source code in libs\kotaemon\kotaemon\embeddings\endpoint_based.py 
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class EndpointEmbeddings(BaseEmbeddings):
    """
    An Embeddings component that uses an OpenAI API compatible endpoint.

    Attributes:
        endpoint_url (str): The url of an OpenAI API compatible endpoint.
    """

    endpoint_url: str

    def run(
        self, text: str | list[str] | Document | list[Document]
    ) -> list[DocumentWithEmbedding]:
        """
        Generate embeddings from text Args:
            text (str | list[str] | Document | list[Document]): text to generate
            embeddings from
        Returns:
            list[DocumentWithEmbedding]: embeddings
        """
        if not isinstance(text, list):
            text = [text]

        outputs = []

        for item in text:
            response = requests.post(
                self.endpoint_url, json={"input": str(item)}
            ).json()
            outputs.append(
                DocumentWithEmbedding(
                    text=str(item),
                    embedding=response["data"][0]["embedding"],
                    total_tokens=response["usage"]["total_tokens"],
                    prompt_tokens=response["usage"]["prompt_tokens"],
                )
            )

        return outputs

run 

1
run(text)
Generate embeddings from text Args

text (str | list[str] | Document | list[Document]): text to generate embeddings from

Returns: list[DocumentWithEmbedding]: embeddings

Source code in libs\kotaemon\kotaemon\embeddings\endpoint_based.py 
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def run(
    self, text: str | list[str] | Document | list[Document]
) -> list[DocumentWithEmbedding]:
    """
    Generate embeddings from text Args:
        text (str | list[str] | Document | list[Document]): text to generate
        embeddings from
    Returns:
        list[DocumentWithEmbedding]: embeddings
    """
    if not isinstance(text, list):
        text = [text]

    outputs = []

    for item in text:
        response = requests.post(
            self.endpoint_url, json={"input": str(item)}
        ).json()
        outputs.append(
            DocumentWithEmbedding(
                text=str(item),
                embedding=response["data"][0]["embedding"],
                total_tokens=response["usage"]["total_tokens"],
                prompt_tokens=response["usage"]["prompt_tokens"],
            )
        )

    return outputs