DashScope

DashScopeEmbedding

基类: MultiModalEmbedding

用于文本嵌入的 DashScope 类。

参数

名称	类型	描述	默认值
model_name	str	嵌入的模型名称。默认为 DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2。选项包括： - DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V1 - DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2	TEXT_EMBEDDING_V2
text_type	str	输入类型，['query', 'document']。对于检索等非对称任务，为了获得更好的检索结果，建议区分查询文本 (query) 和基础文本 (document) 类型，分类等对称任务无需特别指定，可使用系统默认值 "document"。	'document'
api_key	str	DashScope API 密钥。	无

源代码位于 llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py

class DashScopeEmbedding(MultiModalEmbedding):
    """
    DashScope class for text embedding.

    Args:
        model_name (str): Model name for embedding.
            Defaults to DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2.
                Options are:

                - DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V1
                - DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2
        text_type (str): The input type, ['query', 'document'],
            For asymmetric tasks such as retrieval, in order to achieve better
            retrieval results, it is recommended to distinguish between query
            text (query) and base text (document) types, clustering Symmetric
            tasks such as classification and classification do not need to
            be specially specified, and the system default
            value "document" can be used.
        api_key (str): The DashScope api key.

    """

    _api_key: Optional[str] = PrivateAttr()
    _text_type: Optional[str] = PrivateAttr()

    def __init__(
        self,
        model_name: str = DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
        text_type: str = "document",
        api_key: Optional[str] = None,
        embed_batch_size: int = EMBED_MAX_BATCH_SIZE,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            model_name=model_name,
            embed_batch_size=embed_batch_size,
            **kwargs,
        )
        self._api_key = api_key
        self._text_type = text_type

    @classmethod
    def class_name(cls) -> str:
        return "DashScopeEmbedding"

    def _get_query_embedding(self, query: str) -> List[float]:
        """Get query embedding."""
        emb = get_text_embedding(
            self.model_name,
            query,
            api_key=self._api_key,
            text_type="query",
        )
        if len(emb) > 0 and emb[0] is not None:
            return emb[0]
        else:
            return []

    def _get_text_embedding(self, text: str) -> List[float]:
        """Get text embedding."""
        emb = get_text_embedding(
            self.model_name,
            text,
            api_key=self._api_key,
            text_type=self._text_type,
        )
        if len(emb) > 0 and emb[0] is not None:
            return emb[0]
        else:
            return []

    def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
        """Get text embeddings."""
        return get_text_embedding(
            self.model_name,
            texts,
            api_key=self._api_key,
            text_type=self._text_type,
        )

    # TODO: use proper async methods
    async def _aget_text_embedding(self, query: str) -> List[float]:
        """Get text embedding."""
        return self._get_text_embedding(query)

    # TODO: user proper async methods
    async def _aget_query_embedding(self, query: str) -> List[float]:
        """Get query embedding."""
        return self._get_query_embedding(query)

    def get_batch_query_embedding(self, embedding_file_url: str) -> Optional[str]:
        """
        Get batch query embeddings.

        Args:
            embedding_file_url (str): The url of the file to embedding which with lines of text to embedding.

        Returns:
            str: The url of the embedding result, format ref:
                 https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details.

        """
        return get_batch_text_embedding(
            self.model_name,
            embedding_file_url,
            api_key=self._api_key,
            text_type=self._text_type,
        )

    def get_batch_text_embedding(self, embedding_file_url: str) -> Optional[str]:
        """
        Get batch text embeddings.

        Args:
            embedding_file_url (str): The url of the file to embedding which with lines of text to embedding.

        Returns:
            str: The url of the embedding result, format ref:
                 https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details.

        """
        return get_batch_text_embedding(
            self.model_name,
            embedding_file_url,
            api_key=self._api_key,
            text_type=self._text_type,
        )

    def _get_image_embedding(self, img_file_path: ImageType) -> List[float]:
        """
        Embed the input image synchronously.
        """
        input = [{"image": img_file_path}]
        return get_multimodal_embedding(
            self.model_name, input=input, api_key=self._api_key
        )

    async def _aget_image_embedding(self, img_file_path: ImageType) -> List[float]:
        """
        Embed the input image asynchronously.

        """
        return self._get_image_embedding(img_file_path=img_file_path)

    def get_multimodal_embedding(
        self, input: List[Dict], auto_truncation: bool = False
    ) -> List[float]:
        """
        Call DashScope multimodal embedding.
        ref: https://help.aliyun.com/zh/dashscope/developer-reference/one-peace-multimodal-embedding-api-details.

        Args:
            input (str): The input of the multimodal embedding, eg:
                [{'factor': 1, 'text': '你好'},
                {'factor': 2, 'audio': 'https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac'},
                {'factor': 3, 'image': 'https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png'}]

        Raises:
            ImportError: Need install dashscope package.

        Returns:
            List[float]: The embedding result

        """
        return get_multimodal_embedding(
            self.model_name,
            input=input,
            api_key=self._api_key,
            auto_truncation=auto_truncation,
        )

get_batch_query_embedding

get_batch_query_embedding(embedding_file_url: str) -> Optional[str]

获取批量查询嵌入。

参数

名称	类型	描述	默认值
embedding_file_url	str	包含待嵌入文本行的文件的 URL。	必需

返回值

名称	类型	描述
str	可选[str]	嵌入结果文件的 URL，格式参考：https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details。

源代码位于 llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py

def get_batch_query_embedding(self, embedding_file_url: str) -> Optional[str]:
    """
    Get batch query embeddings.

    Args:
        embedding_file_url (str): The url of the file to embedding which with lines of text to embedding.

    Returns:
        str: The url of the embedding result, format ref:
             https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details.

    """
    return get_batch_text_embedding(
        self.model_name,
        embedding_file_url,
        api_key=self._api_key,
        text_type=self._text_type,
    )

get_batch_text_embedding

get_batch_text_embedding(embedding_file_url: str) -> Optional[str]

获取批量文本嵌入。

参数

名称	类型	描述	默认值
embedding_file_url	str	包含待嵌入文本行的文件的 URL。	必需

返回值

名称	类型	描述
str	可选[str]	嵌入结果文件的 URL，格式参考：https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details。

源代码位于 llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py

def get_batch_text_embedding(self, embedding_file_url: str) -> Optional[str]:
    """
    Get batch text embeddings.

    Args:
        embedding_file_url (str): The url of the file to embedding which with lines of text to embedding.

    Returns:
        str: The url of the embedding result, format ref:
             https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details.

    """
    return get_batch_text_embedding(
        self.model_name,
        embedding_file_url,
        api_key=self._api_key,
        text_type=self._text_type,
    )

get_multimodal_embedding

get_multimodal_embedding(input: List[Dict], auto_truncation: bool = False) -> List[float]

调用 DashScope 多模态嵌入。参考：https://help.aliyun.com/zh/dashscope/developer-reference/one-peace-multimodal-embedding-api-details。

参数

名称	类型	描述	默认值
input	str	多模态嵌入的输入，例如：[{'factor': 1, 'text': '你好'}, {'factor': 2, 'audio': 'https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac'}, {'factor': 3, 'image': 'https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png'}]	必需

抛出

类型	描述
ImportError	需要安装 dashscope 包。

返回值

类型	描述
列表[浮点数]	List[float]: 嵌入结果

源代码位于 llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py

def get_multimodal_embedding(
    self, input: List[Dict], auto_truncation: bool = False
) -> List[float]:
    """
    Call DashScope multimodal embedding.
    ref: https://help.aliyun.com/zh/dashscope/developer-reference/one-peace-multimodal-embedding-api-details.

    Args:
        input (str): The input of the multimodal embedding, eg:
            [{'factor': 1, 'text': '你好'},
            {'factor': 2, 'audio': 'https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac'},
            {'factor': 3, 'image': 'https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png'}]

    Raises:
        ImportError: Need install dashscope package.

    Returns:
        List[float]: The embedding result

    """
    return get_multimodal_embedding(
        self.model_name,
        input=input,
        api_key=self._api_key,
        auto_truncation=auto_truncation,
    )