Metal

MetalReader

基类：BaseReader

Metal 读取器。

参数

名称	类型	描述	默认值
api_key	str	Metal API 密钥。	必需
client_id	str	Metal 客户端 ID。	必需
index_id	str	Metal 索引 ID。	必需

源代码位于 llama-index-integrations/readers/llama-index-readers-metal/llama_index/readers/metal/base.py

class MetalReader(BaseReader):
    """
    Metal reader.

    Args:
        api_key (str): Metal API key.
        client_id (str): Metal client ID.
        index_id (str): Metal index ID.

    """

    def __init__(self, api_key: str, client_id: str, index_id: str):
        import_err_msg = (
            "`metal_sdk` package not found, please run `pip install metal_sdk`"
        )
        try:
            import metal_sdk  # noqa
        except ImportError:
            raise ImportError(import_err_msg)
        from metal_sdk.metal import Metal

        """Initialize with parameters."""
        self._api_key = api_key
        self._client_id = client_id
        self._index_id = index_id
        self.metal_client = Metal(api_key, client_id, index_id)

    def load_data(
        self,
        limit: int,
        query_embedding: Optional[List[float]] = None,
        filters: Optional[Dict[str, Any]] = None,
        separate_documents: bool = True,
        **query_kwargs: Any
    ) -> List[Document]:
        """
        Load data from Metal.

        Args:
            query_embedding (Optional[List[float]]): Query embedding for search.
            limit (int): Number of results to return.
            filters (Optional[Dict[str, Any]]): Filters to apply to the search.
            separate_documents (Optional[bool]): Whether to return separate
                documents per retrieved entry. Defaults to True.
            **query_kwargs: Keyword arguments to pass to the search.

        Returns:
            List[Document]: A list of documents.

        """
        payload = {
            "embedding": query_embedding,
            "filters": filters,
        }
        response = self.metal_client.search(payload, limit=limit, **query_kwargs)

        documents = []
        for item in response["data"]:
            text = item["text"] or (item["metadata"] and item["metadata"]["text"])
            documents.append(Document(text=text))

        if not separate_documents:
            text_list = [doc.get_content() for doc in documents]
            text = "\n\n".join(text_list)
            documents = [Document(text=text)]

        return documents

load_data

load_data(limit: int, query_embedding: Optional[List[float]] = None, filters: Optional[Dict[str, Any]] = None, separate_documents: bool = True, **query_kwargs: Any) -> List[Document]

从 Metal 加载数据。

参数

名称	类型	描述	默认值
query_embedding	Optional[List[float]]	用于搜索的查询嵌入。	无
limit	int	返回结果的数量。	必需
filters	Optional[Dict[str, Any]]	应用于搜索的过滤器。	无
separate_documents	Optional[bool]]	是否为每个检索到的条目返回单独的文档。默认为 True。	True
**query_kwargs	Any	传递给搜索的关键字参数。	{}

返回

类型	描述
List[Document]]	List[Document]: 文档列表。

源代码位于 llama-index-integrations/readers/llama-index-readers-metal/llama_index/readers/metal/base.py

def load_data(
    self,
    limit: int,
    query_embedding: Optional[List[float]] = None,
    filters: Optional[Dict[str, Any]] = None,
    separate_documents: bool = True,
    **query_kwargs: Any
) -> List[Document]:
    """
    Load data from Metal.

    Args:
        query_embedding (Optional[List[float]]): Query embedding for search.
        limit (int): Number of results to return.
        filters (Optional[Dict[str, Any]]): Filters to apply to the search.
        separate_documents (Optional[bool]): Whether to return separate
            documents per retrieved entry. Defaults to True.
        **query_kwargs: Keyword arguments to pass to the search.

    Returns:
        List[Document]: A list of documents.

    """
    payload = {
        "embedding": query_embedding,
        "filters": filters,
    }
    response = self.metal_client.search(payload, limit=limit, **query_kwargs)

    documents = []
    for item in response["data"]:
        text = item["text"] or (item["metadata"] and item["metadata"]["text"])
        documents.append(Document(text=text))

    if not separate_documents:
        text_list = [doc.get_content() for doc in documents]
        text = "\n\n".join(text_list)
        documents = [Document(text=text)]

    return documents