Pinecone

PineconeReader

Bases: BaseReader

Pinecone reader.

Parameters:

Name	Type	Description	Default
api_key	str	Pinecone API key.	required
environment	str	Pinecone environment.	None

Source code in llama-index-integrations/readers/llama-index-readers-pinecone/llama_index/readers/pinecone/base.py

class PineconeReader(BaseReader):
    """Pinecone reader.

    Args:
        api_key (str): Pinecone API key.
        environment (str): Pinecone environment.
    """

    def __init__(self, api_key: str, environment: Optional[str] = None) -> None:
        """Initialize with parameters."""
        raise NotImplementedError(
            "PineconeReader has been deprecated. Please use `PineconeVectorStore` instead."
        )

    def load_data(
        self,
        index_name: str,
        id_to_text_map: Dict[str, str],
        vector: Optional[List[float]],
        top_k: int,
        separate_documents: bool = True,
        include_values: bool = True,
        **query_kwargs: Any
    ) -> List[Document]:
        """Load data from Pinecone.

        Args:
            index_name (str): Name of the index.
            id_to_text_map (Dict[str, str]): A map from ID's to text.
            separate_documents (Optional[bool]): Whether to return separate
                documents per retrieved entry. Defaults to True.
            vector (List[float]): Query vector.
            top_k (int): Number of results to return.
            include_values (bool): Whether to include the embedding in the response.
                Defaults to True.
            **query_kwargs: Keyword arguments to pass to the query.
                Arguments are the exact same as those found in
                Pinecone's reference documentation for the
                query method.

        Returns:
            List[Document]: A list of documents.
        """
        raise NotImplementedError(
            "PineconeReader has been deprecated. Please use `PineconeVectorStore` instead."
        )

load_data

load_data(index_name: str, id_to_text_map: Dict[str, str], vector: Optional[List[float]], top_k: int, separate_documents: bool = True, include_values: bool = True, **query_kwargs: Any) -> List[Document]

Load data from Pinecone.

Parameters:

Name	Type	Description	Default
index_name	str	Name of the index.	required
id_to_text_map	Dict[str, str]	A map from ID's to text.	required
separate_documents	Optional[bool]	Whether to return separate documents per retrieved entry. Defaults to True.	True
vector	List[float]	Query vector.	required
top_k	int	Number of results to return.	required
include_values	bool	Whether to include the embedding in the response. Defaults to True.	True
**query_kwargs	Any	Keyword arguments to pass to the query. Arguments are the exact same as those found in Pinecone's reference documentation for the query method.	{}

Returns:

Type	Description
List[Document]	List[Document]: A list of documents.

Source code in llama-index-integrations/readers/llama-index-readers-pinecone/llama_index/readers/pinecone/base.py

def load_data(
    self,
    index_name: str,
    id_to_text_map: Dict[str, str],
    vector: Optional[List[float]],
    top_k: int,
    separate_documents: bool = True,
    include_values: bool = True,
    **query_kwargs: Any
) -> List[Document]:
    """Load data from Pinecone.

    Args:
        index_name (str): Name of the index.
        id_to_text_map (Dict[str, str]): A map from ID's to text.
        separate_documents (Optional[bool]): Whether to return separate
            documents per retrieved entry. Defaults to True.
        vector (List[float]): Query vector.
        top_k (int): Number of results to return.
        include_values (bool): Whether to include the embedding in the response.
            Defaults to True.
        **query_kwargs: Keyword arguments to pass to the query.
            Arguments are the exact same as those found in
            Pinecone's reference documentation for the
            query method.

    Returns:
        List[Document]: A list of documents.
    """
    raise NotImplementedError(
        "PineconeReader has been deprecated. Please use `PineconeVectorStore` instead."
    )