"""Pinecone reader."""

from typing import Any, Dict, List, Optional

from llama_index.readers.base import BaseReader
from llama_index.schema import Document


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

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

    def __init__(self, api_key: str, environment: str):
        """Initialize with parameters."""
        try:
            import pinecone  # noqa: F401
        except ImportError:
            raise ImportError(
                "`pinecone` package not found, please run `pip install pinecone-client`"
            )

        self._api_key = api_key
        self._environment = environment
        pinecone.init(api_key=api_key, environment=environment)

    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.
        """
        import pinecone

        index = pinecone.Index(index_name)
        if "include_values" not in query_kwargs:
            query_kwargs["include_values"] = True
        response = index.query(top_k=top_k, vector=vector, **query_kwargs)

        documents = []
        for match in response.matches:
            if match.id not in id_to_text_map:
                raise ValueError("ID not found in id_to_text_map.")
            text = id_to_text_map[match.id]
            embedding = match.values
            if len(embedding) == 0:
                embedding = None
            documents.append(Document(text=text, embedding=embedding))

        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
