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# Source code for langchain_pinecone.vectorstores

    from __future__ import annotations

    import logging
    import os
    import uuid
    from typing import (
        TYPE_CHECKING,
        Any,
        Callable,
        Iterable,
        List,
        Optional,
        Tuple,
        TypeVar,
    )

    import numpy as np
    from langchain_core._api.deprecation import deprecated
    from langchain_core.documents import Document
    from langchain_core.embeddings import Embeddings
    from langchain_core.utils.iter import batch_iterate
    from langchain_core.vectorstores import VectorStore
    from pinecone import Pinecone as PineconeClient  # type: ignore

    from langchain_pinecone._utilities import DistanceStrategy, maximal_marginal_relevance

    if TYPE_CHECKING:
        from pinecone import Index

    logger = logging.getLogger(__name__)

    VST = TypeVar("VST", bound=VectorStore)

    class PineconeVectorStore(VectorStore):
        """`Pinecone` vector store.

        Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.

        Example:
            .. code-block:: python

                from langchain_pinecone import PineconeVectorStore
                from langchain_openai import OpenAIEmbeddings

                embeddings = OpenAIEmbeddings()
                index_name = "my-index"
                namespace = "my-namespace"
                vectorstore = Pinecone(
                    index_name=index_name,
                    embedding=embedding,
                    namespace=namespace,
                )
        """

        def __init__(
            self,
            # setting default params to bypass having to pass in
            # the index and embedding objects - manually throw
            # exceptions if they are not passed in or set in environment
            # (keeping param for backwards compatibility)
            index: Optional[Any] = None,
            embedding: Optional[Embeddings] = None,
            text_key: Optional[str] = "text",
            namespace: Optional[str] = None,
            distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE,
            *,
            pinecone_api_key: Optional[str] = None,
            index_name: Optional[str] = None,
        ):
            if embedding is None:
                raise ValueError("Embedding must be provided")
            self._embedding = embedding
            if text_key is None:
                raise ValueError("Text key must be provided")
            self._text_key = text_key

            self._namespace = namespace
            self.distance_strategy = distance_strategy

            if index:
                # supports old way of initializing externally
                self._index = index
            else:
                # all internal initialization
                _pinecone_api_key = (
                    pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
                )
                if not _pinecone_api_key:
                    raise ValueError(
                        "Pinecone API key must be provided in either `pinecone_api_key` "
                        "or `PINECONE_API_KEY` environment variable"
                    )

                _index_name = index_name or os.environ.get("PINECONE_INDEX_NAME") or ""
                if not _index_name:
                    raise ValueError(
                        "Pinecone index name must be provided in either `index_name` "
                        "or `PINECONE_INDEX_NAME` environment variable"
                    )

                # needs
                client = PineconeClient(api_key=_pinecone_api_key, source_tag="langchain")
                self._index = client.Index(_index_name)

        @property
        def embeddings(self) -> Optional[Embeddings]:
            """Access the query embedding object if available."""
            return self._embedding

        def add_texts(
            self,
            texts: Iterable[str],
            metadatas: Optional[List[dict]] = None,
            ids: Optional[List[str]] = None,
            namespace: Optional[str] = None,
            batch_size: int = 32,
            embedding_chunk_size: int = 1000,
            *,
            async_req: bool = True,
            **kwargs: Any,
        ) -> List[str]:
            """Run more texts through the embeddings and add to the vectorstore.

            Upsert optimization is done by chunking the embeddings and upserting them.
            This is done to avoid memory issues and optimize using HTTP based embeddings.
            For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
            embedding_chunk_size>1000 and batch_size~64 for best performance.
            Args:
                texts: Iterable of strings to add to the vectorstore.
                metadatas: Optional list of metadatas associated with the texts.
                ids: Optional list of ids to associate with the texts.
                namespace: Optional pinecone namespace to add the texts to.
                batch_size: Batch size to use when adding the texts to the vectorstore.
                embedding_chunk_size: Chunk size to use when embedding the texts.

            Returns:
                List of ids from adding the texts into the vectorstore.

            """
            if namespace is None:
                namespace = self._namespace

            texts = list(texts)
            ids = ids or [str(uuid.uuid4()) for _ in texts]
            metadatas = metadatas or [{} for _ in texts]
            for metadata, text in zip(metadatas, texts):
                metadata[self._text_key] = text

            # For loops to avoid memory issues and optimize when using HTTP based embeddings
            # The first loop runs the embeddings, it benefits when using OpenAI embeddings
            # The second loops runs the pinecone upsert asynchronously.
            for i in range(0, len(texts), embedding_chunk_size):
                chunk_texts = texts[i : i + embedding_chunk_size]
                chunk_ids = ids[i : i + embedding_chunk_size]
                chunk_metadatas = metadatas[i : i + embedding_chunk_size]
                embeddings = self._embedding.embed_documents(chunk_texts)
                async_res = [
                    self._index.upsert(
                        vectors=batch,
                        namespace=namespace,
                        async_req=async_req,
                        **kwargs,
                    )
                    for batch in batch_iterate(
                        batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
                    )
                ]
                if async_req:
                    [res.get() for res in async_res]

            return ids

        def similarity_search_with_score(
            self,
            query: str,
            k: int = 4,
            filter: Optional[dict] = None,
            namespace: Optional[str] = None,
        ) -> List[Tuple[Document, float]]:
            """Return pinecone documents most similar to query, along with scores.

            Args:
                query: Text to look up documents similar to.
                k: Number of Documents to return. Defaults to 4.
                filter: Dictionary of argument(s) to filter on metadata
                namespace: Namespace to search in. Default will search in '' namespace.

            Returns:
                List of Documents most similar to the query and score for each
            """
            return self.similarity_search_by_vector_with_score(
                self._embedding.embed_query(query), k=k, filter=filter, namespace=namespace
            )

        def similarity_search_by_vector_with_score(
            self,
            embedding: List[float],
            *,
            k: int = 4,
            filter: Optional[dict] = None,
            namespace: Optional[str] = None,
        ) -> List[Tuple[Document, float]]:
            """Return pinecone documents most similar to embedding, along with scores."""

            if namespace is None:
                namespace = self._namespace
            docs = []
            results = self._index.query(
                vector=embedding,
                top_k=k,
                include_metadata=True,
                namespace=namespace,
                filter=filter,
            )
            for res in results["matches"]:
                metadata = res["metadata"]
                if self._text_key in metadata:
                    text = metadata.pop(self._text_key)
                    score = res["score"]
                    docs.append((Document(page_content=text, metadata=metadata), score))
                else:
                    logger.warning(
                        f"Found document with no `{self._text_key}` key. Skipping."
                    )
            return docs

        def similarity_search(
            self,
            query: str,
            k: int = 4,
            filter: Optional[dict] = None,
            namespace: Optional[str] = None,
            **kwargs: Any,
        ) -> List[Document]:
            """Return pinecone documents most similar to query.

            Args:
                query: Text to look up documents similar to.
                k: Number of Documents to return. Defaults to 4.
                filter: Dictionary of argument(s) to filter on metadata
                namespace: Namespace to search in. Default will search in '' namespace.

            Returns:
                List of Documents most similar to the query and score for each
            """
            docs_and_scores = self.similarity_search_with_score(
                query, k=k, filter=filter, namespace=namespace, **kwargs
            )
            return [doc for doc, _ in docs_and_scores]

        def _select_relevance_score_fn(self) -> Callable[[float], float]:
            """
            The 'correct' relevance function
            may differ depending on a few things, including:
            - the distance / similarity metric used by the VectorStore
            - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
            - embedding dimensionality
            - etc.
            """

            if self.distance_strategy == DistanceStrategy.COSINE:
                return self._cosine_relevance_score_fn
            elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
                return self._max_inner_product_relevance_score_fn
            elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
                return self._euclidean_relevance_score_fn
            else:
                raise ValueError(
                    "Unknown distance strategy, must be cosine, max_inner_product "
                    "(dot product), or euclidean"
                )

        @staticmethod
        def _cosine_relevance_score_fn(score: float) -> float:
            """Pinecone returns cosine similarity scores between [-1,1]"""
            return (score + 1) / 2

        def max_marginal_relevance_search_by_vector(
            self,
            embedding: List[float],
            k: int = 4,
            fetch_k: int = 20,
            lambda_mult: float = 0.5,
            filter: Optional[dict] = None,
            namespace: Optional[str] = None,
            **kwargs: Any,
        ) -> List[Document]:
            """Return docs selected using the maximal marginal relevance.

            Maximal marginal relevance optimizes for similarity to query AND diversity
            among selected documents.

            Args:
                embedding: Embedding to look up documents similar to.
                k: Number of Documents to return. Defaults to 4.
                fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                lambda_mult: Number between 0 and 1 that determines the degree
                            of diversity among the results with 0 corresponding
                            to maximum diversity and 1 to minimum diversity.
                            Defaults to 0.5.
            Returns:
                List of Documents selected by maximal marginal relevance.
            """
            if namespace is None:
                namespace = self._namespace
            results = self._index.query(
                vector=[embedding],
                top_k=fetch_k,
                include_values=True,
                include_metadata=True,
                namespace=namespace,
                filter=filter,
            )
            mmr_selected = maximal_marginal_relevance(
                np.array([embedding], dtype=np.float32),
                [item["values"] for item in results["matches"]],
                k=k,
                lambda_mult=lambda_mult,
            )
            selected = [results["matches"][i]["metadata"] for i in mmr_selected]
            return [
                Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
                for metadata in selected
            ]

        def max_marginal_relevance_search(
            self,
            query: str,
            k: int = 4,
            fetch_k: int = 20,
            lambda_mult: float = 0.5,
            filter: Optional[dict] = None,
            namespace: Optional[str] = None,
            **kwargs: Any,
        ) -> List[Document]:
            """Return docs selected using the maximal marginal relevance.

            Maximal marginal relevance optimizes for similarity to query AND diversity
            among selected documents.

            Args:
                query: Text to look up documents similar to.
                k: Number of Documents to return. Defaults to 4.
                fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                lambda_mult: Number between 0 and 1 that determines the degree
                            of diversity among the results with 0 corresponding
                            to maximum diversity and 1 to minimum diversity.
                            Defaults to 0.5.
            Returns:
                List of Documents selected by maximal marginal relevance.
            """
            embedding = self._embedding.embed_query(query)
            return self.max_marginal_relevance_search_by_vector(
                embedding, k, fetch_k, lambda_mult, filter, namespace
            )

        @classmethod
        def get_pinecone_index(
            cls,
            index_name: Optional[str],
            pool_threads: int = 4,
            *,
            pinecone_api_key: Optional[str] = None,
        ) -> Index:
            """Return a Pinecone Index instance.

            Args:
                index_name: Name of the index to use.
                pool_threads: Number of threads to use for index upsert.
            Returns:
                Pinecone Index instance."""
            _pinecone_api_key = pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
            client = PineconeClient(
                api_key=_pinecone_api_key, pool_threads=pool_threads, source_tag="langchain"
            )
            indexes = client.list_indexes()
            index_names = [i.name for i in indexes.index_list["indexes"]]

            if index_name in index_names:
                index = client.Index(index_name)
            elif len(index_names) == 0:
                raise ValueError(
                    "No active indexes found in your Pinecone project, "
                    "are you sure you're using the right Pinecone API key and Environment? "
                    "Please double check your Pinecone dashboard."
                )
            else:
                raise ValueError(
                    f"Index '{index_name}' not found in your Pinecone project. "
                    f"Did you mean one of the following indexes: {', '.join(index_names)}"
                )
            return index

        @classmethod
        def from_texts(
            cls,
            texts: List[str],
            embedding: Embeddings,
            metadatas: Optional[List[dict]] = None,
            ids: Optional[List[str]] = None,
            batch_size: int = 32,
            text_key: str = "text",
            namespace: Optional[str] = None,
            index_name: Optional[str] = None,
            upsert_kwargs: Optional[dict] = None,
            pool_threads: int = 4,
            embeddings_chunk_size: int = 1000,
            **kwargs: Any,
        ) -> PineconeVectorStore:
            """Construct Pinecone wrapper from raw documents.

            This is a user friendly interface that:
                1. Embeds documents.
                2. Adds the documents to a provided Pinecone index

            This is intended to be a quick way to get started.

            The `pool_threads` affects the speed of the upsert operations.

            Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.

            Example:
                .. code-block:: python

                    from langchain_pinecone import PineconeVectorStore
                    from langchain_openai import OpenAIEmbeddings

                    embeddings = OpenAIEmbeddings()
                    index_name = "my-index"
                    vectorstore = PineconeVectorStore.from_texts(
                        texts,
                        index_name=index_name,
                        embedding=embedding,
                        namespace=namespace,
                    )
            """
            pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
            pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs)

            pinecone.add_texts(
                texts,
                metadatas=metadatas,
                ids=ids,
                namespace=namespace,
                batch_size=batch_size,
                embedding_chunk_size=embeddings_chunk_size,
                **(upsert_kwargs or {}),
            )
            return pinecone

        @classmethod
        def from_existing_index(
            cls,
            index_name: str,
            embedding: Embeddings,
            text_key: str = "text",
            namespace: Optional[str] = None,
            pool_threads: int = 4,
        ) -> PineconeVectorStore:
            """Load pinecone vectorstore from index name."""
            pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
            return cls(pinecone_index, embedding, text_key, namespace)

        def delete(
            self,
            ids: Optional[List[str]] = None,
            delete_all: Optional[bool] = None,
            namespace: Optional[str] = None,
            filter: Optional[dict] = None,
            **kwargs: Any,
        ) -> None:
            """Delete by vector IDs or filter.
            Args:
                ids: List of ids to delete.
                filter: Dictionary of conditions to filter vectors to delete.
            """

            if namespace is None:
                namespace = self._namespace

            if delete_all:
                self._index.delete(delete_all=True, namespace=namespace, **kwargs)
            elif ids is not None:
                chunk_size = 1000
                for i in range(0, len(ids), chunk_size):
                    chunk = ids[i : i + chunk_size]
                    self._index.delete(ids=chunk, namespace=namespace, **kwargs)
            elif filter is not None:
                self._index.delete(filter=filter, namespace=namespace, **kwargs)
            else:
                raise ValueError("Either ids, delete_all, or filter must be provided.")

            return None

    @deprecated(since="0.0.3", removal="0.3.0", alternative="PineconeVectorStore")
    class Pinecone(PineconeVectorStore):
        """Deprecated. Use PineconeVectorStore instead."""

        pass