f"client should be an instance of pinecone.index.Index, "
                f"got {type(index)}"
            )
        self._index = index
        self._embedding_function = embedding_function
        self._text_key = text_key
        self._namespace = namespace
[docs]    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,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        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.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if namespace is None:
            namespace = self._namespace
        # Embed and create the documents
        docs = []
        ids = ids or [str(uuid.uuid4()) for _ in texts]
        for i, text in enumerate(texts):
            embedding = self._embedding_function(text)
            metadata = metadatas[i] if metadatas else {}
            metadata[self._text_key] = text
            docs.append((ids[i], embedding, metadata))
        # upsert to Pinecone
        self._index.upsert(vectors=docs, namespace=namespace, batch_size=batch_size)
        return ids
[docs]    def similarity_search_with_score(
        self,
        query: str,
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html