langchain/vectorstores/pinecone.py/Pinecone/from_texts
class Pinecone:    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",
        index_name: Optional[str] = None,
        namespace: Optional[str] = None,
        **kwargs: Any,
    ) -> Pinecone:
        """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.

        Example:
            .. code-block:: python

                from langchain import Pinecone
                from langchain.embeddings import OpenAIEmbeddings
                import pinecone

                # The environment should be the one specified next to the API key
                # in your Pinecone console
                pinecone.init(api_key="***", environment="...")
                embeddings = OpenAIEmbeddings()
                pinecone = Pinecone.from_texts(
                    texts,
                    embeddings,
                    index_name="langchain-demo"
                )
        """
        try:
            import pinecone
        except ImportError:
            raise ValueError(
                "Could not import pinecone python package. "
                "Please install it with `pip install pinecone-client`."
            )

        indexes = pinecone.list_indexes()  # checks if provided index exists

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

        for i in range(0, len(texts), batch_size):
            # set end position of batch
            i_end = min(i + batch_size, len(texts))
            # get batch of texts and ids
            lines_batch = texts[i:i_end]
            # create ids if not provided
            if ids:
                ids_batch = ids[i:i_end]
            else:
                ids_batch = [str(uuid.uuid4()) for n in range(i, i_end)]
            # create embeddings
            embeds = embedding.embed_documents(lines_batch)
            # prep metadata and upsert batch
            if metadatas:
                metadata = metadatas[i:i_end]
            else:
                metadata = [{} for _ in range(i, i_end)]
            for j, line in enumerate(lines_batch):
                metadata[j][text_key] = line
            to_upsert = zip(ids_batch, embeds, metadata)

            # upsert to Pinecone
            index.upsert(vectors=list(to_upsert), namespace=namespace)
        return cls(index, embedding.embed_query, text_key, namespace)