"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)
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/pinecone.html