texts_batch = []
        metadatas_batch = []
        result_ids = []
        for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
            texts_batch.append(text)
            metadatas_batch.append(metadata)
            if (i + 1) % batch_size == 0:
                result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
                texts_batch = []
                metadatas_batch = []
        if texts_batch:
            result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
        return result_ids
    def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
        """Used to Load Documents into the collection
        Args:
            texts: The list of documents strings to load
            metadatas: The list of metadata objects associated with each document
        Returns:
        """
        # If the text is empty, then exit early
        if not texts:
            return []
        # Embed and create the documents
        embeddings = self._embedding.embed_documents(texts)
        to_insert = [
            {self._text_key: t, self._embedding_key: embedding, **m}
            for t, m, embedding in zip(texts, metadatas, embeddings)
        ]
        # insert the documents in Cosmos DB
        insert_result = self._collection.insert_many(to_insert)  # type: ignore
        return insert_result.inserted_ids
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        collection: Optional[Collection[CosmosDBDocumentType]] = None,