embedding and the columns are decided by the first metadata dict.
        Metada keys will need to be present for all inserted values. At
        the moment there is no None equivalent in Milvus.
        Args:
            texts (Iterable[str]): The texts to embed, it is assumed
                that they all fit in memory.
            metadatas (Optional[List[dict]]): Metadata dicts attached to each of
                the texts. Defaults to None.
            timeout (Optional[int]): Timeout for each batch insert. Defaults
                to None.
            batch_size (int, optional): Batch size to use for insertion.
                Defaults to 1000.
        Raises:
            MilvusException: Failure to add texts
        Returns:
            List[str]: The resulting keys for each inserted element.
        """
        from pymilvus import Collection, MilvusException
        texts = list(texts)
        try:
            embeddings = self.embedding_func.embed_documents(texts)
        except NotImplementedError:
            embeddings = [self.embedding_func.embed_query(x) for x in texts]
        if len(embeddings) == 0:
            logger.debug("Nothing to insert, skipping.")
            return []
        # If the collection hasnt been initialized yet, perform all steps to do so
        if not isinstance(self.col, Collection):
            self._init(embeddings, metadatas)
        # Dict to hold all insert columns
        insert_dict: dict[str, list] = {
            self._text_field: texts,
            self._vector_field: embeddings,
        }
        # Collect the metadata into the insert dict.
        if metadatas is not None:
            for d in metadatas:
                for key, value in d.items():
                    if key in self.fields:
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html