to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5
            param (dict, optional): The search params for the specified index.
                Defaults to None.
            expr (str, optional): Filtering expression. Defaults to None.
            timeout (int, optional): How long to wait before timeout error.
                Defaults to None.
            kwargs: Collection.search() keyword arguments.
        Returns:
            List[Document]: Document results for search.
        """
        if self.col is None:
            logger.debug("No existing collection to search.")
            return []
        if param is None:
            param = self.search_params
        # Determine result metadata fields.
        output_fields = self.fields[:]
        output_fields.remove(self._vector_field)
        # Perform the search.
        res = self.col.search(
            data=[embedding],
            anns_field=self._vector_field,
            param=param,
            limit=fetch_k,
            expr=expr,
            output_fields=output_fields,
            timeout=timeout,
            **kwargs,
        )
        # Organize results.
        ids = []
        documents = []
        scores = []
        for result in res[0]:
            meta = {x: result.entity.get(x) for x in output_fields}
            doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
            documents.append(doc)
            scores.append(result.score)
            ids.append(result.id)
        vectors = self.col.query(
            expr=f"{self._primary_field} in {ids}",
            output_fields=[self._primary_field, self._vector_field],
            timeout=timeout,
        )
        # Reorganize the results from query to match search order.
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html