microsoft/autogen · error · ValueError

Query text cannot be empty for vector search operations

Error message

Query text cannot be empty for vector search operations

What it means

Error "Query text cannot be empty for vector search operations" thrown in microsoft/autogen.

Source

Thrown at python/packages/autogen-ext/src/autogen_ext/tools/azure/_ai_search.py:493

                search_kwargs["search_text"] = search_query.query
                search_kwargs["query_type"] = self.search_config.query_type

                if self.search_config.search_fields:
                    search_kwargs["search_fields"] = self.search_config.search_fields  # type: ignore[assignment]

                if self.search_config.query_type == "semantic" and self.search_config.semantic_config_name:
                    search_kwargs["semantic_configuration_name"] = self.search_config.semantic_config_name

            if self.search_config.select_fields:
                search_kwargs["select"] = self.search_config.select_fields  # type: ignore[assignment]
            if self.search_config.filter:
                search_kwargs["filter"] = str(self.search_config.filter)
            if self.search_config.top is not None:
                search_kwargs["top"] = self.search_config.top  # type: ignore[assignment]

            if self.search_config.vector_fields and len(self.search_config.vector_fields) > 0:
                if not search_query.query:
                    raise ValueError("Query text cannot be empty for vector search operations")

                use_client_side_embeddings = bool(
                    self.search_config.embedding_model and self.search_config.embedding_provider
                )

                vector_queries: List[Union[VectorizedQuery, VectorizableTextQuery]] = []
                if use_client_side_embeddings:
                    from azure.search.documents.models import VectorizedQuery

                    embedding_vector: List[float] = await self._get_embedding(search_query.query)
                    for field_spec in self.search_config.vector_fields:
                        fields = field_spec if isinstance(field_spec, str) else ",".join(field_spec)
                        vector_queries.append(
                            VectorizedQuery(
                                vector=embedding_vector,
                                k_nearest_neighbors=self.search_config.top or 5,
                                fields=fields,
                                kind="vector",

View on GitHub (pinned to 027ecf0a37)

Solutions

1. Supply query text to embed for the vector search

Example fix

Provide non-empty query text

When it happens

Trigger: Thrown at python/packages/autogen-ext/src/autogen_ext/tools/azure/_ai_search.py:493 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of microsoft/autogen@027ecf0a37 (2026-08-15). Data as JSON: /api/errors/99581944b262eef0. Report an issue: GitHub.