{"record":{"id":"99581944b262eef0","repo":"microsoft/autogen","slug":"query-text-cannot-be-empty-for-vector-search-opera","errorCode":null,"errorMessage":"Query text cannot be empty for vector search operations","messagePattern":"Query text cannot be empty for vector search operations","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"python/packages/autogen-ext/src/autogen_ext/tools/azure/_ai_search.py","lineNumber":493,"sourceCode":"                search_kwargs[\"search_text\"] = search_query.query\n                search_kwargs[\"query_type\"] = self.search_config.query_type\n\n                if self.search_config.search_fields:\n                    search_kwargs[\"search_fields\"] = self.search_config.search_fields  # type: ignore[assignment]\n\n                if self.search_config.query_type == \"semantic\" and self.search_config.semantic_config_name:\n                    search_kwargs[\"semantic_configuration_name\"] = self.search_config.semantic_config_name\n\n            if self.search_config.select_fields:\n                search_kwargs[\"select\"] = self.search_config.select_fields  # type: ignore[assignment]\n            if self.search_config.filter:\n                search_kwargs[\"filter\"] = str(self.search_config.filter)\n            if self.search_config.top is not None:\n                search_kwargs[\"top\"] = self.search_config.top  # type: ignore[assignment]\n\n            if self.search_config.vector_fields and len(self.search_config.vector_fields) > 0:\n                if not search_query.query:\n                    raise ValueError(\"Query text cannot be empty for vector search operations\")\n\n                use_client_side_embeddings = bool(\n                    self.search_config.embedding_model and self.search_config.embedding_provider\n                )\n\n                vector_queries: List[Union[VectorizedQuery, VectorizableTextQuery]] = []\n                if use_client_side_embeddings:\n                    from azure.search.documents.models import VectorizedQuery\n\n                    embedding_vector: List[float] = await self._get_embedding(search_query.query)\n                    for field_spec in self.search_config.vector_fields:\n                        fields = field_spec if isinstance(field_spec, str) else \",\".join(field_spec)\n                        vector_queries.append(\n                            VectorizedQuery(\n                                vector=embedding_vector,\n                                k_nearest_neighbors=self.search_config.top or 5,\n                                fields=fields,\n                                kind=\"vector\",","sourceCodeStart":475,"sourceCodeEnd":511,"githubUrl":"https://github.com/microsoft/autogen/blob/027ecf0a379bcc1d09956d46d12d44a3ad9cee14/python/packages/autogen-ext/src/autogen_ext/tools/azure/_ai_search.py#L475-L511","documentation":"Error \"Query text cannot be empty for vector search operations\" thrown in microsoft/autogen.","triggerScenarios":"Thrown at python/packages/autogen-ext/src/autogen_ext/tools/azure/_ai_search.py:493 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":["Supply query text to embed for the vector search"],"exampleFix":"Provide non-empty query text","handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"027ecf0a379bcc1d09956d46d12d44a3ad9cee14","analyzedAt":"2026-08-15T03:38:00.719Z","schemaVersion":2},"datasetVersion":"2026-08-15T17:31:12.345Z"}