{"record":{"id":"bd328f6a3d511e54","repo":"BerriAI/litellm","slug":"failed-to-generate-embedding-for-query-e-bd328f","errorCode":null,"errorMessage":"Failed to generate embedding for query: {e}","messagePattern":"Failed to generate embedding for query: (.+?)","errorType":"exception","errorClass":"Exception","httpStatus":null,"severity":"error","filePath":"litellm/llms/milvus/vector_stores/transformation.py","lineNumber":161,"sourceCode":"\n        embedding_config: Final = litellm_params.get(\"litellm_embedding_config\", {})\n        if not embedding_config:\n            raise ValueError(\n                \"embedding_config is required in litellm_params for Milvus. You can call any litellm embedding model.\"\n                \"Example: litellm_params['embedding_config'] = {'api_base': 'https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/', 'api_key': 'os.environ/AZURE_API_KEY', 'api_version': '2025-09-01'}\"\n            )\n\n        # Get top_k (number of results to return)\n        # Generate embedding for the query using litellm.embeddings\n        try:\n            embedding_response: Final = litellm.embedding(\n                model=embedding_model,\n                input=[query],\n                **embedding_config,\n            )\n            query_vector: Final = embedding_response.data[0][\"embedding\"]\n        except Exception as e:\n            raise Exception(f\"Failed to generate embedding for query: {e}\")\n\n        # Azure AI Search endpoint for search\n        index_name: Final = vector_store_id  # vector_store_id is the index name\n        url: Final = f\"{api_base}/v2/vectordb/entities/search\"\n\n        # Build the request body for Azure AI Search with vector search\n        request_body: Final[dict[str, Any]] = {\n            \"collectionName\": index_name,\n            \"data\": [query_vector],\n            \"annsField\": \"book_intro_vector\",\n            **vector_store_search_optional_params,\n        }\n\n        db_name: Final = litellm_params.get(\"milvus_db_name\")\n        if db_name:\n            request_body[\"dbName\"] = db_name\n\n        partition_names: Final = litellm_params.get(\"milvus_partition_names\")","sourceCodeStart":143,"sourceCodeEnd":179,"githubUrl":"https://github.com/BerriAI/litellm/blob/6c2dcb801bf2b75c18f1bb24140e7cf57465cc4d/litellm/llms/milvus/vector_stores/transformation.py#L143-L179","documentation":"Error \"Failed to generate embedding for query: {e}\" thrown in BerriAI/litellm.","triggerScenarios":"Thrown at litellm/llms/milvus/vector_stores/transformation.py:161 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":["Check the embedding model configuration; see the wrapped exception."],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"6c2dcb801bf2b75c18f1bb24140e7cf57465cc4d","analyzedAt":"2026-08-15T07:12:03.035Z","schemaVersion":2},"datasetVersion":"2026-08-15T17:31:12.345Z"}