{"record":{"id":"e4860432ec251936","repo":"BerriAI/litellm","slug":"embedding-model-is-required-in-litellm-params-for-e48604","errorCode":null,"errorMessage":"embedding_model is required in litellm_params for Milvus. You can call any litellm embedding model.Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'","messagePattern":"embedding_model is required in litellm_params for Milvus\\. You can call any litellm embedding model\\.Example: litellm_params\\['embedding_model'\\] = 'azure/text-embedding-3-large'","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"litellm/llms/milvus/vector_stores/transformation.py","lineNumber":139,"sourceCode":"        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,\n        api_base: str,\n        litellm_logging_obj: LiteLLMLoggingObj,\n        litellm_params: dict,\n        extra_body: dict[str, Any] | None = None,\n    ) -> tuple[str, dict[str, Any]]:\n        \"\"\"\n        Transform search request for Azure AI Search API\n\n        Generates embeddings using litellm.embeddings and constructs Azure AI Search request\n        \"\"\"\n        # Convert query to string if it's a list\n        if isinstance(query, list):\n            query = \" \".join(query)\n\n        # Get embedding model from litellm_params (required)\n        embedding_model: Final = litellm_params.get(\"litellm_embedding_model\")\n        if not embedding_model:\n            raise ValueError(\n                \"embedding_model is required in litellm_params for Milvus. You can call any litellm embedding model.\"\n                \"Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'\"\n            )\n\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,","sourceCodeStart":121,"sourceCodeEnd":157,"githubUrl":"https://github.com/BerriAI/litellm/blob/6c2dcb801bf2b75c18f1bb24140e7cf57465cc4d/litellm/llms/milvus/vector_stores/transformation.py#L121-L157","documentation":"Error \"embedding_model is required in litellm_params for Milvus. You can call any litellm embedding model.Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'\" thrown in BerriAI/litellm.","triggerScenarios":"Thrown at litellm/llms/milvus/vector_stores/transformation.py:139 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":["Set litellm_params['embedding_model'] for Milvus."],"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"}