BerriAI/litellm · error · ValueError

embedding_model is required in litellm_params for Milvus. Yo

Error message

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'

What it means

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.

Source

Thrown at litellm/llms/milvus/vector_stores/transformation.py:139

        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
        api_base: str,
        litellm_logging_obj: LiteLLMLoggingObj,
        litellm_params: dict,
        extra_body: dict[str, Any] | None = None,
    ) -> tuple[str, dict[str, Any]]:
        """
        Transform search request for Azure AI Search API

        Generates embeddings using litellm.embeddings and constructs Azure AI Search request
        """
        # Convert query to string if it's a list
        if isinstance(query, list):
            query = " ".join(query)

        # Get embedding model from litellm_params (required)
        embedding_model: Final = litellm_params.get("litellm_embedding_model")
        if not embedding_model:
            raise ValueError(
                "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'"
            )

        embedding_config: Final = litellm_params.get("litellm_embedding_config", {})
        if not embedding_config:
            raise ValueError(
                "embedding_config is required in litellm_params for Milvus. You can call any litellm embedding model."
                "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'}"
            )

        # Get top_k (number of results to return)
        # Generate embedding for the query using litellm.embeddings
        try:
            embedding_response: Final = litellm.embedding(
                model=embedding_model,
                input=[query],
                **embedding_config,

View on GitHub (pinned to 6c2dcb801b)

Solutions

1. Set litellm_params['embedding_model'] for Milvus.

When it happens

Trigger: Thrown at litellm/llms/milvus/vector_stores/transformation.py:139 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of BerriAI/litellm@6c2dcb801b (2026-08-15). Data as JSON: /api/errors/e4860432ec251936. Report an issue: GitHub.