Add model_info_fields and return_litellm_params options in /model/info endpoint

litellm/proxy/common_utils/openai_endpoint_utils.py

@@ -2,7 +2,7 @@
 Contains utils used by OpenAI compatible endpoints 
 """
 
-from typing import Optional
+from typing import Optional, List
 
 from fastapi import Request
 
@@ -33,6 +33,36 @@ def remove_sensitive_info_from_deployment(deployment_dict: dict) -> dict:
     return deployment_dict
 
 
+def process_model_info_fields_from_deployment(
+    deployment_dict: dict, model_info_fields: Optional[List[str]]
+) -> dict:
+    """
+    Keeps only the specified fields in a deployment dictionary (whitelist approach).
+
+    This function filters the deployment dictionary to include only the fields
+    specified in model_info_fields. All other fields are removed.
+
+    Args:
+        deployment_dict (dict): The deployment dictionary to filter fields from.
+        model_info_fields (Optional[List[str]]): List of field names to keep in
+            the deployment dictionary. If None, all fields are kept.
+
+    Returns:
+        dict: The modified deployment dictionary with only specified fields kept.
+    """
+    if model_info_fields is None:
+        return deployment_dict
+
+    # Keep only fields that are in the model_info_fields list
+    filtered_dict = {
+        key: value 
+        for key, value in deployment_dict.items() 
+        if key in model_info_fields
+    }
+
+    return filtered_dict
+
+
 async def get_custom_llm_provider_from_request_body(request: Request) -> Optional[str]:
     """
     Get the `custom_llm_provider` from the request body

litellm/proxy/proxy_server.py

@@ -225,7 +225,7 @@ def generate_feedback_box():
     get_file_contents_from_s3,
 )
 from litellm.proxy.common_utils.openai_endpoint_utils import (
-    remove_sensitive_info_from_deployment,
+    remove_sensitive_info_from_deployment, process_model_info_fields_from_deployment,
 )
 from litellm.proxy.common_utils.proxy_state import ProxyState
 from litellm.proxy.common_utils.reset_budget_job import ResetBudgetJob
@@ -7497,7 +7497,7 @@ async def model_metrics_exceptions(
     return {"data": response, "exception_types": list(exception_types)}
 
 
-def _get_proxy_model_info(model: dict) -> dict:
+def _get_proxy_model_info(model: dict, model_info_fields: Optional[List[str]], return_litellm_params: bool) -> dict:
     # provided model_info in config.yaml
     model_info = model.get("model_info", {})
 
@@ -7534,6 +7534,9 @@ def _get_proxy_model_info(model: dict) -> dict:
     model["model_info"] = model_info
     # don't return the llm credentials
     model = remove_sensitive_info_from_deployment(deployment_dict=model)
+    model = process_model_info_fields_from_deployment(deployment_dict=model, model_info_fields=model_info_fields)
+    if not return_litellm_params and "litellm_params" in model:
+        del model["litellm_params"]
 
     return model
 
@@ -7551,6 +7554,8 @@ def _get_proxy_model_info(model: dict) -> dict:
 async def model_info_v1(  # noqa: PLR0915
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
     litellm_model_id: Optional[str] = None,
+    model_info_fields: Optional[List[str]] = None,
+    return_litellm_params: bool = True
 ):
     """
     Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)
@@ -7561,6 +7566,16 @@ async def model_info_v1(  # noqa: PLR0915
         - When litellm_model_id is passed, it will return the info for that specific model
         - When litellm_model_id is not passed, it will return the info for all models
 
+        model_info_fields: Optional[List[str]] = None (list of field names to include in the response)
+
+        - When model_info_fields is passed, only the specified fields will be included in each model's data
+        - When model_info_fields is not passed, all fields are included in the response
+
+        return_litellm_params: bool = True (controls whether to include litellm_params in the response)
+
+        - When return_litellm_params is True (default), litellm_params are included with sensitive data masked
+        - When return_litellm_params is False, litellm_params are completely removed from the response
+
     Returns:
         Returns a dictionary containing information about each model.
 
@@ -7603,6 +7618,14 @@ async def model_info_v1(  # noqa: PLR0915
         _deployment_info_dict = remove_sensitive_info_from_deployment(
             deployment_dict=_deployment_info_dict
         )
+        _deployment_info_dict = process_model_info_fields_from_deployment(
+            deployment_dict=_deployment_info_dict,
+            model_info_fields=model_info_fields,
+        )
+
+        if not return_litellm_params and "litellm_params" in _deployment_info_dict:
+            del _deployment_info_dict["litellm_params"]
+
         return {"data": _deployment_info_dict}
 
     if llm_model_list is None:
@@ -7632,7 +7655,9 @@ async def model_info_v1(  # noqa: PLR0915
                 },
             )
         _deployment_info_dict = _get_proxy_model_info(
-            model=deployment_info.model_dump(exclude_none=True)
+            model=deployment_info.model_dump(exclude_none=True),
+            model_info_fields=model_info_fields,
+            return_litellm_params=return_litellm_params,
         )
         return {"data": [_deployment_info_dict]}
 
@@ -7675,7 +7700,9 @@ async def model_info_v1(  # noqa: PLR0915
             all_models = []
 
     for in_place_model in all_models:
-        in_place_model = _get_proxy_model_info(model=in_place_model)
+        in_place_model = _get_proxy_model_info(
+            model=in_place_model, model_info_fields=model_info_fields, return_litellm_params=return_litellm_params
+        )
 
     verbose_proxy_logger.debug("all_models: %s", all_models)
     return {"data": all_models}

tests/test_litellm/proxy/test_proxy_server.py

@@ -2048,6 +2048,146 @@ async def test_model_info_v1_oci_secrets_not_leaked():
         assert "/path/to/oci_api_key.pem" not in result_str
 
 
+@pytest.mark.asyncio
+async def test_model_info_v1_with_model_info_fields_filter():
+    """Test that model_info_fields parameter keeps only specified fields (whitelist approach)"""
+    from unittest.mock import MagicMock, patch
+
+    from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+    from litellm.proxy.proxy_server import model_info_v1
+    from litellm.types.router import Deployment as RouterDeployment
+    from litellm.types.router import LiteLLM_Params
+    from litellm.types.router import ModelInfo
+
+    # Mock user
+    user_api_key_dict = UserAPIKeyAuth(
+        user_id="test_user",
+        api_key="test_key",
+        user_role=LitellmUserRoles.PROXY_ADMIN,
+    )
+
+    # Create mock model data with multiple fields
+    mock_model_data = {
+        "model_name": "gpt-4",
+        "litellm_params": {
+            "model": "gpt-4",
+            "api_key": "sk-***",
+            "api_base": "https://api.openai.com/v1",
+        },
+        "model_info": {
+            "id": "test-model-id",
+            "db_model": False,
+            "description": "Test model description",
+            "tags": ["production", "test"],
+        },
+    }
+
+    # Mock router with deployment
+    mock_router = MagicMock()
+    mock_deployment = RouterDeployment(
+        model_name="gpt-4",
+        litellm_params=LiteLLM_Params(model="gpt-4"),
+        model_info=ModelInfo(id="test-model-id", db_model=False),
+    )
+    mock_router.get_deployment.return_value = mock_deployment
+
+    with (
+        patch("litellm.proxy.proxy_server.llm_router", mock_router),
+        patch("litellm.proxy.proxy_server.llm_model_list", [mock_model_data]),
+        patch(
+            "litellm.proxy.proxy_server.general_settings",
+            {"infer_model_from_keys": False},
+        ),
+        patch("litellm.proxy.proxy_server.user_model", None),
+    ):
+
+        # Call model_info_v1 with model_info_fields to keep only "model_name" and "model_info"
+        result = await model_info_v1(
+            user_api_key_dict=user_api_key_dict,
+            litellm_model_id="test-model-id",
+            model_info_fields=["model_name", "model_info"],
+        )
+
+        # Verify the result structure
+        assert "data" in result
+        assert len(result["data"]) == 1
+
+        model_data = result["data"][0]
+
+        assert set(model_data.keys()) == {
+            "model_name",
+            "model_info",
+        }, f"Expected only ['model_name', 'model_info'], but got {list(model_data.keys())}"
+        assert "model_name" in model_data, "model_name should be present"
+        assert "model_info" in model_data, "model_info should be present"
+
+
+@pytest.mark.asyncio
+async def test_model_info_v1_with_return_litellm_params_false():
+    """Test that return_litellm_params=False removes litellm_params from /v1/model/info response"""
+    from unittest.mock import MagicMock, patch
+
+    from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+    from litellm.proxy.proxy_server import model_info_v1
+    from litellm.types.router import Deployment as RouterDeployment
+    from litellm.types.router import LiteLLM_Params
+    from litellm.types.router import ModelInfo
+
+    # Mock user
+    user_api_key_dict = UserAPIKeyAuth(
+        user_id="test_user",
+        api_key="test_key",
+        user_role=LitellmUserRoles.PROXY_ADMIN,
+    )
+
+    # Create mock model data
+    mock_model_data = {
+        "model_name": "gpt-4",
+        "litellm_params": {
+            "model": "gpt-4",
+            "api_key": "sk-***",
+            "api_base": "https://api.openai.com/v1",
+        },
+        "model_info": {"id": "test-model-id", "db_model": False},
+    }
+
+    # Mock router with deployment
+    mock_router = MagicMock()
+    mock_deployment = RouterDeployment(
+        model_name="gpt-4",
+        litellm_params=LiteLLM_Params(model="gpt-4"),
+        model_info=ModelInfo(id="test-model-id", db_model=False),
+    )
+    mock_router.get_deployment.return_value = mock_deployment
+
+    with (
+        patch("litellm.proxy.proxy_server.llm_router", mock_router),
+        patch("litellm.proxy.proxy_server.llm_model_list", [mock_model_data]),
+        patch(
+            "litellm.proxy.proxy_server.general_settings",
+            {"infer_model_from_keys": False},
+        ),
+        patch("litellm.proxy.proxy_server.user_model", None),
+    ):
+
+        # Call model_info_v1 with return_litellm_params=False
+        result = await model_info_v1(
+            user_api_key_dict=user_api_key_dict,
+            litellm_model_id="test-model-id",
+            return_litellm_params=False,
+        )
+
+        # Verify the result structure
+        assert "data" in result
+        assert len(result["data"]) == 1
+
+        model_data = result["data"][0]
+
+        # Verify litellm_params is NOT present
+        assert (
+            "litellm_params" not in model_data
+        ), "litellm_params should be removed when return_litellm_params=False"
+
 def test_add_callback_from_db_to_in_memory_litellm_callbacks():
     """
     Test that _add_callback_from_db_to_in_memory_litellm_callbacks correctly adds callbacks