Add /vllm/* and /mistral/* passthrough endpoints (adds support for Mistral OCR via passthrough)

* feat(llm_passthrough_endpoints.py): support mistral passthrough

Closes https://github.com/BerriAI/litellm/issues/9051

* feat(llm_passthrough_endpoints.py): initial commit for adding vllm passthrough route

* feat(vllm/common_utils.py): add new vllm model info route

make it possible to use vllm passthrough route via factory function

* fix(llm_passthrough_endpoints.py): add all methods to vllm passthrough route

* fix: fix linting error

* fix: fix linting error

* fix: fix ruff check

* fix(proxy/_types.py): add new passthrough routes

* docs(config_settings.md): add mistral env vars to docs

docs/my-website/docs/proxy/config_settings.md

@@ -449,6 +449,8 @@ router_settings:
 | LITELLM_TOKEN | Access token for LiteLLM integration
 | LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD | If true, prints the standard logging payload to the console - useful for debugging
 | LOGFIRE_TOKEN | Token for Logfire logging service
+| MISTRAL_API_BASE | Base URL for Mistral API
+| MISTRAL_API_KEY | API key for Mistral API
 | MICROSOFT_CLIENT_ID | Client ID for Microsoft services
 | MICROSOFT_CLIENT_SECRET | Client secret for Microsoft services
 | MICROSOFT_TENANT | Tenant ID for Microsoft Azure

litellm/llms/anthropic/chat/transformation.py

@@ -44,7 +44,7 @@ from litellm.utils import (
     token_counter,
 )
 
-from ..common_utils import AnthropicError, process_anthropic_headers
+from ..common_utils import AnthropicError, AnthropicModelInfo, process_anthropic_headers
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -54,7 +54,7 @@ else:
     LoggingClass = Any
 
 
-class AnthropicConfig(BaseConfig):
+class AnthropicConfig(AnthropicModelInfo, BaseConfig):
     """
     Reference: https://docs.anthropic.com/claude/reference/messages_post
 
@@ -127,41 +127,6 @@ class AnthropicConfig(BaseConfig):
             "anthropic-beta": "prompt-caching-2024-07-31",
         }
 
-    def get_anthropic_headers(
-        self,
-        api_key: str,
-        anthropic_version: Optional[str] = None,
-        computer_tool_used: bool = False,
-        prompt_caching_set: bool = False,
-        pdf_used: bool = False,
-        is_vertex_request: bool = False,
-        user_anthropic_beta_headers: Optional[List[str]] = None,
-    ) -> dict:
-        betas = set()
-        if prompt_caching_set:
-            betas.add("prompt-caching-2024-07-31")
-        if computer_tool_used:
-            betas.add("computer-use-2024-10-22")
-        if pdf_used:
-            betas.add("pdfs-2024-09-25")
-        headers = {
-            "anthropic-version": anthropic_version or "2023-06-01",
-            "x-api-key": api_key,
-            "accept": "application/json",
-            "content-type": "application/json",
-        }
-
-        if user_anthropic_beta_headers is not None:
-            betas.update(user_anthropic_beta_headers)
-
-        # Don't send any beta headers to Vertex, Vertex has failed requests when they are sent
-        if is_vertex_request is True:
-            pass
-        elif len(betas) > 0:
-            headers["anthropic-beta"] = ",".join(betas)
-
-        return headers
-
     def _map_tool_choice(
         self, tool_choice: Optional[str], parallel_tool_use: Optional[bool]
     ) -> Optional[AnthropicMessagesToolChoice]:
@@ -446,49 +411,6 @@ class AnthropicConfig(BaseConfig):
         )
         return _tool
 
-    def is_cache_control_set(self, messages: List[AllMessageValues]) -> bool:
-        """
-        Return if {"cache_control": ..} in message content block
-
-        Used to check if anthropic prompt caching headers need to be set.
-        """
-        for message in messages:
-            if message.get("cache_control", None) is not None:
-                return True
-            _message_content = message.get("content")
-            if _message_content is not None and isinstance(_message_content, list):
-                for content in _message_content:
-                    if "cache_control" in content:
-                        return True
-
-        return False
-
-    def is_computer_tool_used(
-        self, tools: Optional[List[AllAnthropicToolsValues]]
-    ) -> bool:
-        if tools is None:
-            return False
-        for tool in tools:
-            if "type" in tool and tool["type"].startswith("computer_"):
-                return True
-        return False
-
-    def is_pdf_used(self, messages: List[AllMessageValues]) -> bool:
-        """
-        Set to true if media passed into messages.
-
-        """
-        for message in messages:
-            if (
-                "content" in message
-                and message["content"] is not None
-                and isinstance(message["content"], list)
-            ):
-                for content in message["content"]:
-                    if "type" in content and content["type"] != "text":
-                        return True
-        return False
-
     def translate_system_message(
         self, messages: List[AllMessageValues]
     ) -> List[AnthropicSystemMessageContent]:
@@ -862,47 +784,3 @@ class AnthropicConfig(BaseConfig):
             message=error_message,
             headers=cast(httpx.Headers, headers),
         )
-
-    def _get_user_anthropic_beta_headers(
-        self, anthropic_beta_header: Optional[str]
-    ) -> Optional[List[str]]:
-        if anthropic_beta_header is None:
-            return None
-        return anthropic_beta_header.split(",")
-
-    def validate_environment(
-        self,
-        headers: dict,
-        model: str,
-        messages: List[AllMessageValues],
-        optional_params: dict,
-        litellm_params: dict,
-        api_key: Optional[str] = None,
-        api_base: Optional[str] = None,
-    ) -> Dict:
-        if api_key is None:
-            raise litellm.AuthenticationError(
-                message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars",
-                llm_provider="anthropic",
-                model=model,
-            )
-
-        tools = optional_params.get("tools")
-        prompt_caching_set = self.is_cache_control_set(messages=messages)
-        computer_tool_used = self.is_computer_tool_used(tools=tools)
-        pdf_used = self.is_pdf_used(messages=messages)
-        user_anthropic_beta_headers = self._get_user_anthropic_beta_headers(
-            anthropic_beta_header=headers.get("anthropic-beta")
-        )
-        anthropic_headers = self.get_anthropic_headers(
-            computer_tool_used=computer_tool_used,
-            prompt_caching_set=prompt_caching_set,
-            pdf_used=pdf_used,
-            api_key=api_key,
-            is_vertex_request=optional_params.get("is_vertex_request", False),
-            user_anthropic_beta_headers=user_anthropic_beta_headers,
-        )
-
-        headers = {**headers, **anthropic_headers}
-
-        return headers

litellm/llms/anthropic/common_utils.py

@@ -2,7 +2,7 @@
 This file contains common utils for anthropic calls.
 """
 
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 
 import httpx
 
@@ -10,6 +10,8 @@ import litellm
 from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.anthropic import AllAnthropicToolsValues
+from litellm.types.llms.openai import AllMessageValues
 
 
 class AnthropicError(BaseLLMException):
@@ -23,6 +25,128 @@ class AnthropicError(BaseLLMException):
 
 
 class AnthropicModelInfo(BaseLLMModelInfo):
+    def is_cache_control_set(self, messages: List[AllMessageValues]) -> bool:
+        """
+        Return if {"cache_control": ..} in message content block
+
+        Used to check if anthropic prompt caching headers need to be set.
+        """
+        for message in messages:
+            if message.get("cache_control", None) is not None:
+                return True
+            _message_content = message.get("content")
+            if _message_content is not None and isinstance(_message_content, list):
+                for content in _message_content:
+                    if "cache_control" in content:
+                        return True
+
+        return False
+
+    def is_computer_tool_used(
+        self, tools: Optional[List[AllAnthropicToolsValues]]
+    ) -> bool:
+        if tools is None:
+            return False
+        for tool in tools:
+            if "type" in tool and tool["type"].startswith("computer_"):
+                return True
+        return False
+
+    def is_pdf_used(self, messages: List[AllMessageValues]) -> bool:
+        """
+        Set to true if media passed into messages.
+
+        """
+        for message in messages:
+            if (
+                "content" in message
+                and message["content"] is not None
+                and isinstance(message["content"], list)
+            ):
+                for content in message["content"]:
+                    if "type" in content and content["type"] != "text":
+                        return True
+        return False
+
+    def _get_user_anthropic_beta_headers(
+        self, anthropic_beta_header: Optional[str]
+    ) -> Optional[List[str]]:
+        if anthropic_beta_header is None:
+            return None
+        return anthropic_beta_header.split(",")
+
+    def get_anthropic_headers(
+        self,
+        api_key: str,
+        anthropic_version: Optional[str] = None,
+        computer_tool_used: bool = False,
+        prompt_caching_set: bool = False,
+        pdf_used: bool = False,
+        is_vertex_request: bool = False,
+        user_anthropic_beta_headers: Optional[List[str]] = None,
+    ) -> dict:
+        betas = set()
+        if prompt_caching_set:
+            betas.add("prompt-caching-2024-07-31")
+        if computer_tool_used:
+            betas.add("computer-use-2024-10-22")
+        if pdf_used:
+            betas.add("pdfs-2024-09-25")
+        headers = {
+            "anthropic-version": anthropic_version or "2023-06-01",
+            "x-api-key": api_key,
+            "accept": "application/json",
+            "content-type": "application/json",
+        }
+
+        if user_anthropic_beta_headers is not None:
+            betas.update(user_anthropic_beta_headers)
+
+        # Don't send any beta headers to Vertex, Vertex has failed requests when they are sent
+        if is_vertex_request is True:
+            pass
+        elif len(betas) > 0:
+            headers["anthropic-beta"] = ",".join(betas)
+
+        return headers
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> Dict:
+        if api_key is None:
+            raise litellm.AuthenticationError(
+                message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars",
+                llm_provider="anthropic",
+                model=model,
+            )
+
+        tools = optional_params.get("tools")
+        prompt_caching_set = self.is_cache_control_set(messages=messages)
+        computer_tool_used = self.is_computer_tool_used(tools=tools)
+        pdf_used = self.is_pdf_used(messages=messages)
+        user_anthropic_beta_headers = self._get_user_anthropic_beta_headers(
+            anthropic_beta_header=headers.get("anthropic-beta")
+        )
+        anthropic_headers = self.get_anthropic_headers(
+            computer_tool_used=computer_tool_used,
+            prompt_caching_set=prompt_caching_set,
+            pdf_used=pdf_used,
+            api_key=api_key,
+            is_vertex_request=optional_params.get("is_vertex_request", False),
+            user_anthropic_beta_headers=user_anthropic_beta_headers,
+        )
+
+        headers = {**headers, **anthropic_headers}
+
+        return headers
+
     @staticmethod
     def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
         return (

litellm/llms/base_llm/base_utils.py

@@ -44,6 +44,19 @@ class BaseLLMModelInfo(ABC):
     def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
         pass
 
+    @abstractmethod
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        pass
+
     @staticmethod
     @abstractmethod
     def get_base_model(model: str) -> Optional[str]:

litellm/llms/openai/chat/gpt_transformation.py

@@ -389,7 +389,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         )
 
     @staticmethod
-    def get_base_model(model: str) -> str:
+    def get_base_model(model: Optional[str] = None) -> Optional[str]:
         return model
 
     def get_model_response_iterator(

litellm/llms/topaz/common_utils.py

@@ -1,6 +1,7 @@
 from typing import List, Optional
 
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllMessageValues
 
 from ..base_llm.base_utils import BaseLLMModelInfo
 from ..base_llm.chat.transformation import BaseLLMException
@@ -11,6 +12,26 @@ class TopazException(BaseLLMException):
 
 
 class TopazModelInfo(BaseLLMModelInfo):
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is None:
+            raise ValueError(
+                "API key is required for Topaz image variations. Set via `TOPAZ_API_KEY` or `api_key=..`"
+            )
+        return {
+            # "Content-Type": "multipart/form-data",
+            "Accept": "image/jpeg",
+            "X-API-Key": api_key,
+        }
+
     def get_models(
         self, api_key: Optional[str] = None, api_base: Optional[str] = None
     ) -> List[str]:

litellm/llms/topaz/image_variations/transformation.py

@@ -10,10 +10,7 @@ from litellm.llms.base_llm.chat.transformation import (
     BaseLLMException,
     LiteLLMLoggingObj,
 )
-from litellm.types.llms.openai import (
+from litellm.types.llms.openai import OpenAIImageVariationOptionalParams
-    AllMessageValues,
-    OpenAIImageVariationOptionalParams,
-)
 from litellm.types.utils import (
     FileTypes,
     HttpHandlerRequestFields,
@@ -22,35 +19,15 @@ from litellm.types.utils import (
 )
 
 from ...base_llm.image_variations.transformation import BaseImageVariationConfig
-from ..common_utils import TopazException
+from ..common_utils import TopazException, TopazModelInfo
 
 
-class TopazImageVariationConfig(BaseImageVariationConfig):
+class TopazImageVariationConfig(TopazModelInfo, BaseImageVariationConfig):
     def get_supported_openai_params(
         self, model: str
     ) -> List[OpenAIImageVariationOptionalParams]:
         return ["response_format", "size"]
 
-    def validate_environment(
-        self,
-        headers: dict,
-        model: str,
-        messages: List[AllMessageValues],
-        optional_params: dict,
-        litellm_params: dict,
-        api_key: Optional[str] = None,
-        api_base: Optional[str] = None,
-    ) -> dict:
-        if api_key is None:
-            raise ValueError(
-                "API key is required for Topaz image variations. Set via `TOPAZ_API_KEY` or `api_key=..`"
-            )
-        return {
-            # "Content-Type": "multipart/form-data",
-            "Accept": "image/jpeg",
-            "X-API-Key": api_key,
-        }
-
     def get_complete_url(
         self,
         api_base: Optional[str],

litellm/llms/vllm/common_utils.py

@@ -0,0 +1,75 @@
+from typing import List, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllMessageValues
+from litellm.utils import _add_path_to_api_base
+
+
+class VLLMError(BaseLLMException):
+    pass
+
+
+class VLLMModelInfo(BaseLLMModelInfo):
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        """Google AI Studio sends api key in query params"""
+        return headers
+
+    @staticmethod
+    def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
+        api_base = api_base or get_secret_str("VLLM_API_BASE")
+        if api_base is None:
+            raise ValueError(
+                "VLLM_API_BASE is not set. Please set the environment variable, to use VLLM's pass-through - `{LITELLM_API_BASE}/vllm/{endpoint}`."
+            )
+        return api_base
+
+    @staticmethod
+    def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
+        return None
+
+    @staticmethod
+    def get_base_model(model: str) -> Optional[str]:
+        return model
+
+    def get_models(
+        self, api_key: Optional[str] = None, api_base: Optional[str] = None
+    ) -> List[str]:
+        api_base = VLLMModelInfo.get_api_base(api_base)
+        api_key = VLLMModelInfo.get_api_key(api_key)
+        endpoint = "/v1/models"
+        if api_base is None or api_key is None:
+            raise ValueError(
+                "GEMINI_API_BASE or GEMINI_API_KEY is not set. Please set the environment variable, to query Gemini's `/models` endpoint."
+            )
+
+        url = _add_path_to_api_base(api_base, endpoint)
+        response = litellm.module_level_client.get(
+            url=url,
+        )
+
+        response.raise_for_status()
+
+        models = response.json()["data"]
+
+        return [model["id"] for model in models]
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return VLLMError(
+            status_code=status_code, message=error_message, headers=headers
+        )

litellm/llms/xai/common_utils.py

@@ -5,9 +5,29 @@ import httpx
 import litellm
 from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllMessageValues
 
 
 class XAIModelInfo(BaseLLMModelInfo):
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is not None:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        # Ensure Content-Type is set to application/json
+        if "content-type" not in headers and "Content-Type" not in headers:
+            headers["Content-Type"] = "application/json"
+
+        return headers
+
     @staticmethod
     def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
         return api_base or get_secret_str("XAI_API_BASE") or "https://api.x.ai"

litellm/proxy/_types.py

@@ -317,6 +317,8 @@ class LiteLLMRoutes(enum.Enum):
         "/openai",
         "/assemblyai",
         "/eu.assemblyai",
+        "/vllm",
+        "/mistral",
     ]
 
     anthropic_routes = [

litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py

@@ -6,6 +6,7 @@ Provider-specific Pass-Through Endpoints
 Use litellm with Anthropic SDK, Vertex AI SDK, Cohere SDK, etc.
 """
 
+import os
 from typing import Optional
 
 import httpx
@@ -43,6 +44,84 @@ def create_request_copy(request: Request):
     }
 
 
+async def llm_passthrough_factory_proxy_route(
+    custom_llm_provider: str,
+    endpoint: str,
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Factory function for creating pass-through endpoints for LLM providers.
+    """
+    from litellm.types.utils import LlmProviders
+    from litellm.utils import ProviderConfigManager
+
+    provider_config = ProviderConfigManager.get_provider_model_info(
+        provider=LlmProviders(custom_llm_provider),
+        model=None,
+    )
+    if provider_config is None:
+        raise HTTPException(
+            status_code=404, detail=f"Provider {custom_llm_provider} not found"
+        )
+    base_target_url = provider_config.get_api_base()
+
+    if base_target_url is None:
+        raise HTTPException(
+            status_code=404, detail=f"Provider {custom_llm_provider} api base not found"
+        )
+
+    encoded_endpoint = httpx.URL(endpoint).path
+
+    # Ensure endpoint starts with '/' for proper URL construction
+    if not encoded_endpoint.startswith("/"):
+        encoded_endpoint = "/" + encoded_endpoint
+
+    # Construct the full target URL using httpx
+    base_url = httpx.URL(base_target_url)
+    updated_url = base_url.copy_with(path=encoded_endpoint)
+
+    # Add or update query parameters
+    provider_api_key = passthrough_endpoint_router.get_credentials(
+        custom_llm_provider=custom_llm_provider,
+        region_name=None,
+    )
+
+    auth_headers = provider_config.validate_environment(
+        headers={},
+        model="",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key=provider_api_key,
+        api_base=base_target_url,
+    )
+
+    ## check for streaming
+    is_streaming_request = False
+    # anthropic is streaming when 'stream' = True is in the body
+    if request.method == "POST":
+        _request_body = await request.json()
+        if _request_body.get("stream"):
+            is_streaming_request = True
+
+    ## CREATE PASS-THROUGH
+    endpoint_func = create_pass_through_route(
+        endpoint=endpoint,
+        target=str(updated_url),
+        custom_headers=auth_headers,
+    )  # dynamically construct pass-through endpoint based on incoming path
+    received_value = await endpoint_func(
+        request,
+        fastapi_response,
+        user_api_key_dict,
+        stream=is_streaming_request,  # type: ignore
+    )
+
+    return received_value
+
+
 @router.api_route(
     "/gemini/{endpoint:path}",
     methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
@@ -162,6 +241,84 @@ async def cohere_proxy_route(
     return received_value
 
 
+@router.api_route(
+    "/vllm/{endpoint:path}",
+    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
+    tags=["VLLM Pass-through", "pass-through"],
+)
+async def vllm_proxy_route(
+    endpoint: str,
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    [Docs](https://docs.litellm.ai/docs/pass_through/vllm)
+    """
+    return await llm_passthrough_factory_proxy_route(
+        endpoint=endpoint,
+        request=request,
+        fastapi_response=fastapi_response,
+        user_api_key_dict=user_api_key_dict,
+        custom_llm_provider="vllm",
+    )
+
+
+@router.api_route(
+    "/mistral/{endpoint:path}",
+    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
+    tags=["Mistral Pass-through", "pass-through"],
+)
+async def mistral_proxy_route(
+    endpoint: str,
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    [Docs](https://docs.litellm.ai/docs/anthropic_completion)
+    """
+    base_target_url = os.getenv("MISTRAL_API_BASE") or "https://api.mistral.ai"
+    encoded_endpoint = httpx.URL(endpoint).path
+
+    # Ensure endpoint starts with '/' for proper URL construction
+    if not encoded_endpoint.startswith("/"):
+        encoded_endpoint = "/" + encoded_endpoint
+
+    # Construct the full target URL using httpx
+    base_url = httpx.URL(base_target_url)
+    updated_url = base_url.copy_with(path=encoded_endpoint)
+
+    # Add or update query parameters
+    mistral_api_key = passthrough_endpoint_router.get_credentials(
+        custom_llm_provider="mistral",
+        region_name=None,
+    )
+
+    ## check for streaming
+    is_streaming_request = False
+    # anthropic is streaming when 'stream' = True is in the body
+    if request.method == "POST":
+        _request_body = await request.json()
+        if _request_body.get("stream"):
+            is_streaming_request = True
+
+    ## CREATE PASS-THROUGH
+    endpoint_func = create_pass_through_route(
+        endpoint=endpoint,
+        target=str(updated_url),
+        custom_headers={"Authorization": "Bearer {}".format(mistral_api_key)},
+    )  # dynamically construct pass-through endpoint based on incoming path
+    received_value = await endpoint_func(
+        request,
+        fastapi_response,
+        user_api_key_dict,
+        stream=is_streaming_request,  # type: ignore
+    )
+
+    return received_value
+
+
 @router.api_route(
     "/anthropic/{endpoint:path}",
     methods=["GET", "POST", "PUT", "DELETE", "PATCH"],

litellm/utils.py

@@ -516,9 +516,9 @@ def function_setup(  # noqa: PLR0915
         function_id: Optional[str] = kwargs["id"] if "id" in kwargs else None
 
         ## DYNAMIC CALLBACKS ##
-        dynamic_callbacks: Optional[List[Union[str, Callable, CustomLogger]]] = (
+        dynamic_callbacks: Optional[
-            kwargs.pop("callbacks", None)
+            List[Union[str, Callable, CustomLogger]]
-        )
+        ] = kwargs.pop("callbacks", None)
         all_callbacks = get_dynamic_callbacks(dynamic_callbacks=dynamic_callbacks)
 
         if len(all_callbacks) > 0:
@@ -1202,9 +1202,9 @@ def client(original_function):  # noqa: PLR0915
                         exception=e,
                         retry_policy=kwargs.get("retry_policy"),
                     )
-                    kwargs["retry_policy"] = (
+                    kwargs[
-                        reset_retry_policy()
+                        "retry_policy"
-                    )  # prevent infinite loops
+                    ] = reset_retry_policy()  # prevent infinite loops
                 litellm.num_retries = (
                     None  # set retries to None to prevent infinite loops
                 )
@@ -3013,16 +3013,16 @@ def get_optional_params(  # noqa: PLR0915
                     True  # so that main.py adds the function call to the prompt
                 )
                 if "tools" in non_default_params:
-                    optional_params["functions_unsupported_model"] = (
+                    optional_params[
-                        non_default_params.pop("tools")
+                        "functions_unsupported_model"
-                    )
+                    ] = non_default_params.pop("tools")
                     non_default_params.pop(
                         "tool_choice", None
                     )  # causes ollama requests to hang
                 elif "functions" in non_default_params:
-                    optional_params["functions_unsupported_model"] = (
+                    optional_params[
-                        non_default_params.pop("functions")
+                        "functions_unsupported_model"
-                    )
+                    ] = non_default_params.pop("functions")
             elif (
                 litellm.add_function_to_prompt
             ):  # if user opts to add it to prompt instead
@@ -3045,11 +3045,11 @@ def get_optional_params(  # noqa: PLR0915
 
     if "response_format" in non_default_params:
         if provider_config is not None:
-            non_default_params["response_format"] = (
+            non_default_params[
-                provider_config.get_json_schema_from_pydantic_object(
+                "response_format"
+            ] = provider_config.get_json_schema_from_pydantic_object(
                 response_format=non_default_params["response_format"]
             )
-            )
         else:
             non_default_params["response_format"] = type_to_response_format_param(
                 response_format=non_default_params["response_format"]
@@ -4064,9 +4064,9 @@ def _count_characters(text: str) -> int:
 
 
 def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) -> str:
-    _choices: Union[List[Union[Choices, StreamingChoices]], List[StreamingChoices]] = (
+    _choices: Union[
-        response_obj.choices
+        List[Union[Choices, StreamingChoices]], List[StreamingChoices]
-    )
+    ] = response_obj.choices
 
     response_str = ""
     for choice in _choices:
@@ -4458,14 +4458,14 @@ def _get_model_info_helper(  # noqa: PLR0915
 
             if combined_model_name in litellm.model_cost:
                 key = combined_model_name
-                _model_info = _get_model_info_from_model_cost(key=key)
+                _model_info = _get_model_info_from_model_cost(key=cast(str, key))
                 if not _check_provider_match(
                     model_info=_model_info, custom_llm_provider=custom_llm_provider
                 ):
                     _model_info = None
             if _model_info is None and model in litellm.model_cost:
                 key = model
-                _model_info = _get_model_info_from_model_cost(key=key)
+                _model_info = _get_model_info_from_model_cost(key=cast(str, key))
                 if not _check_provider_match(
                     model_info=_model_info, custom_llm_provider=custom_llm_provider
                 ):
@@ -4475,21 +4475,21 @@ def _get_model_info_helper(  # noqa: PLR0915
                 and combined_stripped_model_name in litellm.model_cost
             ):
                 key = combined_stripped_model_name
-                _model_info = _get_model_info_from_model_cost(key=key)
+                _model_info = _get_model_info_from_model_cost(key=cast(str, key))
                 if not _check_provider_match(
                     model_info=_model_info, custom_llm_provider=custom_llm_provider
                 ):
                     _model_info = None
             if _model_info is None and stripped_model_name in litellm.model_cost:
                 key = stripped_model_name
-                _model_info = _get_model_info_from_model_cost(key=key)
+                _model_info = _get_model_info_from_model_cost(key=cast(str, key))
                 if not _check_provider_match(
                     model_info=_model_info, custom_llm_provider=custom_llm_provider
                 ):
                     _model_info = None
             if _model_info is None and split_model in litellm.model_cost:
                 key = split_model
-                _model_info = _get_model_info_from_model_cost(key=key)
+                _model_info = _get_model_info_from_model_cost(key=cast(str, key))
                 if not _check_provider_match(
                     model_info=_model_info, custom_llm_provider=custom_llm_provider
                 ):
@@ -6510,7 +6510,12 @@ class ProviderConfigManager:
             return litellm.AnthropicModelInfo()
         elif LlmProviders.XAI == provider:
             return litellm.XAIModelInfo()
+        elif LlmProviders.VLLM == provider:
+            from litellm.llms.vllm.common_utils import (
+                VLLMModelInfo,  # experimental approach, to reduce bloat on __init__.py
+            )
 
+            return VLLMModelInfo()
         return None
 
     @staticmethod