litellm_fix(mypy): fix type errors across multiple files

Fixes 23 mypy errors:
- opentelemetry.py: Fix callback_name str|None to str
- files/main.py: Replace wildcard import to avoid uuid redefinition
- responses transformation: Fix Dict type annotations
- gemini/files: Fix validate_environment signature, status Literal type
- experimental_mcp_client: Add type:ignore for conditional import
- completion_extras transformation: Add type:ignore for tools append
- common_request_processing: Add type:ignore for route_type Literal
- search endpoints: Add type:ignore for TypedDict key access
- files_endpoints: Convert FormData to dict for form_data param
- mcp_server_manager: Add type:ignore for conditional function variant
- mcp_management_endpoints: Same conditional function fix
- key_management_endpoints: Add type:ignore for prisma_client arg
- vector_stores endpoints: Add missing team_id and user_id args
- cache_coordinator: Add type:ignore for func-returns-value

enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py

@@ -282,6 +282,8 @@ async def get_vector_store_info(
                     updated_at=vector_store.get("updated_at") or None,
                     litellm_credential_name=vector_store.get("litellm_credential_name"),
                     litellm_params=vector_store.get("litellm_params") or None,
+                    team_id=vector_store.get("team_id"),
+                    user_id=vector_store.get("user_id"),
                 )
                 return {"vector_store": vector_store_pydantic_obj}
 

litellm/completion_extras/litellm_responses_transformation/transformation.py

@@ -748,7 +748,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         if isinstance(web_search_options, dict):
             web_search_tool.update(web_search_options)
 
-        responses_api_request["tools"].append(web_search_tool)
+        responses_api_request["tools"].append(web_search_tool)  # type: ignore[union-attr, arg-type]
 
     def _transform_response_format_to_text_format(
         self, response_format: Union[Dict[str, Any], Any]

litellm/experimental_mcp_client/client.py

@@ -14,7 +14,7 @@ from mcp.client.stdio import stdio_client
 try:
     from mcp.client.streamable_http import streamable_http_client  # type: ignore
 except ImportError:
-    streamable_http_client = None
+    streamable_http_client = None  # type: ignore[assignment]
 from mcp.types import CallToolRequestParams as MCPCallToolRequestParams
 from mcp.types import CallToolResult as MCPCallToolResult
 from mcp.types import (

litellm/files/main.py

@@ -34,7 +34,7 @@ from litellm.types.llms.openai import (
     HttpxBinaryResponseContent,
     OpenAIFileObject,
 )
-from litellm.types.router import *
+import litellm.types.router  # noqa: F401 - side effects needed
 from litellm.types.utils import (
     OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS,
     LlmProviders,

litellm/integrations/opentelemetry.py

@@ -1631,7 +1631,7 @@ class OpenTelemetry(CustomLogger):
                                     )
 
         except Exception as e:
-            self.handle_callback_failure(callback_name= self.callback_name)  
+            self.handle_callback_failure(callback_name=self.callback_name or "opentelemetry")  
             verbose_logger.exception(
                 "OpenTelemetry logging error in set_attributes %s", str(e)
             )

litellm/llms/gemini/files/transformation.py

@@ -4,7 +4,7 @@ Supports writing files to Google AI Studio Files API.
 For vertex ai, check out the vertex_ai/files/handler.py file.
 """
 import time
-from typing import List, Optional
+from typing import List, Literal, Optional, cast
 
 import httpx
 from openai.types.file_deleted import FileDeleted
@@ -37,12 +37,13 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
 
     def validate_environment(
         self,
-        api_key: Optional[str],
         headers: dict,
         model: str,
         messages: list,
         optional_params: dict,
         litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
     ) -> dict:
         """
         Validate environment and add Gemini API key to headers.
@@ -236,7 +237,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
             
             # Map Gemini state to OpenAI status
             gemini_state = response_json.get("state", "STATE_UNSPECIFIED")
-            status = "uploaded" # Default
+            status: Literal["uploaded", "processed", "error"] = "uploaded"  # Default
             if gemini_state == "ACTIVE":
                 status = "processed"
             elif gemini_state == "FAILED":
@@ -301,7 +302,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
         url = f"{api_base}/v1beta/{file_name}"
         
         # Add API key as header (Google AI Studio uses x-goog-api-key header)
-        params = {}
+        params: dict = {}
         
         return url, params
 

litellm/proxy/_experimental/mcp_server/mcp_server_manager.py

@@ -69,7 +69,7 @@ try:
 except ImportError:
     SEP_986_URL = "https://github.com/modelcontextprotocol/protocol/blob/main/proposals/0001-tool-name-validation.md"
 
-    def validate_tool_name(name: str):
+    def validate_tool_name(name: str):  # type: ignore[misc]
         from pydantic import BaseModel
 
         class MockResult(BaseModel):

litellm/proxy/common_request_processing.py

@@ -751,7 +751,7 @@ class ProxyBaseLLMRequestProcessing:
         # Do not change this - it should be a constant time fetch - ALWAYS
         llm_call = await route_request(
             data=self.data,
-            route_type=route_type,
+            route_type=route_type,  # type: ignore[arg-type]
             llm_router=llm_router,
             user_model=user_model,
         )

litellm/proxy/common_utils/cache_coordinator.py

@@ -172,7 +172,7 @@ class EventDrivenCacheCoordinator:
         Returns the value from cache or from load_fn, or None if load failed or
         cache was still empty after waiting.
         """
-        value = await self._get_cached(cache_key, cache)
+        value = await self._get_cached(cache_key, cache)  # type: ignore[func-returns-value]
         if value is not None:
             self._log_cache_hit(value)
             return value

litellm/proxy/management_endpoints/key_management_endpoints.py

@@ -1544,7 +1544,7 @@ async def _process_single_key_update(
     await TeamMemberPermissionChecks.can_team_member_execute_key_management_endpoint(
         user_api_key_dict=user_api_key_dict,
         route=KeyManagementRoutes.KEY_UPDATE,
-        prisma_client=prisma_client,
+        prisma_client=prisma_client,  # type: ignore[arg-type]
         existing_key_row=existing_key_row,
         user_api_key_cache=user_api_key_cache,
     )

litellm/proxy/management_endpoints/mcp_management_endpoints.py

@@ -60,7 +60,7 @@ if MCP_AVAILABLE:
         from mcp.shared.tool_name_validation import validate_tool_name  # type: ignore
     except ImportError:
 
-        def validate_tool_name(name: str):
+        def validate_tool_name(name: str):  # type: ignore[misc]
             from pydantic import BaseModel
 
             class MockResult(BaseModel):

litellm/proxy/openai_files_endpoints/files_endpoints.py

@@ -361,7 +361,7 @@ async def create_file(  # noqa: PLR0915
         expires_after = None
         form_data = await request.form()
         litellm_metadata = extract_nested_form_metadata(
-            form_data=form_data,
+            form_data=dict(form_data),
             prefix="litellm_metadata["
         )
         expires_after_anchor = form_data.get("expires_after[anchor]")

litellm/proxy/search_endpoints/endpoints.py

@@ -245,8 +245,8 @@ async def list_search_tools(
                 }
                 
                 # Add description if available
-                if "search_tool_info" in tool and tool["search_tool_info"]:
-                    description = tool["search_tool_info"].get("description")
+                if "search_tool_info" in tool and tool["search_tool_info"]:  # type: ignore[typeddict-item]
+                    description = tool["search_tool_info"].get("description")  # type: ignore[typeddict-item]
                     if description:
                         tool_info["description"] = description
                 

litellm/responses/litellm_completion_transformation/transformation.py

@@ -1743,7 +1743,7 @@ class LiteLLMCompletionResponsesConfig:
             and usage.prompt_tokens_details is not None
         ):
             prompt_details = usage.prompt_tokens_details
-            input_details_dict: Dict[str, Optional[int]] = {}
+            input_details_dict: Dict[str, int] = {}
 
             if (
                 hasattr(prompt_details, "cached_tokens")
@@ -1776,7 +1776,7 @@ class LiteLLMCompletionResponsesConfig:
             and usage.completion_tokens_details is not None
         ):
             completion_details = usage.completion_tokens_details
-            output_details_dict: Dict[str, Optional[int]] = {}
+            output_details_dict: Dict[str, int] = {}
             if (
                 hasattr(completion_details, "reasoning_tokens")
                 and completion_details.reasoning_tokens is not None