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fix: resolve type checking errors in vertex_ai and mcp_server_manager
- Fix type incompatibility in vertex_ai/videos/transformation.py by casting litellm_params to Dict[str, Any] - Add await to async add_update_server call in mcp_server_manager.py - Resolves 4 type checking errors across 2 files
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7f991fe897
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2 changed files with 6 additions and 5 deletions
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@ -7,7 +7,7 @@ Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-refer
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import base64
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import time
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from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
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from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast
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import httpx
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from httpx._types import RequestFiles
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@ -174,10 +174,11 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase):
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"""
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# Extract Vertex AI parameters using safe helpers from VertexBase
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# Use safe_get_* methods that don't mutate litellm_params dict
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litellm_params = litellm_params or {}
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# Ensure litellm_params is a dict for type checking
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params_dict: Dict[str, Any] = cast(Dict[str, Any], litellm_params) if litellm_params is not None else {}
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vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=litellm_params)
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vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=litellm_params)
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vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=params_dict)
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vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=params_dict)
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# Get access token from Vertex credentials
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access_token, project_id = self.get_access_token(
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@ -2270,7 +2270,7 @@ class MCPServerManager:
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server.status = "unhealthy"
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## try adding server to registry to get error
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try:
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self.add_update_server(server)
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await self.add_update_server(server)
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except Exception as e:
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server.health_check_error = str(e)
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server.health_check_error = "Server is not in in memory registry yet. This could be a temporary sync issue."
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