Replace the OpenAI-compatible endpoint approach with the native
google-genai SDK for Vertex AI. This eliminates the custom token
refresher, TokenInjectingTransport, and async lifecycle complexity
while also removing the 8192 output token cap that the OpenAI
endpoint enforced.
Changes:
- vertexai provider now uses genai.Client(vertexai=True) instead of
AsyncOpenAI with token-injecting transport
- Routes through existing _call_gemini/_call_with_tools_gemini paths
- Strips google/ prefix from model names (native SDK uses bare names)
- Preserves service account key auth via credentials parameter
- Delete vertexai_token_refresher.py (no longer needed)
- Strip markdown code fences in consolidator JSON parsing
- Rewrite vertexai tests for native SDK integration
* feat: support vertex as llm provider
* fix
* fix: add uv index-strategy to resolve dependency conflicts with pytorch index
When using pytorch index for faster torch downloads in CI,
filelock dependency resolution was failing because pytorch index
only has older versions. Adding unsafe-best-match strategy allows
uv to search all configured indexes.
Also fix type checking warnings from ty.
* fix: add index-strategy to root pyproject.toml for workspace-level uv resolution
* chore: regenerate client SDKs after Vertex AI support
