Teknium
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
"""Google Vertex AI provider profile: Gemini via Google Cloud's OpenAI-compatible
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endpoint.
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Auth is OAuth2 (service-account JSON or ADC), not a static key: ``agent/
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vertex_adapter.py`` mints ``(token, base_url)`` and the token is passed as
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``api_key``. ``auth_type="vertex"`` keeps it out of the api_key provider path so
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a credentials-file path is never mistaken for a key.
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"""
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from typing import Any
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from providers import register_provider
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from providers.base import ProviderProfile
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class VertexProfile(ProviderProfile):
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"""Vertex AI — reuse Gemini's thinking_config translation for extra_body."""
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def build_extra_body(self, *, session_id: str | None = None, **context: Any) -> dict[str, Any]:
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"""Emit ``extra_body.google.thinking_config`` like the ``gemini`` provider's
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OpenAI-compat subpath."""
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from agent.transports.chat_completions import (
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_build_gemini_thinking_config,
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_snake_case_gemini_thinking_config,
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)
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raw = _build_gemini_thinking_config(context.get("model") or "", context.get("reasoning_config"))
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thinking_config = _snake_case_gemini_thinking_config(raw) if raw else None
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if not thinking_config:
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return {}
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return {"extra_body": {"google": {"thinking_config": thinking_config}}}
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def fetch_models(
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self, *, api_key: str | None = None, base_url: str | None = None, timeout: float = 8.0
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) -> list[str] | None:
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"""No ``/models`` route on the OpenAI-compat endpoint; setup ships a curated list."""
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return None
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vertex = VertexProfile(
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name="vertex",
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aliases=("google-vertex", "vertex-ai", "gcp-vertex"),
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api_mode="chat_completions",
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env_vars=(), # OAuth2 via service account / ADC — not a static key env var
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base_url="https://aiplatform.googleapis.com", # real base_url computed at runtime
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auth_type="vertex",
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default_aux_model="google/gemini-3.6-flash",
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)
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register_provider(vertex)
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