cetingokhan commented on code in PR #62816:
URL: https://github.com/apache/airflow/pull/62816#discussion_r2901762480
##########
providers/common/ai/src/airflow/providers/common/ai/hooks/pydantic_ai.py:
##########
@@ -75,62 +78,89 @@ def get_ui_field_behaviour() -> dict[str, Any]:
"hidden_fields": ["schema", "port", "login"],
"relabeling": {"password": "API Key"},
"placeholders": {
- "host": "https://api.openai.com/v1 (optional, for custom
endpoints)",
+ "host": "https://api.openai.com/v1 (optional, for custom
endpoints / Ollama)",
+ "extra": '{"model": "openai:gpt-5.3"}',
},
}
+ # ------------------------------------------------------------------
+ # Core connection / agent API
+ # ------------------------------------------------------------------
+
+ def _get_provider_kwargs(
+ self,
+ api_key: str | None,
+ base_url: str | None,
+ extra: dict[str, Any],
+ ) -> dict[str, Any]:
+ """
+ Return the kwargs to pass to the provider constructor.
+
+ Subclasses override this method to map their connection fields to the
+ parameters expected by their specific provider class. The base
+ implementation handles the common ``api_key`` / ``base_url`` pattern
+ used by OpenAI, Anthropic, Groq, Mistral, Ollama, and most other
+ providers.
+
+ :param api_key: Value of ``conn.password``.
+ :param base_url: Value of ``conn.host``.
+ :param extra: Deserialized ``conn.extra`` JSON.
+ :return: Kwargs forwarded to ``provider_cls(**kwargs)``. Empty dict
+ signals that no explicit credentials are available and the hook
+ should fall back to environment-variable–based auth.
+ """
+ kwargs: dict[str, Any] = {}
+ if api_key:
+ kwargs["api_key"] = api_key
+ if base_url:
+ kwargs["base_url"] = base_url
+ return kwargs
+
def get_conn(self) -> Model:
"""
- Return a configured pydantic-ai Model.
+ Return a configured pydantic-ai ``Model``.
- Reads API key from connection password, base_url from connection host,
- and model from (in priority order):
+ Resolution order:
- 1. ``model_id`` parameter on the hook
- 2. ``extra["model"]`` on the connection (set by the "Model" conn-field
in the UI)
+ 1. **Explicit credentials** — when :meth:`_get_provider_kwargs` returns
+ a non-empty dict the provider class is instantiated with those
kwargs
+ and wrapped in a ``provider_factory``.
+ 2. **Default resolution** — delegates to pydantic-ai ``infer_model``
+ which reads standard env vars (``OPENAI_API_KEY``, ``AWS_PROFILE``,
…).
- The result is cached for the lifetime of this hook instance.
+ The resolved model is cached for the lifetime of this hook instance.
"""
if self._model is not None:
return self._model
conn = self.get_connection(self.llm_conn_id)
- model_name: str | KnownModelName = self.model_id or
conn.extra_dejson.get("model", "")
+
+ extra: dict[str, Any] = conn.extra_dejson
+ model_name: str | KnownModelName = self.model_id or extra.get("model",
"")
if not model_name:
raise ValueError(
"No model specified. Set model_id on the hook or the Model
field on the connection."
)
- api_key = conn.password
- base_url = conn.host or None
- if not api_key and not base_url:
- # No credentials to inject — use default provider resolution
- # (picks up env vars like OPENAI_API_KEY, AWS_PROFILE, etc.)
- self._model = infer_model(model_name)
+ api_key: str | None = conn.password or None
+ base_url: str | None = conn.host or None
+
+ provider_kwargs = self._get_provider_kwargs(api_key, base_url, extra)
+ if provider_kwargs:
+ _kwargs = provider_kwargs # capture for closure
+ self.log.info(
+ "Using explicit credentials for provider with model '%s': %s",
+ model_name,
+ list(provider_kwargs),
+ )
+
+ def _provider_factory(pname: str) -> Any:
+ return infer_provider_class(pname)(**_kwargs)
+
+ self._model = infer_model(model_name,
provider_factory=_provider_factory)
return self._model
Review Comment:
I replaced with following;
```
def _provider_factory(pname: str) -> Any:
try:
return infer_provider_class(pname)(**_kwargs)
except TypeError:
return infer_provider(pname)
```
?
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