gopidesupavan commented on PR #62816:
URL: https://github.com/apache/airflow/pull/62816#issuecomment-4000582807
> Hi @kaxil
>
> I have taken your "thin hook" suggestion into consideration. However, I
believe that relying solely on environment variables might not be sufficient
for Airflow users; being able to fetch configurations directly from connection
extras is highly valuable.
>
> I've reviewed all the current pydanticai providers and noticed that only
AzureProvider and LiteLLMProvider require their own specific parameters instead
of the standard base_url.
>
> To handle this distinction cleanly without over-engineering or building
registries, I am planning to introduce a simple _build_provider_kwargs(conn)
helper method. This method will parse the connection and return the appropriate
kwargs dictionary to feed directly into the resolved provider_cls.
>
> What are your thoughts on proceeding with this approach? Does this align
better with the design goals?
>
> ```
> def _build_provider_kwargs(
> provider_cls: type,
> api_key: str | None,
> base_url: str | None,
> extra: dict[str, Any],
> ) -> dict[str, Any]:
> sig = inspect.signature(provider_cls.__init__)
> accepted: set[str] = {
> name
> for name, param in sig.parameters.items()
> if name != "self"
> and param.kind
> not in (inspect.Parameter.VAR_POSITIONAL,
inspect.Parameter.VAR_KEYWORD)
> }
>
> candidates: dict[str, Any] = {
> "api_key": api_key,
> "base_url": base_url,
> # Alias used by AzureProvider instead of base_url
> "azure_endpoint": base_url,
> # Alias used by LiteLLMProvider instead of base_url
> "api_base": base_url,
> **{k: v for k, v in extra.items() if k != "model"},
> }
>
> return {k: v for k, v in candidates.items() if k in accepted and v
is not None}
>
> def get_conn(self) -> Model:
> .....
> if api_key or base_url:
> provider_name = model_name.split(":")[0] if ":" in model_name
else "openai"
> provider_cls = infer_provider_class(provider_name)
> kwargs: dict[str, Any] =
self._build_provider_kwargs(provider_cls, api_key, base_url, extra)
> try:
> provider = provider_cls(**kwargs)
> except TypeError:
> provider = infer_provider(provider_name)
> ....
> ```
What if we simply read key-value pairs from conn_extras and pass them
directly to the provider? We could define a schema in the connection extras
like {provider_extra_args: {...}}, then document that this field and point
users to the pydantic-ai docs to configure it correctly themselves ?
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