Add quick start guide to common.ai provider docs#69552
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also adding a few screenshots for reference. thanks! |
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New docs/quickstart.rst walks a new user from install through the Pydantic AI connection to a first @task.llm Dag, linking into the existing guides; wired as the first Guides toctree entry.
The install step omitted the model SDK extra, so following the guide as written raised ImportError on the quickstart's own example. The connection example also double-encoded the extra field as a JSON string instead of the nested object Connection.from_json already accepts. Also clarifies the opener and adds a prerequisites note.
The quickstart's runnable Dag was inline code-block, so it wasn't covered by the example-dag import test and duplicated the howto_decorator_llm block in example_llm.py. Move it to example_quickstart.py (using airflow.sdk directly, since this provider requires Airflow 3.0+) and reference it from the doc via exampleinclude, so it stays in sync with what actually runs.
Only the model-SDK extras (openai/anthropic/google/bedrock) are worth calling out in the quickstart's install step; point readers to the provider's full extras table in index.rst instead of duplicating it, since that table already stays in sync with pyproject.toml at release time.
pydantic-ai supports more model providers than the four extras the docs call out, each under its own extra name; point readers at the upstream slim-install list so they can find the one matching their provider.
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Why
The
common.aiprovider has reference docs (connections, hooks, operators) but no single entry point that guides a new user from installation to their first running LLM task. This adds that on-ramp so someone landing on the provider docs has an obvious first page to follow.What
providers/common/ai/docs/quickstart.rst— a three-step guide:pydanticaiconn type,provider:modelformat, API key in the password field)
@task.llmDag and run it withairflow dags testoutput_typewith a PydanticBaseModel) briefly,deferring the full example to the existing
howto/operator:llmguide.agent guide, and observability.
Was generative AI tooling used to co-author this PR?
Generated-by: [Claude] following the guidelines
{pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.