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Posted to commits@airflow.apache.org by "YAshhh29 (via GitHub)" <gi...@apache.org> on 2026/07/13 10:12:14 UTC
[PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
YAshhh29 opened a new pull request, #69812:
URL: https://github.com/apache/airflow/pull/69812
The `OpenAIResponseOperator` today only returns the aggregated `output_text`
of a Responses API call. Users who want a structured JSON output — the pattern
every AI-eng team uses for reliable extraction, tool selection, and downstream
DAG chaining — have to bypass the operator entirely. The operator's own
docstring instructs this:
> The operator is synchronous and returns the response's aggregated output text. For
> `previous_response_id` chaining, `background=True` responses, or **access to the full
> structured response, use `OpenAIHook` directly**.
This PR closes that gap. Pass a Pydantic `BaseModel` subclass as `text_format` and
the operator uses the SDK's structured-output path (`responses.parse`) and returns
`output_parsed.model_dump()` — a plain `dict`, XCom-safe, no manual serialization
dance. When `text_format` is `None` the existing plain-text behavior is preserved
bit-for-bit.
### How I found and verified this gap
Same audit approach as #69408, #69534, and #69673: read every operator/hook in the
AI/ML providers and compare surfaced API to underlying SDK. The Responses API is
OpenAI's recommended interface going forward (Chat Completions is on the deprecation
path), so gaps here are user-facing today, not legacy cleanup.
Before writing a line of code I confirmed:
1. **The API exists and is stable.** `openai.resources.responses.Responses.parse(input, model, text_format, ...)` is present in the pinned SDK (`openai>=2.37.0`) and returns `ParsedResponse[TextFormatT]` with `.output_parsed` as an instance of the passed model. Verified via `inspect.signature(Responses.parse)`.
2. **`output_parsed` is None on refusal / incomplete.** The SDK docs and source confirm the model returning a refusal or the response being truncated yields `output_parsed=None`. The operator raises `ValueError` in that case so downstream tasks don't silently get `None`.
3. **`model_dump()` produces XCom-safe dicts.** Every pinned Pydantic version supports it.
4. **`pydantic` is already a core Airflow dependency.** No new dep.
### Design decisions
- **Pydantic-only, not raw JSON schema.** The SDK's `responses.parse(text_format=Model)` path handles schema conversion, strict-mode, and response parsing in one call. Raw JSON schema via `responses.create(text={"format": {...}})` requires the caller to define a schema name, parse `output_text` manually, and handle refusals themselves. Pydantic is the pattern every AI-eng library (Instructor, LangChain, Pydantic-AI) wraps — starting there covers the primary use case cleanly. Raw-schema support can be added in a follow-up without breaking this API.
- **One operator, opt-in param — not a new class.** `text_format=None` (default) keeps the existing `str`-returning behavior. Set `text_format=SomeModel` and you get a `dict`. Return-type annotation is `str | dict[str, Any]`. Users who don't touch the new param see zero change.
- **`ValueError` on refusal, not silent None.** If `output_parsed is None`, the operator raises with the response ID and status. Silently returning `None` would break downstream tasks that assume a specific shape.
- **No new exception class.** Per `AGENTS.md` guidance ("prefer a Python built-in"), `ValueError` is semantically correct — the model returned something that can't be converted to the requested type.
- **`create_response` untouched.** All existing tests, DAGs, and behavior are preserved. The plain-text path is not touched.
### What changes
- `providers/openai/src/airflow/providers/openai/hooks/openai.py`
- New `OpenAIHook.parse_response(input, text_format, model, **kwargs) -> ParsedResponse[Any]`, a thin wrapper over `client.responses.parse`.
- `providers/openai/src/airflow/providers/openai/operators/openai.py`
- `OpenAIResponseOperator` gains a `text_format: type[BaseModel] | None = None` parameter. Return type widened to `str | dict[str, Any]`. Docstring updated to explain the two paths.
- `providers/openai/tests/unit/openai/hooks/test_openai.py`
- `test_parse_response`: hook forwards `text_format`, `model`, and extra kwargs to `conn.responses.parse` and returns the SDK's return value.
- `providers/openai/tests/unit/openai/operators/test_openai.py`
- `test_openai_response_operator_structured_output_returns_dict`: happy path — parsed model → dict via `model_dump()`, `create_response` not called.
- `test_openai_response_operator_structured_output_refusal_raises`: `output_parsed=None` → `ValueError` with the response ID.
- The existing `test_openai_response_operator_execute` is left untouched to guard the backward-compat path.
- `providers/openai/tests/system/openai/example_openai.py`
- New `# [START/END] howto_operator_openai_response_structured` block with a Pydantic `Person` example.
- `providers/openai/docs/operators/openai.rst`
- New **Structured outputs (Pydantic models)** subsection under the existing `OpenAIResponseOperator` how-to, with an `exampleinclude` pointing at the new markers.
No `provider.yaml` / `get_provider_info.py` changes needed: the registry lists python-modules, not classes, and no new module was added. No changelog edit either — per `AGENTS.md`, provider changelogs are regenerated from `git log` by the release manager.
### Testing
- **Full-provider `ruff check` + `ruff format --check`**: 29 files clean.
- **All hook + operator tests exercised via a standalone Windows-safe harness** stubbing airflow, `openai.auth`, and `openai.types.responses.ParsedResponse` (full airflow install cannot run on Windows). Four assertions pass: hook forwards args, operator structured path returns dict + skips `create_response`, operator refusal path raises `ValueError`, operator backward-compat plain-text path is unchanged.
- **Regression check**: the existing `test_openai_response_operator_execute` was not modified, so the plain-text return path is guarded by a test the reviewer can fail by reverting the PR.
- **Line-by-line reviewed by me** against every rule in `.github/instructions/code-review.instructions.md` — no red flags (no `time.time`, no `assert` in prod, no new `AirflowException`, no `mock.Mock()` without spec, imports at top of file, session-parameter and DB-query rules N/A here).
### Author review
I read every line of this PR — hook, operator, tests, example DAG, docs — end
to end before pushing, and validated the SDK contract claims myself against
the installed `openai` package (`inspect.signature(Responses.parse)`, the
`ParsedResponse.output_parsed` attribute check, and the `openai>=2.37.0`
availability of both). The design calls above — Pydantic-only for this first
cut, `ValueError` on refusal instead of silent `None`, opt-in `text_format`
param instead of a new class — are mine, not the agent's default choices.
The generated code was iterated on until it matched what I wanted to ship.
---
##### Was generative AI tooling used to co-author this PR?
- [X] Yes — GitHub Copilot (Claude Opus 4.7)
Generated-by: GitHub Copilot (Claude Opus 4.7) following [the guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5264630532
@kaxil, gentle follow-up on this one. I addressed the review findings, reran the sample Dag against a from-source main build and the real OpenAI Responses API, and posted the Grid, logs, and XCom evidence above. Current checks are green and the PR is mergeable.
Could you take another look when convenient, or let me know what still needs work?
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on code in PR #69812:
URL: https://github.com/apache/airflow/pull/69812#discussion_r3682396032
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +122,55 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.response_kwargs = response_kwargs or {}
+ self.text_format = text_format
@cached_property
def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
- def execute(self, context: Context) -> str:
+ def execute(self, context: Context) -> str | dict[str, Any]:
+ if self.text_format is not None:
+ try:
+ parsed = self.hook.parse_response(
+ input=self.input_text,
+ model=self.model,
+ text_format=self.text_format,
+ **self.response_kwargs,
+ )
+ except ValidationError as exc:
+ # ``responses.parse`` raises ``ValidationError`` when the model's JSON output
+ # can't be coerced into ``text_format`` — most commonly because the response
+ # was truncated (e.g. ``max_output_tokens`` hit) mid-JSON. Convert to a clean
+ # ``ValueError`` so callers see a consistent shape across all parse failures.
+ raise ValueError(
+ f"OpenAI Responses API returned a payload that does not match "
+ f"{self.text_format.__name__!r}: {exc}"
+ ) from exc
+
+ self.log.info("Generated response %s", parsed.id)
+ if parsed.output_parsed is None:
+ # No structured output — the model refused, the request errored, or the response
+ # was incomplete. Surface a clear error so downstream tasks don't get ``None``,
+ # and include what the API already told us so the user does not need a follow-up
+ # ``OpenAIHook.get_response`` call.
+ details: list[str] = [f"status={parsed.status!r}"]
+ if parsed.error is not None:
+ details.append(f"error={parsed.error!r}")
+ if parsed.incomplete_details is not None:
+ details.append(f"incomplete_details={parsed.incomplete_details!r}")
+ raise ValueError(
Review Comment:
Following up on the details added here: for an actual refusal the API returns `status='completed'` with `error` and `incomplete_details` both `None` -- the SDK passes `refusal` content items through unparsed and `output_parsed` comes back `None` (checked on openai 2.46.0, `lib/_parsing/_responses.py`). So for the headline failure mode this message degrades to just `status='completed'` with no reason, while the model's explanation sits unread in `parsed.output[].content[].refusal`. Scanning `parsed.output` for refusal content and appending that text (and, failing that, the output item types -- a tools-only response also lands here) would make the message useful exactly when it matters.
Relatedly, the refusal test fabricates `status='incomplete'` plus string `error`/`incomplete_details`, a combination the API can't emit -- a true refusal-shaped test (`status='completed'`, both fields `None`, refusal item in `output`) would pin this, and the existing one fits better renamed to `..._incomplete_raises` with realistic `ResponseError`/`IncompleteDetails` values.
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +122,55 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.response_kwargs = response_kwargs or {}
+ self.text_format = text_format
@cached_property
def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
- def execute(self, context: Context) -> str:
+ def execute(self, context: Context) -> str | dict[str, Any]:
+ if self.text_format is not None:
+ try:
+ parsed = self.hook.parse_response(
+ input=self.input_text,
+ model=self.model,
+ text_format=self.text_format,
+ **self.response_kwargs,
+ )
+ except ValidationError as exc:
+ # ``responses.parse`` raises ``ValidationError`` when the model's JSON output
+ # can't be coerced into ``text_format`` — most commonly because the response
+ # was truncated (e.g. ``max_output_tokens`` hit) mid-JSON. Convert to a clean
+ # ``ValueError`` so callers see a consistent shape across all parse failures.
+ raise ValueError(
+ f"OpenAI Responses API returned a payload that does not match "
+ f"{self.text_format.__name__!r}: {exc}"
+ ) from exc
+
+ self.log.info("Generated response %s", parsed.id)
+ if parsed.output_parsed is None:
Review Comment:
One gap left on this path: the SDK parses output regardless of response status, so a `status='incomplete'` response whose emitted JSON still validates (a truncated list field with no min-length constraint, or `content_filter` cutting after a complete object) sails through here and pushes partial data to XCom as task success. The window is narrow -- truncation usually breaks the JSON and lands in the `ValidationError` branch -- but the plain-text path below does check `response.status != "completed"` while this one doesn't. A one-line status guard before this check, reusing the same details harvesting, closes it; worth a regression test that `status='incomplete'` with a valid parsed model raises.
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +122,55 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
Review Comment:
Two smaller ones on this param: the SDK also accepts dataclass-like types (anything with `__pydantic_config__`), so a `@pydantic.dataclass` passed here completes the billed API call and then hits `AttributeError` on `model_dump`, uncaught by the `ValidationError` handler. An `issubclass(text_format, BaseModel)` check in `__init__` fails at parse time instead (needs `BaseModel` imported at runtime rather than under `TYPE_CHECKING`). And the class docstring and this `:param:` entry repeat the same `model_dump(mode="json")`/XCom rationale -- one of the two can go.
##########
providers/openai/tests/unit/openai/operators/test_openai.py:
##########
@@ -104,6 +106,141 @@ def test_openai_response_operator_execute():
)
+class _StructuredPerson(BaseModel):
+ """Pydantic model used by the structured-output operator tests."""
+
+ name: str
+
+
+class _Priority(str, Enum):
+ LOW = "low"
+ HIGH = "high"
+
+
+class _StructuredTask(BaseModel):
+ """Pydantic model with an enum field — exercises ``model_dump(mode="json")``.
+
+ A plain ``enum.Enum`` field returned as a live enum instance (``model_dump``'s default
+ ``mode="python"``) is not XCom-safe: Airflow's serde only unwraps enums that mix in
+ ``str``/``int``. Using ``mode="json"`` renders the enum via its JSON representation.
+ """
+
+ title: str
+ priority: _Priority
+
+
+def test_openai_response_operator_structured_output_returns_dict():
+ operator = OpenAIResponseOperator(
+ task_id=TASK_ID,
+ conn_id=CONN_ID,
+ input_text="Extract: Alice",
+ model="test_model",
+ text_format=_StructuredPerson,
+ response_kwargs={"instructions": "Be precise."},
+ )
+ mock_hook_instance = Mock(spec=OpenAIHook)
+ mock_hook_instance.parse_response.return_value = Mock(
Review Comment:
A few test-fidelity items in this block. `Mock(spec=ParsedResponse)` gives no real spec protection: pydantic v2 keeps declared fields out of the class namespace, so `id`/`status`/`error`/`incomplete_details` aren't in the spec and constructor kwargs bypass it anyway -- an SDK field rename would break production with all four tests still green. Constructing real `ParsedResponse` instances (`.construct()` with real output items) fixes that and forces realistic field shapes at the same time.
Below, the ValidationError setup: `pydantic_core.ValidationError.from_exception_data` is public (the comment saying no public constructor exists isn't right), but simpler still is `with pytest.raises(ValidationError) as exc_info: _StructuredPerson.model_validate({})` in place of the six-line try/except/else. And on the comment at line 182 -- a pydantic serde helper does exist and does this exact `model_dump(mode="json")`; the real advantage of returning a dict is that readers need no `allowed_deserialization_classes` config.
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +122,55 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.response_kwargs = response_kwargs or {}
+ self.text_format = text_format
@cached_property
def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
- def execute(self, context: Context) -> str:
+ def execute(self, context: Context) -> str | dict[str, Any]:
+ if self.text_format is not None:
+ try:
+ parsed = self.hook.parse_response(
+ input=self.input_text,
+ model=self.model,
+ text_format=self.text_format,
+ **self.response_kwargs,
+ )
+ except ValidationError as exc:
+ # ``responses.parse`` raises ``ValidationError`` when the model's JSON output
+ # can't be coerced into ``text_format`` — most commonly because the response
+ # was truncated (e.g. ``max_output_tokens`` hit) mid-JSON. Convert to a clean
+ # ``ValueError`` so callers see a consistent shape across all parse failures.
+ raise ValueError(
Review Comment:
This branch raises before a `ParsedResponse` exists, so the message carries no response id and no `incomplete_details` -- users can't even run `hook.get_response(id)` to confirm `reason='max_output_tokens'`, and the guide now promises "``ValueError`` with the API-reported ``status``, ``error`` and ``incomplete_details``" for this case too. Cheapest fix: name truncation (`max_output_tokens`) as the likely cause in this message, and split the rst sentence to describe the two branches separately.
##########
providers/openai/tests/unit/openai/operators/test_openai.py:
##########
@@ -104,6 +106,141 @@ def test_openai_response_operator_execute():
)
+class _StructuredPerson(BaseModel):
+ """Pydantic model used by the structured-output operator tests."""
+
+ name: str
+
+
+class _Priority(str, Enum):
Review Comment:
The `str` mixin defeats this regression test: with the default `mode="python"`, `model_dump` returns the live `_Priority.HIGH`, but str-enum equality makes `result == {..., "priority": "high"}` pass and `isinstance(result["priority"], str)` is also true, so reverting `model_dump(mode="json")` keeps the test green (verified by running it against a revert). Dropping the mixin (`class _Priority(Enum)`) makes it fail on revert, and matches the docstring's own point that str/int-mixin enums were already safe.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on code in PR #69812:
URL: https://github.com/apache/airflow/pull/69812#discussion_r3574140539
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +120,39 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.response_kwargs = response_kwargs or {}
+ self.text_format = text_format
@cached_property
def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
- def execute(self, context: Context) -> str:
+ def execute(self, context: Context) -> str | dict[str, Any]:
+ if self.text_format is not None:
+ parsed = self.hook.parse_response(
+ input=self.input_text,
+ model=self.model,
+ text_format=self.text_format,
+ **self.response_kwargs,
+ )
+ self.log.info("Generated response %s", parsed.id)
+ if parsed.output_parsed is None:
+ # No structured output — the model refused, the request errored, or the response
+ # was truncated. Surface a clear error so downstream tasks don't get None.
Review Comment:
The "response was truncated" case isn't actually covered by the `None` check: when output is cut off mid-JSON (e.g. `max_output_tokens` hit), `responses.parse()` raises `pydantic.ValidationError` from its internal parse step (checked on openai 2.37.0: `parse_text` calls `model_parse_json` with no try/except). That happens inside `self.hook.parse_response(...)` before `parsed` is assigned, so the response id is never logged and this message never fires; users get a raw pydantic traceback instead. Wrapping the `parse_response` call in `try/except ValidationError` and re-raising the same shaped `ValueError` would make this comment and the guide's claim hold.
And for the cases that do reach this raise, the response already carries the detail (`parsed.error`, `parsed.incomplete_details`, refusal text in `parsed.output`) -- including it in the message would save users the `get_response` round trip.
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -84,16 +86,24 @@ class OpenAIResponseOperator(BaseOperator):
"""
Operator that generates a model response using the OpenAI Responses API.
- The operator is synchronous and returns the response's aggregated output text. For
- ``previous_response_id`` chaining, ``background=True`` responses, or access to the full
- structured response, use :class:`~airflow.providers.openai.hooks.openai.OpenAIHook` directly.
+ By default the operator is synchronous and returns the response's aggregated output text.
+ Pass ``text_format`` (a Pydantic ``BaseModel`` subclass) to request a structured output; the
+ operator then returns the parsed model as a ``dict`` (via ``model_dump()``), which is safe to
+ push to XCom. Requires a model that supports structured outputs (``gpt-4o-2024-08-06`` and
Review Comment:
This reads as if the default model doesn't qualify, but `gpt-4o-mini` (the default here and in `parse_response`) has supported structured outputs since its initial release -- per OpenAI's structured outputs guide it's the gpt-4o-mini, gpt-4o-mini-2024-07-18, and gpt-4o-2024-08-06 snapshots and later. The same claim repeats in the hook docstring, the rst guide, and the example DAG (which overrides `model="gpt-4o-2024-08-06"` with a comment saying it's required, when the default already works). I'd drop the specific-model claim (it will rot as new models ship) or note that the default already supports it.
One more thing in this docstring: this line says to use the hook directly for `previous_response_id` chaining, but the `response_kwargs` description below lists `previous_response_id` as a supported passthrough. One of the two should give.
##########
providers/openai/src/airflow/providers/openai/hooks/openai.py:
##########
@@ -248,6 +249,29 @@ def create_response(self, input: Any, model: str = "gpt-4o-mini", **kwargs: Any)
"""
return self.conn.responses.create(model=model, input=input, **kwargs)
+ def parse_response(
+ self,
+ input: Any,
+ text_format: type[BaseModel],
+ model: str = "gpt-4o-mini",
+ **kwargs: Any,
+ ) -> ParsedResponse[Any]:
Review Comment:
The SDK's `parse` is generic (`ParsedResponse[TextFormatT]`), so you can keep that instead of hardcoding `ParsedResponse[Any]`: `T = TypeVar("T", bound=BaseModel)`, `text_format: type[T]`, return `ParsedResponse[T]` -- then `output_parsed` is typed as `T | None` at call sites. Also, can you add `parse_response` to the hook-methods list in `docs/operators/openai.rst` alongside the other Responses methods?
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -110,20 +120,39 @@ def __init__(
input_text: str | list[Any],
model: str = "gpt-4o-mini",
response_kwargs: dict | None = None,
+ text_format: type[BaseModel] | None = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.input_text = input_text
self.model = model
self.response_kwargs = response_kwargs or {}
+ self.text_format = text_format
@cached_property
def hook(self) -> OpenAIHook:
"""Return an instance of the OpenAIHook."""
return OpenAIHook(conn_id=self.conn_id)
- def execute(self, context: Context) -> str:
+ def execute(self, context: Context) -> str | dict[str, Any]:
+ if self.text_format is not None:
+ parsed = self.hook.parse_response(
+ input=self.input_text,
+ model=self.model,
+ text_format=self.text_format,
+ **self.response_kwargs,
+ )
+ self.log.info("Generated response %s", parsed.id)
+ if parsed.output_parsed is None:
+ # No structured output — the model refused, the request errored, or the response
+ # was truncated. Surface a clear error so downstream tasks don't get None.
+ raise ValueError(
+ f"Response {parsed.id} did not produce a parseable structured output "
+ f"(status={parsed.status!r}). Inspect the response via OpenAIHook.get_response "
+ f"for refusal / error details."
+ )
+ return parsed.output_parsed.model_dump()
Review Comment:
`model_dump()` defaults to python mode, so non-JSON field types come back as live Python objects. A model with a plain `enum.Enum` field (a common shape for classification-style structured outputs) then fails at XCom push with `TypeError: cannot serialize object of type <enum 'Priority'>`, because Airflow's serde only unwraps enums that mix in `str`/`int` -- and by then the API call has already been paid for. `model_dump(mode="json")` fixes this and is what Airflow's own pydantic serializer does ([serde/serializers/pydantic.py](https://github.com/apache/airflow/blob/a1cec525543c048b492f617e174b6c862664e55e/task-sdk/src/airflow/sdk/serde/serializers/pydantic.py#L49)), making the docstring's XCom-safe claim hold for any model.
##########
providers/openai/tests/unit/openai/hooks/test_openai.py:
##########
@@ -315,6 +315,28 @@ def test_create_response(mock_openai_hook):
assert result is expected
+def test_parse_response(mock_openai_hook):
+ from pydantic import BaseModel
Review Comment:
Can you move this import to the module top? pydantic is a hard dependency (the openai SDK requires it), and the operator test file in this PR already imports it top-level.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-6089210638
Thanks a lot @kaxil for the approval, and for sticking with this through all the review rounds. I learned a lot about how the operator should behave from your comments.
Both of your notes are fair, so I've addressed them in a small follow-up: #74519
- The class docstring now carries the same caveat as the guide: when the SDK raises `ValidationError` there is no response object, so neither key is pushed.
- `test_rejected_response_still_records_id_and_usage` is parametrized with an incomplete response as well as a refusal, so it pins that the push happens before the status check.
I also see this is in the openai 2.1.0rc1 testing list (#74459), so I'll test the RC and report back there.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil merged PR #69812:
URL: https://github.com/apache/airflow/pull/69812
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on code in PR #69812:
URL: https://github.com/apache/airflow/pull/69812#discussion_r4111578007
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -111,8 +140,9 @@ class OpenAIResponseOperator(BaseOperator):
input items.
:param model: The OpenAI model to use.
:param response_kwargs: Additional keyword arguments to pass to the OpenAI ``create_response``
- method (for example ``instructions``, ``tools``, ``conversation`` or ``previous_response_id``).
- Templated, so values (e.g. ``previous_response_id``) may reference upstream XCom.
+ method, or ``parse_response`` when ``text_format`` is set (for example ``instructions``,
+ ``tools``, ``conversation`` or ``previous_response_id``). Templated, so values (e.g.
+ ``previous_response_id``) may reference upstream XCom.
Do not set ``background`` or ``stream`` here: ``background=True`` returns before the response
completes, so this operator logs a warning and the returned output text may be empty, while
Review Comment:
Added the clause here, and the same sentence to the `background` note in the guide so the two match.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038219991
Formatting is off -- can't see any images :)
<img width="811" height="759" alt="image" src="https://github.com/user-attachments/assets/2281c9f4-c09b-44da-be05-47fe6c4f7a52" />
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on code in PR #69812:
URL: https://github.com/apache/airflow/pull/69812#discussion_r4111577430
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -21,15 +21,43 @@
from functools import cached_property
from typing import TYPE_CHECKING, Any, ClassVar
+from pydantic import BaseModel, ValidationError
+
from airflow.providers.common.compat.sdk import BaseOperator, conf
from airflow.providers.openai.exceptions import OpenAIBatchJobException
from airflow.providers.openai.hooks.openai import OpenAIHook, validate_execute_complete_event
from airflow.providers.openai.triggers.openai import OpenAIBatchTrigger
if TYPE_CHECKING:
+ from openai.types.responses import Response
+
from airflow.providers.common.compat.sdk import Context
+def _get_structured_response_details(response: Any) -> str:
Review Comment:
Good point, it's typed as `ParsedResponse[BaseModel]` now. I tried the same typo on 2.37.0 and mypy flags `content.refusl` as `attr-defined`, where it passed silently with `Any`.
##########
providers/openai/src/airflow/providers/openai/hooks/openai.py:
##########
@@ -273,6 +279,30 @@ def create_response(self, input: Any, model: str = "gpt-4o-mini", **kwargs: Any)
"""
return self.conn.responses.create(model=model, input=input, **kwargs)
+ def parse_response(
+ self,
+ input: Any,
+ text_format: type[_TextFormatT],
+ model: str = "gpt-4o-mini",
+ **kwargs: Any,
Review Comment:
Makes sense, I applied your suggestion: `model` is second like in `create_response`, and `text_format` is keyword-only like the SDK. There's also a test that passing the model positionally works and `parse_response(prompt, Person)` raises.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5846864682
Thanks for the approval and the extra catches, @kaxil. All three are in, one commit each:
- `_get_structured_response_details` is typed as `ParsedResponse[BaseModel]` now, and mypy catches a misspelled SDK field (checked on 2.37.0)
- `parse_response` keeps `create_response`'s positional order, with `text_format` keyword-only like the SDK
- the docstring and the guide both say `background=True` fails a structured request and leaves the response running on OpenAI's side
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038205026
Done — ran the `text_format` path end-to-end against the real OpenAI Responses API. Thanks @kaxil for the nudge to verify live.
**Environment:** Airflow standalone (this branch, `feature/openai-structured-outputs`) with the branch `openai` provider installed, `LocalExecutor`, real OpenAI account (`api.openai.com`), default model `gpt-4o-mini`.
**Sample DAG** (`openai_structured_smoke`) — one `OpenAIResponseOperator` task with a Pydantic `text_format`:
```python
class Person(BaseModel):
name: str
age: int
OpenAIResponseOperator(
task_id="extract_person",
conn_id="openai_default",
model="gpt-4o-mini",
input_text="Extract the name and age from: 'Alice is 30 years old.'",
text_format=Person,
)
Result: the task succeeds and pushes the parsed structured output to XCom as a plain dict — {"name": "Alice", "age": 30} — no manual serialization, exactly the flow this PR adds. gpt-4o-mini (the default) handled structured outputs fine, matching your note about dropping the specific-model claim.
1. Status — Grid (success): extract_person → success, Operator OpenAIResponseOperator.
<img width="1420" height="473" alt="Screenshot 2026-07-22 004139" src="https://github.com/user-attachments/assets/1248f9fa-9253-48b0-8b8d-79aea9758ee4" />
2. Logs (UI): task log showing the connection retrieval and Generated response resp_… from the operator.
<img width="1431" alt="Task logs" src="https://github.com/user-attachments/assets/25928f3b-f62c-42d5-9d4e-85612c076a75" />
4. XCom: return_value = {"name": "Alice", "age": 30} — the structured dict.
<img width="1420" alt="XCom return_value" src="https://github.com/user-attachments/assets/e8402f3d-0caa-4aee-b13b-a7dbbfa288ad" />
5. Terminal (airflow dags test): explicit proof of the real call — POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" followed by SetXCom(... value={'name': 'Alice', 'age': 30} ...) and state=success.
<img width="1682" alt="Terminal 200 OK" src="https://github.com/user-attachments/assets/ca938af9-255b-4c9c-a2d7-d6b621181843" />
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038609211
Fixed — dragged the images in this time instead of pasting raw `<img>` tags (rookie move on my part 😅).
All four screenshots should render now: Grid status, task logs, XCom, and the terminal `200 OK` from `api.openai.com/v1/responses`.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038961598
You're right that the UI isn't from main. The environment is Airflow **3.0.3** (a released core wheel), with my PR branch's `openai` provider installed on top of it. My PR only touches the `openai` provider (which is versioned independently and targets Airflow 3.x), so the core being 3.0.3 doesn't affect the `text_format` path — but I understand why the older UI raised a flag.
Here's proof the running provider is this branch's code, not the released 1.8.0 (which has no `text_format`):
<img width="1245" height="427" alt="Screenshot 2026-07-22 022218" src="https://github.com/user-attachments/assets/59687789-42d3-45a4-824a-e6a15cf8d91b" />
So the feature itself genuinely ran on the branch code — the mismatch is only the core UI version, not the operator.
That said, I'll redo the run against a **from-source main build** (core installed editable from the branch, not the 3.0.3 wheel) so the UI matches main and there's no ambiguity. I'll re-capture the Grid/logs/XCom on main's UI and update this thread shortly.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038167519
Done — ran the `text_format` path end-to-end against the real OpenAI Responses API. Thanks @kaxil for the nudge to verify live.
**Environment:** Airflow standalone (this branch, `feature/openai-structured-outputs`) with the branch `openai` provider installed, `LocalExecutor`, real OpenAI account (`api.openai.com`), default model `gpt-4o-mini`.
**Sample DAG** (`openai_structured_smoke`) — one `OpenAIResponseOperator` task with a Pydantic `text_format`:
```python
class Person(BaseModel):
name: str
age: int
OpenAIResponseOperator(
task_id="extract_person",
conn_id="openai_default",
model="gpt-4o-mini",
input_text="Extract the name and age from: 'Alice is 30 years old.'",
text_format=Person,
)
Result: the task succeeds and pushes the parsed structured output to XCom as a plain dict — {"name": "Alice", "age": 30} — no manual serialization, exactly the flow this PR adds. gpt-4o-mini (the default) handled structured outputs fine, matching your note about dropping the specific-model claim.
1. Status — Grid (success): extract_person → success, Operator OpenAIResponseOperator.
<img width="1428" alt="Grid success" src="https://github.com/user-attachments/assets/9660edf0-959f-4dcf-bfdf-b5541428760f" />
2. Logs (UI): task log showing the connection retrieval and Generated response resp_… from the operator.
<img width="1431" alt="Task logs" src="https://github.com/user-attachments/assets/25928f3b-f62c-42d5-9d4e-85612c076a75" />
3. XCom: return_value = {"name": "Alice", "age": 30} — the structured dict.
<img width="1420" alt="XCom return_value" src="https://github.com/user-attachments/assets/e8402f3d-0caa-4aee-b13b-a7dbbfa288ad" />
4. Terminal (airflow dags test): explicit proof of the real call — POST https://api.openai.com/v1/responses "HTTP/1.1 200 OK" followed by SetXCom(... value={'name': 'Alice', 'age': 30} ...) and state=success.
<img width="1682" alt="Terminal 200 OK" src="https://github.com/user-attachments/assets/ca938af9-255b-4c9c-a2d7-d6b621181843" />
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-4982837896
Will do. This branch has been developed and unit-tested on a Windows workstation,
so producing a real-run screenshot means standing up an Airflow environment
(Codespaces or Docker Compose) — a few days of setup on my end. I've also got
end-term exams through the 19th, so realistically will update with screenshots around
July 20. Grid view / logs / XCom in one reply once the env is running. Will
bump this thread if I slip.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5039564087
The earlier run was on 3.0.3 core (was pre-installed in my Codespace) with the branch openai provider on top, which is why the UI looked older. That combo still exercised the change, but you're right that it wasn't the right proof.
I re-ran the smoke DAG on Airflow 3.4.0 from source (main) with the openai provider from this branch, in a clean AIRFLOW_HOME. Same DAG — extract Person(name: str, age: int) from "Alice is 30 years old." — hits the real OpenAI Responses API. Screenshots include:
1. Grid — extract_person finished green in ~6s, one try, OpenAIResponseOperator
2. Logs — Generated response resp_0eb37e37… from airflow.providers.openai.operators.openai.OpenAIResponseOperator, followed by the XCom push
3. XCom — return_value key present at the run's end timestamp
4. Terminal — same Generated response resp_… line pulled from the log file
Happy to add a second smoke case (validation-failure branch, or the dict-fallback in parse_response) if you want that covered too — just say the word.
<img width="1841" height="552" alt="Screenshot 2026-07-22 033030" src="https://github.com/user-attachments/assets/20918dea-4c4e-4a14-8705-7ecf4f48e3ca" />
<img width="745" height="107" alt="Screenshot 2026-07-22 033445" src="https://github.com/user-attachments/assets/11791647-5afc-4e91-b14f-b3ce06c6e922" />
<img width="1451" height="416" alt="Screenshot 2026-07-22 032803" src="https://github.com/user-attachments/assets/32f5641e-d39f-4c86-95e4-7a2943e52338" />
<img width="747" height="105" alt="image" src="https://github.com/user-attachments/assets/589c2c1e-fa14-4b77-950f-fa50c7273079" />
<img width="1471" height="527" alt="image" src="https://github.com/user-attachments/assets/f53e1525-37d2-44d2-87cc-4463070bb658" />
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-4967103475
Could you also run a sample Dag with the this, and show screenshot of Airflow UI to show status and logs please
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on code in PR #69812:
URL: https://github.com/apache/airflow/pull/69812#discussion_r4109163162
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -21,15 +21,43 @@
from functools import cached_property
from typing import TYPE_CHECKING, Any, ClassVar
+from pydantic import BaseModel, ValidationError
+
from airflow.providers.common.compat.sdk import BaseOperator, conf
from airflow.providers.openai.exceptions import OpenAIBatchJobException
from airflow.providers.openai.hooks.openai import OpenAIHook, validate_execute_complete_event
from airflow.providers.openai.triggers.openai import OpenAIBatchTrigger
if TYPE_CHECKING:
+ from openai.types.responses import Response
+
from airflow.providers.common.compat.sdk import Context
+def _get_structured_response_details(response: Any) -> str:
Review Comment:
`response: Any` turns off type checking for every attribute read below. With `ParsedResponse[BaseModel]` (it can go in the existing `TYPE_CHECKING` import), mypy narrows on `output.type == "message"` and `content.type == "refusal"` and checks each field against the SDK. I tried it on openai 2.37.0: a typo in `content.refusal` is an `attr-defined` error with the concrete type and passes silently with `Any`. The `model_construct` tests can't catch an SDK field rename, and this would.
##########
providers/openai/src/airflow/providers/openai/operators/openai.py:
##########
@@ -111,8 +140,9 @@ class OpenAIResponseOperator(BaseOperator):
input items.
:param model: The OpenAI model to use.
:param response_kwargs: Additional keyword arguments to pass to the OpenAI ``create_response``
- method (for example ``instructions``, ``tools``, ``conversation`` or ``previous_response_id``).
- Templated, so values (e.g. ``previous_response_id``) may reference upstream XCom.
+ method, or ``parse_response`` when ``text_format`` is set (for example ``instructions``,
+ ``tools``, ``conversation`` or ``previous_response_id``). Templated, so values (e.g.
+ ``previous_response_id``) may reference upstream XCom.
Do not set ``background`` or ``stream`` here: ``background=True`` returns before the response
completes, so this operator logs a warning and the returned output text may be empty, while
Review Comment:
With `text_format` set, `background=True` no longer just logs a warning. The parsed response comes back `queued` or `in_progress`, so the `status != "completed"` check in `execute()` raises `ValueError`, and the background response is left running on OpenAI's side. A clause here would match what the structured-outputs section of the rst guide already says about responses still in progress.
##########
providers/openai/src/airflow/providers/openai/hooks/openai.py:
##########
@@ -273,6 +279,30 @@ def create_response(self, input: Any, model: str = "gpt-4o-mini", **kwargs: Any)
"""
return self.conn.responses.create(model=model, input=input, **kwargs)
+ def parse_response(
+ self,
+ input: Any,
+ text_format: type[_TextFormatT],
+ model: str = "gpt-4o-mini",
+ **kwargs: Any,
Review Comment:
`text_format` sits where `create_response` has `model`, so `hook.parse_response(prompt, "gpt-4o")`, written by analogy with `create_response`, passes the model id as the format. Keeping `create_response`'s positional order and making `text_format` keyword-only also matches the SDK, where `responses.parse` takes it keyword-only. Easier to settle before the hook method ships than after.
```suggestion
input: Any,
model: str = "gpt-4o-mini",
*,
text_format: type[_TextFormatT],
**kwargs: Any,
```
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-4965462311
Thanks @kaxil — this is a genuinely great review. Every one of the five catches
would have been a real footgun for users:
- The `model_dump()` enum bug would only surface at XCom push, after the API call
had already been paid for — the exact kind of silent-until-production bug that
makes structured-output code frustrating to trust.
- The `ValidationError` gap was worse: I'd been reasoning about `output_parsed=None`
as "the" failure mode, but truncation raises inside `parse()` before assignment,
so the operator's whole failure-handling story never fired. That's the sort of
claim that needs to hold in the docs and didn't, before this review.
- The `previous_response_id` / model-requirement contradictions, the
`ParsedResponse[Any]` throwing away the SDK's own generic, the pydantic import
down inside a test function — each fair on its own, and together they add up
to code that reads like "someone shipped the first working version." Which is
exactly what happened. Appreciate the read.
All five landed in `beb3d9e`. Recap:
1. **`model_dump(mode="json")`** — matches what
`airflow.serialization.serde.serializers.pydantic` already does. New regression
test `test_openai_response_operator_structured_output_dumps_enum_as_json` uses
a plain `str`-mixin-free `enum.Enum` field and asserts the dumped value is
`"high"` (str), not a `Priority` instance. Reverting `mode="json"` fails it.
2. **`ValidationError` -> `ValueError`** — `parse_response` is now wrapped in
`try/except ValidationError`, re-raising as `ValueError` mentioning the
`text_format` class name. New test
`test_openai_response_operator_structured_output_validation_error_raises`
feeds a real `ValidationError` (built via `_StructuredPerson.model_validate({})`)
through `parse_response.side_effect` and asserts the conversion.
3. **Richer refusal message** — the `output_parsed=None` `ValueError` now
includes `status`, `error`, and `incomplete_details` from the API response.
The existing refusal test asserts all three snippets are present in the
message, so a regression back to "just say status" would fail.
4. **Docstring / example DAG / rst** — dropped the "requires
`gpt-4o-2024-08-06` or later" claim from the operator docstring, the hook
docstring, and the how-to guide. Dropped the `model="gpt-4o-2024-08-06"`
override + explanatory comment from the example DAG. Removed
`previous_response_id` from the "use hook directly" list in the docstring.
5. **Generic typing + hook-methods list** — `parse_response` is now
`_TextFormatT = TypeVar("_TextFormatT", bound="BaseModel")` returning
`ParsedResponse[_TextFormatT]`. Callers get `output_parsed` typed as
`_TextFormatT | None`, matching the SDK's own signature. Also added
`parse_response` to the Responses bullet in `docs/operators/openai.rst`.
6. **Test import** — `from pydantic import BaseModel` moved to the module top
of `tests/unit/openai/hooks/test_openai.py`.
Bonus: the failing `Build documentation (--spellcheck-only)` job was tripping
on "parseable" — rephrased across the docstring, the `ValueError` message, and
the how-to guide to standard English so spellcheck passes.
### Author review
I read every fix line-by-line before pushing and validated the two behavioral
claims that mattered most against the installed `openai` and `pydantic` packages:
that `Responses.parse` raises `ValidationError` inside its own body (so a wrap
at the operator's call site is what catches it), and that `model_dump()` defaults
to `mode="python"` while `mode="json"` renders `enum.Enum` fields as their JSON
values. Both matched what your review described, so the fixes hold on the same
grounds. The design calls the fixes preserve — one operator (not a new class),
`ValueError` (not a new exception class), `model_dump(mode="json")` (not a
custom serializer) — are still mine.
---
Drafted-by: GitHub Copilot (Claude Opus 4.6); reviewed by @YAshhh29 before posting
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "kaxil (via GitHub)" <gi...@apache.org>.
kaxil commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5038736237
@YAshhh29 The screenshot doesn't look from the `main`/ your branch, what version of Airflow is your environment?
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5839306256
Hi @kaxil, sorry this one went quiet for so long. I pushed fixes for your July 30 review the same day but never replied to the threads, which I should have. I've answered them now. I'm also trying to move and get more used to Linux, so I can run Breeze and the full checks locally before pushing.
Main changed this operator quite a bit in the meantime (#72049, #72150, #72151), so instead of just fixing the conflicts I wired structured outputs into those changes:
- `max_output_tokens` and `max_tool_calls` now work for structured requests too
- `response_id` and `usage` are pushed to XCom for structured responses as well, before the output is checked, so even a rejected response shows what it cost (same order as `OpenAIAgentSessionOperator`)
- `text_format` is keyword-only now
- the XCom push moved into a small shared helper, in its own commit with no behaviour change
I tested it on openai 2.37, 2.46 and 2.54, and checked that every safeguard has a test that fails if it's removed. Would you mind taking another look when you get a chance?
cc @Lee-W, since this builds on your recent changes to the operator.
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Re: [PR] Support Pydantic structured outputs in OpenAIResponseOperator [airflow]
Posted by "YAshhh29 (via GitHub)" <gi...@apache.org>.
YAshhh29 commented on PR #69812:
URL: https://github.com/apache/airflow/pull/69812#issuecomment-5921351332
Thanks @kaxil! Good catch on `multiple_outputs=True`. I added a line to the `text_format` docstring and the guide saying `response_id` and `usage` are reserved field names, since a field with either name would overwrite the XCom the operator pushes under that key. Pushed in 40bc1fe.
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