Files
odysseus/specs/model-providers/openai.md
RaresKeYandStressTestor 7026cf40b5 docs: bootstrap specs ground truth (#5794)
* docs(specs): restore bootstrap after dev rewrite

* docs(specs): remove runtime inventory snapshot

* docs(specs): reconcile current dev truth

* docs(specs): document scheduled task actions as an owner-attribution source

Owner Attribution covered cookie, bearer-token and internal-loopback
requests. Scheduled task actions are a fourth source and behave
differently: _execute_action passes owner=task.owner off the stored
ScheduledTask row, so no request and no resolved principal are in
flight, and route-level require_user() never runs.

Webhook triggers are the sharp case. They are unauthenticated by
design with the token as the only credential and execute under the
stored task.owner.

Paths cite routes/task/task_routes.py, the canonical location after
the task subpackage move (#6081); routes/task_routes.py on current dev
is the backward-compat shim.

* docs(specs): add chained tasks to the trigger list, refresh dev stamp

Review feedback from RaresKeY on the previous commit.

"Every trigger path" was too broad: success-chained tasks are another
path into _execute_action. Added them with their own citation, and
noted that chaining additionally requires the target task to share
task.owner and rejects cycles, which is stricter than the trigger-side
checks. Softened the lead-in to "these trigger paths".

Line 56 still pointed at routes/task_routes.py for webhook credential
validation. That path is the backward-compat shim on current dev after
the task subpackage move (#6081); repointed to the canonical
routes/task/task_routes.py.

Stamp moved to dev@2a6b09b. Inspection backing that bump was scoped:
every file path cited in this spec was mechanically checked to resolve
on 2a6b09b, and every file:line in the Owner Attribution additions was
read against it. Behavioral claims elsewhere in the file were not
re-audited.

* docs(specs): correct SECURE_COOKIES description to match current behavior

Third of the stale details RaresKeY enumerated. The cookie section
described SECURE_COOKIES as purely opt-in, which stopped being true.

_secure_cookie() (routes/auth_routes.py:89) treats an explicit true or
false as authoritative and derives the Secure attribute from the
request otherwise, including when the variable is unset and when
docker-compose injects it present-but-empty. Either the connection
scheme or the first X-Forwarded-Proto hop being https is enough.

* docs(specs): refresh current dev truth

---------

Co-authored-by: StressTestor <212606152+StressTestor@users.noreply.github.com>
2026-08-25 14:18:44 +02:00

1.6 KiB

OpenAI Provider Shape

Last updated: dev@e71f8ce | 2026-08-25

Scope

Canonical provider ID openai; API dialects OpenAI Chat Completions and Responses; catalog reader src/model_capability_readers/openai.py.

Catalog Shape

GET /v1/models returns object: list with data[] model cards containing id, object, created, and owned_by. This is identity and availability metadata only. It does not claim vision, tools, reasoning, modality, task, or context length. The record remains unknown and keeps the raw fields.

Request And Response Shape

Chat uses messages, tools[].function, tool_choice, and choices[].message|delta; Responses uses input, flattened tools, output items, and typed stream events. OpenAI may support a parameter at the platform level while individual models differ. A later model registry or probe must scope that fact before it becomes canonical model capability.

Fallback And Safety

An explicit endpoint kind selects this provider. Automatic reader detection accepts exact openai.com or a dot-delimited subdomain after normalizing case/trailing dots; it is a normalization hint rather than a trust boundary. Do not parse model IDs or ownership labels. If a proxy returns richer fields while explicitly configured as OpenAI, the reader preserves them as raw evidence but keeps capability unknown.

Current Gaps

  • OpenAI's Models API does not publish the per-model capability shape needed for automatic canonical classification.
  • Runtime model-specific sampling/reasoning behavior still needs a maintained structured registry or endpoint probes.