YAshhh29 commented on code in PR #71575:
URL: https://github.com/apache/airflow/pull/71575#discussion_r4150235864
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providers/common/ai/docs/durable_execution.rst:
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@@ -102,6 +102,33 @@ cache:
never replays responses that belong to a different conversation.
4. After successful completion, the cached steps are deleted.
+Fingerprints are computed from pydantic's JSON rendering of each value, the
same
+rendering a json-mode dump produces, so ordinary types that are not JSON -- a
+``datetime`` or ``Decimal`` tool argument, a dataclass in ``tool_choice``, the
+bytes in a ``BinaryContent``, a dict keyed by date -- fingerprint normally, and
+entries cached by an earlier version still match. The one adjustment is that
the
+members of a ``set`` are ordered before hashing, so a set matches on a later
+attempt too. If a value cannot be rendered at all, that step is not cached,
and on
+retry it runs live rather than replaying an unverified entry. A parameter
annotated ``Iterable[...]`` is one such case:
+pydantic validates it lazily, and reading it in order to hash it would consume
+the input the tool itself has not read yet, so the step runs live instead of
+being cached.
+
+On the model path this is rarely confined to a single step: the causes are
such a
+value in ``model_settings``, which is attached to every request, in the tool
+definitions the request carries, or in the message history, which every later
+request carries forward. Any one of them degrades all subsequent model steps
the
+same way, leaving durable execution with nothing to
+replay, so the retry re-runs the agent at full cost. The
Review Comment:
Reworded along those lines. It now says it degrades every model step from
that point on, so the retry re-runs from there. For settings and tool
definitions that point is normally the first request, though callable settings
or a tool's `prepare` can bring one in later.
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