kaxil commented on code in PR #76503:
URL: https://github.com/apache/airflow/pull/76503#discussion_r4485370677
##########
providers/google/pyproject.toml:
##########
@@ -83,10 +83,20 @@ dependencies = [
# google-cloud-aiplatform doesn't install ray for python 3.12 (issue:
https://github.com/googleapis/python-aiplatform/issues/5252).
# Temporarily lock in ray 2.42.0 which is compatible with python 3.12
until linked issue is solved.
# Remove the ray dependency as well as google-cloud-bigquery-storage once
linked issue is fixed
- # Floor raised to 1.164.0: earlier "evaluation" extras cap litellm below
the version that
- # carries the fixes for CVE-2026-35030 and its follow-on advisories on
Python 3.14.
- # - https://github.com/googleapis/python-aiplatform/issues/7057
- "google-cloud-aiplatform[evaluation]>=1.164.0",
+ # The dependencies of google-cloud-aiplatform's "evaluation" extra, except
litellm, which
+ # vertexai.preview.evaluation (what the generative model hook uses) never
imports; only the
+ # vertexai._genai evals client does, behind an ImportError guard. The
extra caps litellm at a
+ # version that still requires openai<3, and below the fix for a critical
litellm advisory:
+ # https://github.com/googleapis/python-aiplatform/issues/7252
+ # The extra's pandas is listed further down. The floors of jsonschema,
pyyaml and tqdm match
+ # the other Airflow distributions; scikit-learn 1.7.2 is the first release
with Python 3.14
+ # wheels.
+ "google-cloud-aiplatform>=1.164.0",
Review Comment:
Added a note at the top of the changelog. The provider never calls
`Client().evals` itself, so it only hits users who call it from their own tasks
with a LiteLLM model.
##########
pyproject.toml:
##########
@@ -1467,24 +1467,18 @@ ci-image = [
# floor; `scripts/ci/prek/upgrade_important_versions.py` deliberately skips it.
required-version = ">=0.13.4"
no-build-isolation-package = ["sphinx-redoc"]
-# pydantic-ai-slim[openai]>=2.33 declares openai>=3 (>=3.8 from 2.39), while
-# llama-index-llms-openai (<=0.8.1) and litellm (<=1.102.0, pulled in by the
Google provider's
-# google-cloud-aiplatform[evaluation]) still cap openai below 3. Without this
override the
-# workspace lock cannot move pydantic-ai-slim past 2.31, which is incompatible
with anthropic
-# 1.x, so the Anthropic path could not run in the CI image at all. Holding
openai on 2.x is the
-# pairing constraints-main already ships and the one `pip check` in the image
build accepts
-# (pip ignores extra-gated requirements, but not llama-index's unconditional
cap). pydantic-ai's
-# OpenAI model works against openai 2.x at runtime; the declared floor tracks
the SDK's httpx2
-# move. Published wheels are unaffected: overrides only shape uv.lock and the
CI image. Drop this
-# once run-llama/llama_index#22939 and BerriAI/litellm#40317 land and
google-cloud-aiplatform
-# lifts its litellm cap (<1.86 below Python 3.14) far enough to reach the
fixed litellm.
-override-dependencies = ["openai>=2.47.0,<3"]
-# The override replaces every package's openai requirement, not only
pydantic-ai-slim's, so the
-# resolver no longer rejects a release that needs openai>=3 unconditionally,
and only `pip check`
-# in the image build catches it. Cap each such package here at its last openai
2.x release, which
-# is what the resolver would pick without the override. azure-ai-projects
2.5.0 moved to
-# openai>=3.0.0; the Azure provider needs >=2.2.0.
-constraint-dependencies = ["azure-ai-projects<2.5"]
+# Common AI's "llamaindex" extra cannot be installed alongside its "openai"
extra:
+# llama-index-llms-openai (up to 0.8.3) caps openai below 3, and
pydantic-ai-slim[openai]>=2.33
+# needs openai>=3. Declaring them as conflicting lets uv.lock resolve each one
on its own, so the
+# CI image gets openai 3 and the LlamaIndex tests run in the agent framework
job instead, with
+# the openai 2.x that llama-index pulls in. Remove this once
run-llama/llama_index#22939 is
+# released and the llama-index packages are back in Common AI's dev group.
Review Comment:
Done, the comment now lists all four.
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