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chore(deps): update all non-major dependencies #1588
chore(deps): update all non-major dependencies #1588
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Hi team,
I'm currently reviewing this pull request and will post my full review in a few minutes. In the meantime, here's a quick summary of the changes for you and other reviewers:
This pull request, submitted by renovate-bot, updates all non-major dependencies across the generative-ai
repository. The goal is to keep the project up-to-date with the latest security patches and bug fixes, improving stability and reducing potential vulnerabilities.
Here's a breakdown of the changes:
- Dependency Updates: A large number of dependencies have been updated. The table below summarizes the key changes:
Package | Old Version | New Version | Type | Notes |
---|---|---|---|---|
@angular-devkit/build-angular |
17.3.9 | 17.3.11 | devDependencies | Patch update |
@angular/cli |
17.3.9 | 17.3.11 | devDependencies | Patch update |
@angular/cli |
18.2.4 | 18.2.12 | dependencies | Patch update |
@emotion/cache |
11.13.1 | 11.14.0 | dependencies | Minor update |
@emotion/react |
11.13.3 | 11.14.0 | dependencies | Minor update |
@emotion/styled |
11.13.0 | 11.14.0 | dependencies | Minor update |
...and many more... | ... | ... | ... | ... |
-
package-lock.json
Changes: Thepackage-lock.json
file in theconversation/chat-app
directory has been updated to reflect the new dependency versions. This includes changes to the@esbuild
packages and the addition of theclsx
package. The versions of@esbuild
packages have been updated to0.24.2
across the board.svelte
has been updated to5.16.0
andsvelte-check
to4.1.1
. -
Python Requirements Updates: Several Python requirements files have been updated to newer versions, including
Flask
,SQLAlchemy
,google-cloud-aiplatform
, and others. These updates are mostly minor or patch releases. -
pyproject.toml
Changes: Thepyproject.toml
file in thegemini/sample-apps/e2e-gen-ai-app-starter-pack
directory has been updated to reflect the newer versions of several Python packages, includingfastapi
,pydantic
, anduvicorn
. -
Dockerfile Changes: Some Dockerfiles have been updated to use newer base images (e.g.,
python:3.13-slim
instead ofpython:3.12-slim
).
Please review the detailed changes in each file. I'll provide more specific comments in my full review.
And now, a little haiku to lighten the mood:
Dependencies flow,
Updates bring fresh, clean new code,
Bugs flee in the night.
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This pull request updates numerous non-major dependencies across several projects. Regularly updating dependencies is crucial for incorporating bug fixes, performance enhancements, and new features. Overall, the updates seem appropriate, but I have a few minor suggestions and recommendations for consideration.
I've taken into account the implicit style guide observed within the provided codebase and referred to common style guides such as PEP 8 for Python.
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.11.1/styles/default.min.css" | ||
/> | ||
<script | ||
src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/highlight.min.js" | ||
integrity="sha512-6yoqbrcLAHDWAdQmiRlHG4+m0g/CT/V9AGyxabG8j7Jk8j3r3K6due7oqpiRMZqcYe9WM2gPcaNNxnl2ux+3tA==" | ||
src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.11.1/highlight.min.js" | ||
integrity="sha512-EBLzUL8XLl+va/zAsmXwS7Z2B1F9HUHkZwyS/VKwh3S7T/U0nF4BaU29EP/ZSf6zgiIxYAnKLu6bJ8dqpmX5uw==" |
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The highlight.js resources are being loaded over HTTP, while the main page uses HTTPS. This mixed content can trigger browser warnings and pose a potential security vulnerability, as it opens a pathway for malicious code injection. Consider serving highlight.js locally or using a CDN that supports HTTPS. Alternatively, you could update the URLs to use HTTPS if the resource is available over HTTPS.
fastapi = "0.110.3" | ||
pydantic = "2.9.2" | ||
uvicorn = {extras = ["standard"], version = "^0.30.5"} | ||
python = ">=3.13,<3.14" |
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The Python version specifier (>=3.13,<3.14
) restricts the project to a very specific Python version. While this ensures compatibility with the currently used dependencies, it might limit flexibility in the future. Unless there's a strong reason for this tight constraint, consider broadening the range to allow for minor version updates (e.g., ^3.13
or >=3.13,<3.15
).
python = ">=3.13,<3.14" | |
python = "^3.13" |
langchain-google-vertexai = "^2.0.7" | ||
langchain = "^0.3.0" | ||
langgraph = "^0.2.21" | ||
langchain-core = "^0.3.9" | ||
langchain-google-community = {extras = ["vertexaisearch"], version = "^2.0.2"} | ||
traceloop-sdk = "^0.33.12" | ||
traceloop-sdk = "^0.34.0" | ||
opentelemetry-exporter-gcp-trace = "^1.6.0" | ||
opentelemetry-sdk = "^1.25.0" | ||
google-cloud-logging = "^3.10.0" | ||
google-cloud-aiplatform = {extras = ["evaluation"], version = "^1.73.0"} |
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The version specifier for google-cloud-aiplatform
(^1.73.0
) is quite broad. Narrowing it down to a specific version or a smaller range (e.g., ==1.73.0
or >=1.73.0,<1.74.0
) provides better control over updates and helps prevent unexpected issues from potentially breaking changes in future releases. This aligns with best practices for dependency management.
google-cloud-aiplatform = {extras = ["evaluation"], version = "^1.73.0"} | |
google-cloud-aiplatform = {extras = ["evaluation"], version = "==1.73.0"} |
@@ -273,17 +273,17 @@ endpoint = ["requests (>=2.28.1)"] | |||
evaluation = ["pandas (>=1.0.0)", "tqdm (>=4.23.0)"] | |||
full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"] | |||
langchain = ["langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)"] | |||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"] | |||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)", "pytest-xdist"] |
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The pydantic
dependency in the langchain-testing
group is specified as >=2.6.3,<3
, which is a very broad range. This could lead to inconsistencies between development and production environments or unexpected behavior if a major pydantic
update introduces breaking changes. Consider narrowing the version range to a more specific version or a smaller compatible range (e.g., ==2.6.3
or >=2.6.3,<2.7
).
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)", "pytest-xdist"] | |
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (==2.6.3)", "pytest-xdist"] |
ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "ray[train]", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"] | ||
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"] | ||
ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "ray[train]", "scikit-learn (<1.6.0)", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"] | ||
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)"] |
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The pydantic
dependency in the reasoningengine
group is specified with a broad version range (>=2.6.3,<3
). This can introduce instability as new major versions of pydantic
might have breaking changes. It's generally recommended to pin dependencies to a specific version or a smaller, well-tested range. Consider using pydantic==2.6.3
or a similar more restrictive specifier.
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)"] | |
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (==2.6.3)"] |
google-cloud-logging = "^3.11.0" | ||
anthropic = {extras = ["vertex"], version = "^0.34.0"} | ||
anthropic = {extras = ["vertex"], version = "^0.42.0"} | ||
weave = "^0.51.0" | ||
black = "^24.8.0" |
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The version specifier for black
is too broad (^24.8.0
). Pinning it to a specific version, such as black==24.8.0
, is recommended for better reproducibility and to avoid potential issues with future releases that might introduce breaking changes to formatting. This practice ensures consistency across different environments and builds.
black = "^24.8.0" | |
black = "==24.8.0" |
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Hi team,
I'm currently reviewing this pull request and will post my full review in a few minutes. In the meantime, here's a quick summary for you and other reviewers to get up to speed:
This pull request, submitted by renovate-bot, updates various dependencies across the generative-ai
repository. The goal is to keep our project's dependencies current and benefit from bug fixes and improvements in the updated versions. The changes include updates to both dependencies
and devDependencies
across multiple packages. Specific changes are detailed below:
Summary of Changes:
- Dependency Updates: Numerous packages have been updated, including
@angular/cli
,@emotion/styled
,@genkit-ai/ai
,@google-cloud/storage
,@mui/material
,@radix-ui/react-accordion
,Flask
,Werkzeug
, and many more. The table below summarizes the changes:
Package | Previous Version | New Version | Type | Change Type |
---|---|---|---|---|
@angular/cli |
17.3.9 , 18.2.4 |
17.3.11 , 18.2.12 |
dependencies , devDependencies |
patch, minor |
@emotion/styled |
11.13.0 |
11.14.0 |
dependencies |
minor |
@genkit-ai/ai |
^0.5.13 , ^0.5.0 |
^0.9.0 |
dependencies |
minor |
@google-cloud/storage |
7.12.1 |
7.14.0 |
dependencies |
minor |
@mui/material |
6.1.0 |
6.3.0 |
dependencies |
minor |
@radix-ui/react-accordion |
1.2.1 |
1.2.2 |
dependencies |
patch |
Flask |
==3.0.3 , ==3.0.0 |
==3.1.0 |
minor | |
Werkzeug |
==3.0.6 |
==3.1.3 |
minor | |
...and many more... | ... | ... | ... | ... |
-
package-lock.json
Updates: Thepackage-lock.json
file in theconversation/chat-app
directory has been extensively updated to reflect the new dependency versions and their transitive dependencies. Most notably,@esbuild
packages have been updated from0.24.0
to0.24.2
, andflowbite-svelte
has been updated from0.46.0
to0.47.0
. Additionally,clsx
andesrap
have been added to the dependencies.svelte
andsvelte-check
have also been updated to newer versions. -
requirements.txt
Updates: Severalrequirements.txt
files across different directories have been updated to reflect the newer versions of Python packages. -
Dockerfile
Updates: TheDockerfile
ingemini/sample-apps/e2e-gen-ai-app-starter-pack
has been updated to usepython:3.13-slim
instead ofpython:3.11-slim
. TheDockerfile
ingemini/sample-apps/finance-advisor-spanner
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
ingemini/sample-apps/photo-discovery/ag-web/app
has been updated to usepython:3.13-slim
instead ofpython:3.12-slim
. TheDockerfile
ingemini/sample-apps/llamaindex-rag/ui
has been updated to usepython:3.13-slim
instead ofpython:3.12-slim
. TheDockerfile
ingemini/sample-apps/llamaindex-rag
has been updated to usepython:3.13-slim
instead ofpython:3.12-slim
. TheDockerfile
ingemini/sample-apps/gemini-mesop-cloudrun
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
ingemini/sample-apps/gemini-streamlit-cloudrun
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
inlanguage/sample-apps/chat-flask-cloudrun
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
inlanguage/sample-apps/chat-gradio
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
inlanguage/sample-apps/chat-streamlit
has been updated to usepython:3.13
instead ofpython:3.12
. TheDockerfile
ingemini/sample-apps/llamadeploy-on-cloud-run
has been updated to usepython:3.13
instead ofpython:3.9
. TheDockerfile
ingemini/sample-apps/swot-agent
has been updated to usepython:3.13-slim
instead ofpython:3.10-slim
. -
Minor changes in
pom.xml
: The Spring Boot starter parent version has been updated from3.3.4
to3.4.1
, and the Google Cloud libraries BOM version has been updated from26.47.0
to26.52.0
. The log4j version has been updated from2.24.1
to2.24.3
. -
package.json
updates: Severalpackage.json
files have been updated to reflect the newer versions of npm packages.
Please let me know if you have any questions.
Small updates, big impact,
Code flows like a river's grant,
New features will bloom.
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This pull request updates a number of dependencies across various projects. It's generated by Renovate Bot, so I'll assume the versions were chosen automatically based on some criteria (e.g., latest non-breaking versions). While automated dependency updates are helpful, it's essential to review the changelog for each updated package to understand the changes introduced and assess potential impacts on your projects.
Given the scope of changes across multiple languages and project types, thorough testing is highly recommended after merging. Pay particular attention to the projects where minor or major version bumps occurred, as these have a higher likelihood of introducing breaking changes, even if Renovate Bot deems them non-major. Consider adding integration tests that exercise the interactions with these updated libraries to catch any unexpected behavior early.
Here's a summary of the style guide elements I referenced during this review:
- Python: PEP 8
- JavaScript/TypeScript: No specific style guide provided, so I defaulted to common practices and consistency with the existing code.
- Java: No specific style guide provided, so I defaulted to common practices and consistency with the existing code.
- HTML: No specific style guide provided, so I defaulted to common practices and consistency with the existing code.
- General: Encouraged clear commit messages and PR descriptions.
<script | ||
src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/highlight.min.js" | ||
integrity="sha512-6yoqbrcLAHDWAdQmiRlHG4+m0g/CT/V9AGyxabG8j7Jk8j3r3K6due7oqpiRMZqcYe9WM2gPcaNNxnl2ux+3tA==" | ||
src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.11.1/highlight.min.js" | ||
integrity="sha512-EBLzUL8XLl+va/zAsmXwS7Z2B1F9HUHkZwyS/VKwh3S7T/U0nF4BaU29EP/ZSf6zgiIxYAnKLu6bJ8dqpmX5uw==" |
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It's a good security practice to include the integrity
attribute with a subresource integrity (SRI) hash for the highlight.js script. This ensures that the browser fetches only the intended version of the script, protecting against potential CDN compromises or man-in-the-middle attacks. You've already done this for the stylesheet, which is excellent! Adding crossorigin="anonymous"
is also recommended when using SRI.
@@ -273,17 +273,17 @@ endpoint = ["requests (>=2.28.1)"] | |||
evaluation = ["pandas (>=1.0.0)", "tqdm (>=4.23.0)"] | |||
full = ["docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"] | |||
langchain = ["langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)"] | |||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"] | |||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)", "pytest-xdist"] |
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The testing
extras group lists scikit-learn
twice, once without a version specifier and once with <1.6.0
. This redundancy can lead to confusion and potential conflicts. Unless both versions are intentionally required for testing (which is unusual), remove the duplicate entry. PEP 8 recommends keeping dependency specifications concise and unambiguous.
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.4)", "langchain-core (<0.4)", "langchain-google-vertexai (<3)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<2.10)", "pytest-xdist"] | |
testing = ["aiohttp", "bigframes", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.114.0)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.16.0)", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<2.10.dev0 || >=2.33.dev0,<=2.33.0)", "ray[default] (>=2.5,<=2.33.0)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn (<1.6.0)", "sentencepiece (>=0.2.0)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<2.18.0)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"] |
fastapi = "0.110.3" | ||
pydantic = "2.9.2" | ||
uvicorn = {extras = ["standard"], version = "^0.30.5"} | ||
python = ">=3.13,<3.14" |
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The Python version constraint >=3.10,<3.13
might be too restrictive, especially since the base Docker image is now python:3.13-slim
. Consider broadening the range to include 3.13, or at least ensure the specified range is compatible with your testing and deployment environments. Refer to PEP 8 for guidance on version specifiers.
python = ">=3.13,<3.14" | |
python = ">=3.10,<3.14" |
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Co-authored-by: code-review-assist[bot] <182814678+code-review-assist[bot]@users.noreply.github.com>
This PR contains the following updates:
17.3.9
->17.3.11
17.3.9
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18.2.4
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11.13.1
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7.12.1
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1.9.0
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1.7.0
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0.6.11
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6.1.0
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6.0.0-beta.9
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6.1.0
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2.13.5
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1.26.0
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1.2.1
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1.1.0
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1.3.0
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2.1.0
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1.1.0
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2.1.1
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1.2.0
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1.1.0
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1.1.0
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1.1.0
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1.1.2
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2.8.0
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3.0.6
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2.9.0
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6.5.0
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16.0.1
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1.0.84
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3.58.0
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2.6.11
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8.11.8
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18.3.5
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18.3.7
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18.3.0
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8.5.0
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8.5.0
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3.10.11
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1.3.1
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5.4.1
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4.6.0
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24.2.0
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