chore(deps): bump the all-dependencies group with 3 updates

[dependabot]

Bumps the all-dependencies group with 3 updates: google-genai, openai and sentence-transformers.

Updates google-genai from 1.54.0 to 1.55.0

Release notes

Sourced from google-genai's releases.

v1.55.0

1.55.0 (2025-12-11)

Features

• Add the Interactions API (836a3)
• Add enableEnhancedCivicAnswers feature in GenerateContentConfig (15d1ea9)
• Add IMAGE_RECITATION and IMAGE_OTHER enum values to FinishReason (8bb4b9a)
• Add voice activity detection signal. (feae46d)

Bug Fixes

• Replicated voice config bytes handling (c9f8668)

Documentation

• Regenerate docs for 1.54.0 (8bac8d2)

Changelog

Sourced from google-genai's changelog.

1.55.0 (2025-12-11)

Features

• Add the Interactions API (836a3)
• Add enableEnhancedCivicAnswers feature in GenerateContentConfig (15d1ea9)
• Add IMAGE_RECITATION and IMAGE_OTHER enum values to FinishReason (8bb4b9a)
• Add voice activity detection signal. (feae46d)

Bug Fixes

• Replicated voice config bytes handling (c9f8668)

Documentation

• Regenerate docs for 1.54.0 (8bac8d2)

Commits

• 5946b40 chore(main): release 1.55.0 (#1830)
• 86b81df No public description
• 836a33c No public description
• c9f8668 fix: replicated voice config bytes handling
• 8bb4b9a feat: Add IMAGE_RECITATION and IMAGE_OTHER enum values to FinishReason
• feae46d feat: Add voice activity detection signal.
• 15d1ea9 feat: Add enableEnhancedCivicAnswers feature in GenerateContentConfig
• 8bac8d2 docs: Regenerate docs for 1.54.0
• b27e716 Copybara import of the project:
• 7d78320 chore:internal
• See full diff in compare view

Updates openai from 2.9.0 to 2.11.0

Release notes

Sourced from openai's releases.

v2.11.0

2.11.0 (2025-12-11)

Full Changelog: v2.10.0...v2.11.0

Features

• api: gpt 5.2 (dd9b8e8)

v2.10.0

2.10.0 (2025-12-10)

Full Changelog: v2.9.0...v2.10.0

Features

• api: make model required for the responses/compact endpoint (a12936b)

Bug Fixes

• types: allow pyright to infer TypedDict types within SequenceNotStr (8f0d230)

Chores

• add missing docstrings (f20a9a1)
• internal: update docstring (9a993f2)

Changelog

Sourced from openai's changelog.

2.11.0 (2025-12-11)

Full Changelog: v2.10.0...v2.11.0

Features

• api: gpt 5.2 (dd9b8e8)

2.10.0 (2025-12-10)

Full Changelog: v2.9.0...v2.10.0

Features

• api: make model required for the responses/compact endpoint (a12936b)

Bug Fixes

• types: allow pyright to infer TypedDict types within SequenceNotStr (8f0d230)

Chores

• add missing docstrings (f20a9a1)
• internal: update docstring (9a993f2)

Commits

• 43e324e release: 2.11.0
• 752a293 feat(api): gpt 5.2
• ef00216 release: 2.10.0 (#2767)
• See full diff in compare view

Updates sentence-transformers from 5.1.2 to 5.2.0

Release notes

Sourced from sentence-transformers's releases.

v5.2.0 - CrossEncoder multi-processing, multilingual NanoBEIR evaluators, similarity score in mine_hard_negatives, Transformers v5 support

This minor release introduces multi-processing for CrossEncoder (rerankers), multilingual NanoBEIR evaluators, similarity score outputs in mine_hard_negatives, Transformers v5 support, Python 3.9 deprecations, and more.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.2.0
Inference only, use one of:
pip install sentence-transformers==5.2.0
pip install sentence-transformers[onnx-gpu]==5.2.0
pip install sentence-transformers[onnx]==5.2.0
pip install sentence-transformers[openvino]==5.2.0

CrossEncoder Multi-processing

The CrossEncoder class now supports multiprocessing for faster inference on CPU and multi-GPU setups. This brings CrossEncoder functionality in line with the existing multiprocessing capabilities of SentenceTransformer models, allowing you to use multiple CPU cores or GPUs to speed up both the predict and rank methods when processing large batches of sentence pairs.

The implementation introduces these new methods, mirroring the SentenceTransformer approach:

• start_multi_process_pool() - Initialize a pool of worker processes
• stop_multi_process_pool() - Clean up the worker pool

Usage is straightforward with the new pool parameter:

from sentence_transformers.cross_encoder import CrossEncoder
def main():
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')
# Start a pool of workers
pool = model.start_multi_process_pool()
Use the pool for faster inference
scores = model.predict(sentence_pairs, pool=pool)
rankings = model.rank(query, documents, pool=pool)
Clean up when done
model.stop_multi_process_pool(pool)

if name == "main":
main()

Or simply pass a list of devices to device to have predict and rank automatically create a pool behind the scenes.

from sentence_transformers.cross_encoder import CrossEncoder
</tr></table>

... (truncated)

Commits

• d4d70de Release v5.2.0
• b16248c docs: add release notes summary for v5.2 on main page (#3592)
• 2571f24 docs: update NanoBEIR collection links and descriptions for evaluators (#3591)
• 32cb5de Mine hard negatives: optionally output similarity scores (#3506)
• 9f9ad08 Extend NanoBEIR evaluators to support custom NanoBEIR datasets (#3583)
• 21dfa69 feat/deps: make Pillow an optional dependency (#3589)
• 67ff0fe Skip test_train_stsb tests; triggers rate limit too often (#3590)
• 4cb5cdc [deps] Use optimum-onnx now that both optimum-onnx and optimum-intel can us...
• 3f80d1c [feat] add multiprocessing support for Cross Encoder (#3580)
• f9724ff [feat] Allow transformers v5.0, add CI for transformers <v5 and >=v5 (#3586)
• Additional commits viewable in compare view

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