Gigachat 3 tool parser and tests

[ajpqs]

Purpose

This PR adds a tool parser for parsing the function calls made by the Gigachat 3 models.
Models can generate only one function call without any text using format:

function call{json}

Test Plan

This PR also includes tests. Existing tests have not been modified or will be impacted.

Test Result

pytest tests/entrypoints/openai/tool_parsers/test_gigachat3_tool_parser.py

Output:

========================================================================= test session starts =========================================================================
platform linux -- Python 3.12.3, pytest-9.0.1, pluggy-1.6.0
configfile: pyproject.toml
plugins: anyio-4.11.0
collected 9 items                                                                                                                                                     

tests/entrypoints/openai/tool_parsers/test_gigachat3_tool_parser.py .........                                                                                   [100%]

========================================================================== 9 passed in 8.28s ==========================================================================

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

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[mergify]

Documentation preview: https://vllm--29905.org.readthedocs.build/en/29905/

[gemini-code-assist]

Code Review

This pull request introduces a new tool parser for Gigachat 3 models, along with corresponding documentation and tests. The implementation for non-streaming tool calls is solid. However, the streaming implementation contains a critical bug in its JSON parsing logic that can lead to state corruption and cause streaming to fail for tool calls with complex arguments. I've provided a detailed comment on this issue. The rest of the changes, including the tests and documentation, look good.

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[aarnphm]

not very familiar with this model family, but seems cool, thanks for the contribution.