Set up OpenAI API Python Dataclasses

[vmpuri]

(2/3) Split #875 into separate PRs.

Introduces basic Python dataclasses to represent requests, responses, and associated objects defined in the OpenAI API specification. This will enable creating a basic server that follows the spec so users can leverage TorchChat to run LLMs on their own hardware with a familiar interface to existing cloud tools.

Testing
Lintunner

lintrunner
Warning: Could not find a lintrunner config at: '.lintrunner.private.toml'. Continuing without using configuration file.


>>> Lint for generate.py:

  Advice (FLAKE8) C901
    'Generator.chat' is too complex (32)
    See https://www.flake8rules.com/rules/C901.html.

        581  |            buffer.clear()
        582  |        # print(, end='', flush=True)
        583  |
    >>> 584  |    def chat(
        585  |        self,
        586  |        generator_args: GeneratorArgs,
        587  |    ):

(advice from prior commit)

Dataclasses are used and tested further in PR #908

Stack from ghstack (oldest at bottom):

• Replace browser UI with Basic Streamlit UI Implementation #908
• -> Set up OpenAI API Python Dataclasses #907
• Refactor generate.py for modularity #906

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchchat/907

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✅ You can merge normally! (1 Unrelated Failure)

As of commit 1e1d04d with merge base ab85b2a ():

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• Run the README instructions - with stories - on MPS/MacOS / test-readme-mps-macos / macos-job (gh) (trunk failure)
  ##[error]The operation was canceled.

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