Add MiniMax as first-class LLM provider

[octo-patch]

Summary

Add MiniMax as a first-class LLM provider in DataDreamer, extending the existing OpenAI class to leverage MiniMax's OpenAI-compatible API endpoint (https://api.minimax.io/v1).

Key Changes

• New MiniMax class (src/llms/minimax.py): Extends OpenAI with MiniMax-specific configurations
  • Temperature clamping to MiniMax's required (0.0, 1.0] range
  • Model-specific context lengths: 1M tokens for M2.7, 204K for M2.5
  • Model-specific max output lengths: 16K for M2.7, 8K for M2.5
  • MINIMAX_API_KEY environment variable auto-detection
• Updated src/llms/__init__.py: Registered MiniMax in exports and __all__
• 30 unit tests + 3 integration tests (tests/test_minimax.py): Module structure, constants, implementation logic, and live API tests
• In-project test class (src/tests/llms/test_llms.py): TestMiniMax class following existing test patterns

Supported Models

Model	Context Length	Max Output
MiniMax-M2.7	1,000,000	16,384
MiniMax-M2.7-highspeed	1,000,000	16,384
MiniMax-M2.5	204,800	8,192
MiniMax-M2.5-highspeed	204,800	8,192

Usage

from datadreamer.llms import MiniMax

# Using environment variable MINIMAX_API_KEY
llm = MiniMax("MiniMax-M2.7")

# Or with explicit API key
llm = MiniMax("MiniMax-M2.7", api_key="your-key")

results = llm.run(
    ["Generate a creative story about AI."],
    max_new_tokens=500,
    temperature=0.7,
)

Test Plan

• All 30 unit tests pass (module structure, constants, implementation logic, project test integration)
• All 3 integration tests pass against live MiniMax API (M2.7, M2.7-highspeed, temperature edge case)
• Verify compatibility with DataDreamer's caching and retry mechanisms
• Verify DataDreamer session workflow (with DataDreamer(...))

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