Add MiniMax as a first-class LLM provider

[octo-patch]

Summary

Add MiniMax as a first-class LLM provider in Langfun, following the existing patterns used by DeepSeek and Groq providers.

Changes

• langfun/core/llms/minimax.py — New provider module:

  • MiniMaxModelInfo extending lf.ModelInfo with provider metadata and links
  • MiniMax base class extending OpenAIChatCompletionAPI with MiniMax-specific configuration
  • MiniMaxM27 and MiniMaxM27Highspeed convenience classes for MiniMax-M2.7 and MiniMax-M2.7-highspeed models
  • Temperature clamping (MiniMax API requires temperature ∈ (0.0, 1.0], so 0.0 is clamped to 0.01)
  • API key management via api_key parameter or MINIMAX_API_KEY environment variable
  • Model registration with lf.LanguageModel.register() for LanguageModel.get() support

• langfun/core/llms/minimax_test.py — 10 unit tests covering:

  • Model directory listing
  • API key validation and environment variable fallback
  • Model ID and resource ID verification
  • Model info metadata
  • Request formatting
  • Temperature clamping behavior
  • LanguageModel.get() registration

• langfun/core/llms/__init__.py — Added MiniMax exports

• README.md — Added MiniMax to the list of supported LLMs

Supported Models

Model	Context Window	Description
MiniMax-M2.7	1M tokens	Latest flagship model
MiniMax-M2.7-highspeed	1M tokens	Optimized for speed

Usage

import langfun as lf

# Using convenience class
lm = lf.llms.MiniMaxM27(api_key='YOUR_KEY')
# Or via environment variable MINIMAX_API_KEY
lm = lf.llms.MiniMaxM27()

r = lm('Who are you?')
print(r)

# Using LanguageModel.get()
lm = lf.LanguageModel.get('MiniMax-M2.7')

Test Results

All 10 unit tests pass. Existing provider tests (DeepSeek, Groq) continue to pass without regressions.

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4 files changed, 288 additions, 1 deletion

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