Two missing pieces in the minimax_chat dispatch chain:
1. minimax_backend.py did not define chat_optimizer or chat_optimizer_messages;
only chat_target / chat_target_messages existed. Any caller using
optimizer_backend='minimax_chat' would hit AttributeError or fall through
to _openai (Azure).
2. skillopt/model/__init__.py's chat_optimizer and chat_optimizer_messages
dispatchers checked claude_chat and qwen_chat but not minimax_chat,
so minimax_chat callers would silently fall through to _openai.chat_optimizer
(the Azure path), which fails with 'Azure OpenAI endpoint is not configured'
on any setup without AZURE_OPENAI_* env vars.
Adds chat_optimizer to minimax_backend.py (mirrors chat_target via
_chat_messages_impl) and minimax_chat branches to both
chat_optimizer / chat_optimizer_messages dispatchers.
Verified locally: 1-epoch training on a 4-item SearchQA-format dataset
went from '[skip] no usable patches — skill unchanged' (baseline fallback)
to a successful accept_new_best with success_patches=1 per step.
Co-authored-by: Mavis (MiniMax) <Mavis@MiniMax.local>
Co-authored-by: jc <jc@users.noreply.github.com>
Port qwen_backend.py pattern to minimax_backend.py as a new
OpenAI-compatible urllib-based backend. Includes:
- BASE_URL defaulting to https://api.minimax.chat/v1
- API_KEY, TIMEOUT_SECONDS, MAX_TOKENS, TEMPERATURE env vars
- ENABLE_THINKING support (MiniMax thinking mode)
- configure_minimax_chat() runtime configurator
- chat_target() and chat_target_messages() functions
- TokenTracker integration and get_token_summary()
- set_target_deployment() support
- Default model: MiniMax/MiniMax-Text-01
Jay C