* feat: add MiniMax AI as LLM platform adaptor
Original implementation by octo-patch in PR #318.
This commit includes comprehensive improvements and documentation.
Code Improvements:
- Fix API key validation to properly check JWT format (eyJ prefix)
- Add specific exception handling for timeout and connection errors
- Remove unused variable in upload method
Dependencies:
- Add MiniMax to [all-llms] extra group in pyproject.toml
Tests:
- Remove duplicate setUp method in integration test class
- Add 4 new test methods:
* test_package_excludes_backup_files
* test_upload_success_mocked (with OpenAI mocking)
* test_upload_network_error
* test_upload_connection_error
* test_validate_api_key_jwt_format
- Update test_validate_api_key_valid to use JWT format keys
- Fix test assertions for error message matching
Documentation:
- Create comprehensive MINIMAX_INTEGRATION.md guide (380+ lines)
- Update MULTI_LLM_SUPPORT.md with MiniMax platform entry
- Update 01-installation.md extras table
- Update INTEGRATIONS.md AI platforms table
- Update AGENTS.md adaptor import pattern example
- Fix README.md platform count from 4 to 5
All tests pass (33 passed, 3 skipped)
Lint checks pass
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
* fix: improve MiniMax adaptor — typed exceptions, key validation, tests, docs
- Remove invalid "minimax" self-reference from all-llms dependency group
- Use typed OpenAI exceptions (APITimeoutError, APIConnectionError)
instead of string-matching on generic Exception
- Replace incorrect JWT assumption in validate_api_key with length check
- Use DEFAULT_API_ENDPOINT constant instead of hardcoded URLs (3 sites)
- Add Path() cast for output_path before .is_dir() call
- Add sys.modules mock to test_enhance_missing_library
- Add mocked test_enhance_success with backup/content verification
- Update test assertions for new exception types and key validation
- Add MiniMax to __init__.py docstrings (module, get_adaptor, list_platforms)
- Add MiniMax sections to MULTI_LLM_SUPPORT.md (install, format, API key,
workflow example, export-to-all)
Follows up on PR #318 by @octo-patch (feat: add MiniMax AI as LLM platform adaptor).
Co-Authored-By: Octopus <octo-patch@users.noreply.github.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Auto-detects NVIDIA (CUDA), AMD (ROCm), or CPU-only GPU and installs the
correct PyTorch variant + easyocr + all visual extraction dependencies.
Removes easyocr from video-full pip extras to avoid pulling ~2GB of wrong
CUDA packages on non-NVIDIA systems.
New files:
- video_setup.py (835 lines): GPU detection, PyTorch install, ROCm config,
venv checks, system dep validation, module selection, verification
- test_video_setup.py (60 tests): Full coverage of detection, install, verify
Updated docs: CHANGELOG, AGENTS.md, CLAUDE.md, README.md, CLI_REFERENCE,
FAQ, TROUBLESHOOTING, installation guide, video dependency plan
All 2523 tests passing (15 skipped).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
