- Add MiniMax-M3 to model list and set as default
- Keep MiniMax-M2.7 and MiniMax-M2.7-highspeed
- Remove older models (M2.5/M2.5-highspeed)
- Update related tests
- Add MiniMax-M2.7 and MiniMax-M2.7-highspeed to model list
- Set MiniMax-M2.7 as default model
- Keep all previous models as alternatives
- Update related tests
- Add MiniMax model detection and compatibility handling in LLMClient
- Handle response_format incompatibility: MiniMax does not support
response_format parameter, use prompt engineering for JSON output
- Add temperature clamping for MiniMax (must be > 0)
- Add robust JSON parsing from LLM responses (parse_json_from_response)
- Update simulation_config_generator and oasis_profile_generator for
MiniMax compatibility
- Add MiniMax configuration examples in .env.example
- Add MiniMax documentation in README.md and README-EN.md
- Add unit tests for MiniMax compatibility functions
Supported models: MiniMax-M2.5, MiniMax-M2.5-highspeed
API docs: https://platform.minimax.io/docs/api-reference/text-openai-api
- Deleted docker-compose.yml, backend Dockerfile, frontend Dockerfile, and nginx configuration to streamline project setup.
- Updated .env.example to reorganize LLM and ZEP API configurations for clarity and ease of use.
- Enhanced README.md to reflect changes in project structure and provide clearer setup instructions.
- Updated .env.example to reflect new LLM configuration with Aliyun's API.
- Enhanced .gitignore to include additional files and directories for better exclusion of sensitive and build artifacts.
- Added docker-compose.yml for streamlined deployment of backend and frontend services.
- Introduced Dockerfiles for both backend and frontend to facilitate containerized builds.
- Created README.md to provide comprehensive project documentation and setup instructions.
- Established nginx configuration for frontend to support API proxying and static file serving.
- Updated .env.example to include new keys for dual LLM configuration, allowing for both general and boost settings.
- Modified create_model function to support an optional use_boost parameter, enabling the selection of either general or boost LLM configurations based on availability.
- Improved logging to indicate which LLM configuration is being used during model creation, enhancing clarity for users.
octo-patch
Octopus
PR Bot
666ghj