Claude/multimodal ye linghua refactor 01 p tmtnewy3wnkopm qjd bjyv#16
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…c & OpenAI) Major Changes: 1. **Project Rename**: mini-agent -> ye_linghua (叶灵华) - Renamed package: mini_agent -> ye_linghua - Renamed CLI command: mini-agent -> ye-linghua - Updated all imports and references - Added YeLinghua alias for Agent class 2. **OpenAI Multimodal Support** - Enhanced OpenAI client with multimodal input support - Support text + image (URL or base64) messages - Updated documentation 3. **YAML-based Personality System** - Created personality.yaml for character configuration - Created prompts.yaml for prompt templates - Implemented PersonalityLoader for dynamic prompt generation - Backward compatible with legacy system_prompt.md 4. **Web Configuration Interface** - FastAPI-based web UI for editing personality and prompts - Real-time preview of generated system prompts - YAML validation and syntax highlighting - New CLI command: ye-linghua-config 5. **Default Personality: Ye Linghua (叶灵华)** - Programming-loving AI girl character - Friendly, enthusiastic, and professional - Customizable traits, interests, and behaviors - Chinese + English bilingual support 6. **Updated Configuration** - Added agent.use_personality config option - Added agent.personality_path and prompts_path - Updated config-example.yaml with new options - All config paths now use ye-linghua prefix 7. **Documentation** - Created comprehensive README_ZH.md - Updated all examples with new imports - Added Web UI usage guide - Migration guide from Mini-Agent Breaking Changes: - Package name changed: mini_agent -> ye_linghua - CLI command changed: mini-agent -> ye-linghua - Config directory changed: ~/.mini-agent -> ~/.ye-linghua Migration: - Update imports: from mini_agent -> from ye_linghua - Use YeLinghua or Agent (both work) - Legacy configs still supported for backward compatibility
…port Implement a modular plugin architecture supporting: - Python-based plugins with base classes (Plugin, ReplyHandler) - PowerShell/Shell script plugins with JSON communication - Timer plugin for scheduled reminders with tag-based interface (<set-timer>, <list-timers>, <remove-timer>) - Notification plugin with cross-platform desktop notifications (Windows/Linux/macOS) - PluginLoader with hot-reload capability - PluginRegistry for centralized management - ReplyHandler chain for processing LLM responses by priority - Context and prompt injection into LLM system prompts - Auto-discovery from plugins directory Configuration integration: - Added PluginsConfig to config system - Updated config-example.yaml with plugin settings - Support for enabling/disabling individual plugins Documentation: - Comprehensive PLUGINS.md with usage examples - Architecture explanations and best practices
Agent Integration: - Add plugin_registry, platform, user_id, session_id parameters to Agent.__init__() - Implement _build_plugin_context() to convert agent state to PluginContext - Implement _get_plugin_prompt_extensions() to inject plugin prompts into system prompt - Implement _process_reply_with_plugins() to process LLM responses through ReplyHandler chain - Update run() method to process responses with plugin handlers before display - Plugin prompt extensions are automatically injected into system prompt on initialization CLI Integration: - Initialize PluginRegistry and PluginLoader in run_agent() - Load built-in plugins (Timer & Notification) based on config - Auto-discover and load plugins from plugins directory - Pass plugin_registry to Agent constructor - Generate unique session_id for each session - Display active plugins count in session info - Properly shutdown all plugins on exit Features: - Plugin prompt extensions visible to LLM in system prompt - ReplyHandler chain processes responses by priority - Tag-based functionality (<set-timer>, <notify>) works automatically - Platform context (cli) passed to plugins - Session tracking with unique IDs
- Document automatic plugin integration in Agent - Explain system prompt injection and ReplyHandler chain - Provide CLI usage examples with plugin loading output - Add programmatic usage example for custom integrations - Show how plugins work transparently with Agent runtime
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