392bcfa2ef
Major enhancements to Clover CLI: ✨ New Features: - AI agent with multi-turn conversation capabilities - Tool calling system with 11+ tools for file operations, Git, linting, etc. - Step-by-step AI assistance with play-by-play commentary - Enhanced interactive mode with better UX 🔧 Core Components Added: - ai_agent.py: Main AI agent with conversation management - models/: API client and model management system - Comprehensive tool system for development tasks 🛠️ Tools Available: - File operations (create, read, update, delete) - Command execution with safety checks - Git operations (status, diff, commit, push) - Code linting and formatting - Project structure analysis - Security scanning and dependency management 💡 User Experience: - Real-time tool execution summaries - File creation with full path visibility - Error handling and retry mechanisms - Clean conversation flow until task completion 🧹 Repository Cleanup: - Added comprehensive .gitignore - Removed __pycache__ directories and build artifacts - Organized project structure The AI can now actually create files, run commands, and work through complex development tasks step-by-step with full transparency.
107 lines
3.0 KiB
Python
107 lines
3.0 KiB
Python
"""
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Model manager for Clover - A terminal assistant for AI-powered project management
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Handles switching between different language models and manages API connections
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"""
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import json
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import os
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from typing import Any, Dict, Optional
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from config.settings import load_config
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from models.api_client import APIClient
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class ModelManager:
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"""
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Manages different language models for Clover CLI tool
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"""
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def __init__(self):
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"""Initialize the model manager with configuration"""
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self.config = load_config()
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self.api_client = APIClient()
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def list_models(self) -> Dict[str, Any]:
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"""
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List available models on the server
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Returns:
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dict: Available models information
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"""
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try:
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result = self.api_client.list_models()
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if "error" in result:
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return {
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"error": result.get("error", "Failed to list models"),
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"models": [],
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}
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models_list = result.get("models", [])
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return {
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"models": models_list,
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"active_model": self.config.get("model", "qwen2.5-coder:7b"),
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"base_url": self.config.get("base_url", "http://192.168.8.223:11434"),
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}
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except Exception as e:
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return {"error": f"Failed to list models: {str(e)}", "models": []}
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def get_model(self, model_name: str = None) -> str:
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"""
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Get the active model name
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Args:
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model_name (str): Specific model name to use
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Returns:
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str: Model name to use
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"""
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if model_name:
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return model_name
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return self.config.get("model", "gpt-oss:20b")
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def set_model(self, model_name: str) -> bool:
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"""
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Set the active model for future operations
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Args:
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model_name (str): Name of the model to use
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Returns:
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bool: True if successful
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"""
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try:
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self.config["model"] = model_name
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# In a full implementation, we would save this to config file
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return True
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except Exception as e:
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print(f"Error setting model: {e}")
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return False
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def get_active_model_info(self) -> Dict[str, Any]:
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"""
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Get information about the currently active model
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Returns:
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dict: Information about active model
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"""
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return {
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"model": self.config.get("model", "gpt-oss:20b"),
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"base_url": self.config.get("base_url", "http://192.168.8.223:11434"),
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"timeout": self.config.get("timeout", 300),
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}
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def get_available_models(self) -> list:
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"""
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Get list of available models from the LLM server
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Returns:
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list: List of model names
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"""
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result = self.list_models()
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if "error" in result:
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return []
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models = result.get("models", [])
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return [model.get("name", "") for model in models]
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