Project02/AITrain/main_controller.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
双AI角色对话系统主控制程序
完整的工作流程：PDF处理 -> 角色加载 -> RAG对话 -> 历史记录
'''

import os
import sys
import shutil
from typing import List, Dict
import json

def check_dependencies():
    """检查依赖库"""
    missing_deps = []
    
    try:
        import PyPDF2
    except ImportError:
        missing_deps.append("PyPDF2")
    
    try:
        import pymupdf
        print("✓ pymupdf 可用")
    except ImportError:
        print("⚠ pymupdf 不可用，将使用 PyPDF2")
    
    try:
        import sentence_transformers
        import faiss
        print("✓ 向量化功能可用")
    except ImportError:
        print("⚠ 向量化功能不可用，将使用文本匹配")
    
    if missing_deps:
        print(f"✗ 缺少依赖库: {', '.join(missing_deps)}")
        print("请运行: pip install PyPDF2 sentence-transformers faiss-cpu")
        return False
    
    return True

def setup_directories():
    """设置项目目录结构"""
    directories = [
        "./knowledge_base",
        "./characters", 
        "./worldview",
        "./rag_knowledge",
        "./conversation_data"
    ]
    
    for dir_path in directories:
        os.makedirs(dir_path, exist_ok=True)
        print(f"✓ 目录就绪: {dir_path}")

def copy_demo_files():
    """复制演示文档到知识库目录"""
    file_mappings = [
        ("./worldview/worldview_template_coc.json", "./knowledge_base/worldview_template_coc.json"),
        ("./characters/character_template_detective.json", "./knowledge_base/character_template_detective.json"),
        ("./characters/character_template_professor.json", "./knowledge_base/character_template_professor.json")
    ]
    
    for source, target in file_mappings:
        if os.path.exists(source):
            shutil.copy2(source, target)
            print(f"✓ 复制文档: {os.path.basename(target)}")

def process_pdf_workflow():
    """PDF处理工作流"""
    print("\n" + "="*60)
    print("PDF世界观文档处理")
    print("="*60)
    
    from pdf_to_rag_processor import PDFToRAGProcessor
    
    pdf_path = input("请输入PDF文件路径 (例: ./coc.pdf): ").strip()
    
    if not os.path.exists(pdf_path):
        print(f"✗ 文件不存在: {pdf_path}")
        return False
    
    try:
        processor = PDFToRAGProcessor()
        result = processor.process_pdf_to_rag(pdf_path, "./rag_knowledge")
        
        print(f"\n✓ PDF处理完成!")
        print(f"  - 文档块数: {result['chunks_count']}")
        print(f"  - 概念数: {result['concepts_count']}")
        print(f"  - 向量索引: {'启用' if result['vector_enabled'] else '未启用'}")
        
        return True
        
    except Exception as e:
        print(f"✗ PDF处理失败: {e}")
        return False

def show_character_info():
    """显示角色信息"""
    print("\n" + "="*60)
    print("角色设定信息")
    print("="*60)
    
    knowledge_dir = "./knowledge_base"
    character_files = [f for f in os.listdir(knowledge_dir) if f.startswith('character') and f.endswith('.json')]
    
    for char_file in character_files:
        try:
            with open(os.path.join(knowledge_dir, char_file), 'r', encoding='utf-8') as f:
                char_data = json.load(f)
            
            name = char_data.get('character_name', '未知')
            occupation = char_data.get('basic_info', {}).get('occupation', '未知')
            traits = char_data.get('personality', {}).get('core_traits', [])
            
            print(f"\n角色: {name}")
            print(f"  职业: {occupation}")
            print(f"  特点: {', '.join(traits[:3])}")
            
        except Exception as e:
            print(f"✗ 读取角色文件失败: {char_file} - {e}")

def run_dialogue_system(enableScore: bool, useManualScoring: bool = False):
    """运行双AI对话系统"""
    print("\n" + "="*60)
    print("启动双AI角色对话系统")
    print("="*60)
    
    try:
       
        # 直接启动双模型对话
        print("\n正在初始化双模型对话系统...")
        
        from dual_ai_dialogue_system import RAGKnowledgeBase, ConversationManager, DualAIDialogueEngine
        from npc_dialogue_generator import DualModelDialogueGenerator
        
        # 初始化组件
        kb = RAGKnowledgeBase("./knowledge_base")
        conv_mgr = ConversationManager("./conversation_data/conversations.db")
        
        # 检查模型路径
        base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-4B'
        lora_model_path = './output/iterative_lora_simple/final_model'
        
        if not os.path.exists(base_model_path):
            print(f"✗ 基础模型路径不存在: {base_model_path}")
            print("请修改 main_controller.py 中的模型路径")
            return
        
        if not os.path.exists(lora_model_path):
            lora_model_path = None
            print("⚠ LoRA模型不存在，使用基础模型")
        
        # 检查角色数据
        if not hasattr(kb, 'character_data') or len(kb.character_data) < 2:
            print("✗ 角色数据不足，无法创建双模型对话系统")
            print("请确保knowledge_base目录中有至少两个角色文件")
            return
        
        # 获取前两个角色
        character_names = list(kb.character_data.keys())[:2]
        char1_name = character_names[0]
        char2_name = character_names[1]
        
        print(f"✓ 使用角色: {char1_name} 和 {char2_name}")
        
        # 配置两个角色的模型
        character1_config = {
            "name": char1_name,
            "lora_path": lora_model_path,
            "character_data": kb.character_data[char1_name]
        }
        
        character2_config = {
            "name": char2_name,
            "lora_path": lora_model_path,
            "character_data": kb.character_data[char2_name]
        }
        
        # 创建双模型对话生成器
        print("正在初始化双模型对话生成器...")
        dual_generator = DualModelDialogueGenerator(
            base_model_path, 
            character1_config, 
            character2_config
        )
        
        # 创建对话引擎（启用评分功能）
        dialogue_engine = DualAIDialogueEngine(
            kb, 
            conv_mgr, 
            dual_generator, 
            enable_scoring=enableScore, 
            base_model_path=base_model_path,
            use_manual_scoring=useManualScoring
        )
        
        # 创建对话会话
        characters = [char1_name, char2_name]
        worldview = kb.worldview_data.get('worldview_name', '未知世界观') if kb.worldview_data else '未知世界观'
        
        session_id = conv_mgr.create_session(characters, worldview)
        print(f"✓ 创建对话会话: {session_id}")
        
        # 交互式对话循环
        print(f"\n=== 双AI模型对话系统 ===")
        print(f"角色: {char1_name} vs {char2_name}")
        print(f"世界观: {worldview}")
        print("输入 'quit' 退出对话")
        print("-" * 50)
        
        while True:
            try:
                # 获取用户输入
                user_input = input("\n请输入对话主题或指令: ").strip()
                
                if user_input.lower() == 'quit':
                    print("退出双AI对话系统")
                    break
                
                if not user_input:
                    print("请输入有效的对话主题")
                    continue
                
                # 询问对话轮数
                turns_input = input("请输入对话轮数 (默认4): ").strip()
                turns = int(turns_input) if turns_input.isdigit() else 4
                
                # 询问历史上下文设置
                history_input = input("使用历史对话轮数 (默认2): ").strip()
                history_count = int(history_input) if history_input.isdigit() else 2
                
                context_input = input("使用上下文信息数量 (默认10): ").strip()
                context_info_count = int(context_input) if context_input.isdigit() else 10
                
                print(f"\n开始对话 - 主题: {user_input}")
                print(f"轮数: {turns}, 历史: {history_count}, 上下文: {context_info_count}")
                print("-" * 50)
                
                # 运行双模型对话
                dialogue_engine.run_dual_model_conversation(
                    session_id, user_input, turns, history_count, context_info_count
                )
                
                print("-" * 50)
                print("对话完成！")
                
            except KeyboardInterrupt:
                print("\n\n用户中断对话")
                break
            except Exception as e:
                print(f"对话过程中出现错误: {e}")
                import traceback
                traceback.print_exc()
        
    
            
    except Exception as e:
        print(f"✗ 对话系统启动失败: {e}")
        import traceback
        traceback.print_exc()



def generate_training_dataset():
    """生成训练数据集"""
    print("\n" + "="*60)
    print("生成训练数据集")
    print("="*60)
    
    try:
        from dual_ai_dialogue_system import ConversationManager
        import sqlite3
        import json
        import os
        from datetime import datetime
        
        conv_mgr = ConversationManager("./conversation_data/conversations.db")
        
        # 创建输出目录
        output_dir = "./training_data"
        os.makedirs(output_dir, exist_ok=True)
        
        print("请选择训练数据生成类型:（默认1）")
        print("1. 高质量对话数据集 (分数≥8.0)")
        print("2. 问题对话改进数据集 (分数<6.0)")
        print("3. 角色一致性训练集")
        print("4. 创意性增强训练集")
        print("5. 完整对话质量数据集")
        
        choice = input("请输入选择 (1-5): ").strip()
        choice = int(choice) if choice.isdigit() else 1
        with sqlite3.connect(conv_mgr.db_path) as conn:
            training_data = []
            
            if choice == 1:
                # 高质量对话数据集
                print("\n生成高质量对话数据集...")
                cursor = conn.execute("""
                    SELECT speaker, content, score_details, score_feedback
                    FROM dialogue_turns 
                    WHERE dialogue_score >= 8.0
                    ORDER BY dialogue_score DESC
                    LIMIT 200
                """)
                
                for speaker, content, score_details, feedback in cursor.fetchall():
                    training_data.append({
                        "type": "high_quality",
                        "speaker": speaker,
                        "content": content,
                        "scores": json.loads(score_details) if score_details else {},
                        "feedback": feedback,
                        "label": "positive"
                    })
                
                output_file = f"{output_dir}/high_quality_dialogues_{datetime.now().strftime('%Y%m%d_%H%M')}.json"
                
            elif choice == 2:
                # 问题对话改进数据集
                print("\n生成问题对话改进数据集...")
                cursor = conn.execute("""
                    SELECT speaker, content, score_details, score_feedback
                    FROM dialogue_turns 
                    WHERE dialogue_score < 6.0 AND dialogue_score > 0
                    ORDER BY dialogue_score ASC
                    LIMIT 100
                """)
                
                for speaker, content, score_details, feedback in cursor.fetchall():
                    # 为每个低分对话生成改进指导
                    improvement_prompt = generate_improvement_prompt(content, feedback)
                    
                    training_data.append({
                        "type": "improvement",
                        "speaker": speaker,
                        "original_content": content,
                        "scores": json.loads(score_details) if score_details else {},
                        "problems": feedback,
                        "improvement_prompt": improvement_prompt,
                        "label": "needs_improvement"
                    })
                
                output_file = f"{output_dir}/improvement_dialogues_{datetime.now().strftime('%Y%m%d_%H%M')}.json"
                
            elif choice == 3:
                # 角色一致性训练集
                print("\n生成角色一致性训练集...")
                cursor = conn.execute("""
                    SELECT speaker, content, score_details
                    FROM dialogue_turns 
                    WHERE dialogue_score > 0
                    ORDER BY json_extract(score_details, '$.character_consistency') DESC
                    LIMIT 150
                """)
                
                for speaker, content, score_details in cursor.fetchall():
                    scores = json.loads(score_details) if score_details else {}
                    char_consistency = scores.get('character_consistency', 0)
                    
                    training_data.append({
                        "type": "character_consistency",
                        "speaker": speaker,
                        "content": content,
                        "character_consistency_score": char_consistency,
                        "label": "high_consistency" if char_consistency >= 8.0 else "medium_consistency"
                    })
                
                output_file = f"{output_dir}/character_consistency_{datetime.now().strftime('%Y%m%d_%H%M')}.json"
                
            elif choice == 4:
                # 创意性增强训练集
                print("\n生成创意性增强训练集...")
                cursor = conn.execute("""
                    SELECT speaker, content, score_details
                    FROM dialogue_turns 
                    WHERE dialogue_score > 0
                    ORDER BY json_extract(score_details, '$.creativity') DESC
                    LIMIT 150
                """)
                
                for speaker, content, score_details in cursor.fetchall():
                    scores = json.loads(score_details) if score_details else {}
                    creativity = scores.get('creativity', 0)
                    
                    training_data.append({
                        "type": "creativity",
                        "speaker": speaker,
                        "content": content,
                        "creativity_score": creativity,
                        "label": "high_creativity" if creativity >= 8.0 else "medium_creativity"
                    })
                
                output_file = f"{output_dir}/creativity_enhancement_{datetime.now().strftime('%Y%m%d_%H%M')}.json"
                
            elif choice == 5:
                # 完整对话质量数据集
                print("\n生成完整对话质量数据集...")
                cursor = conn.execute("""
                    SELECT speaker, content, dialogue_score, score_details, score_feedback
                    FROM dialogue_turns 
                    WHERE dialogue_score > 0
                    ORDER BY timestamp DESC
                    LIMIT 300
                """)
                
                for speaker, content, score, score_details, feedback in cursor.fetchall():
                    training_data.append({
                        "type": "complete_dataset",
                        "speaker": speaker,
                        "content": content,
                        "overall_score": score,
                        "dimension_scores": json.loads(score_details) if score_details else {},
                        "feedback": feedback,
                        "quality_label": get_quality_label(score)
                    })
                
                output_file = f"{output_dir}/complete_quality_dataset_{datetime.now().strftime('%Y%m%d_%H%M')}.json"
            
            else:
                print("❌ 无效选择")
                return
            
            if training_data:
                # 保存训练数据
                with open(output_file, 'w', encoding='utf-8') as f:
                    json.dump({
                        "metadata": {
                            "created_at": datetime.now().isoformat(),
                            "total_samples": len(training_data),
                            "data_type": choice,
                            "source": "dialogue_scoring_system"
                        },
                        "data": training_data
                    }, f, ensure_ascii=False, indent=2)
                
                print(f"\n✓ 训练数据集生成成功！")
                print(f"  - 文件路径: {output_file}")
                print(f"  - 数据条数: {len(training_data)}")
                print(f"  - 数据类型: {get_dataset_description(choice)}")
                
                # 生成数据集统计信息
                generate_dataset_statistics(training_data, choice)
                
            else:
                print("❌ 未找到符合条件的数据")
                
    except Exception as e:
        print(f"✗ 训练数据集生成失败: {e}")
        import traceback
        traceback.print_exc()

def generate_improvement_prompt(content, feedback):
    """生成改进提示"""
    return f"""原对话: {content}

问题分析: {feedback}

改进要求:
1. 保持角色特征和设定
2. 增强对话的逻辑性和连贯性
3. 提升表达的自然度
4. 增加信息密度，避免冗余
5. 适当增加创意元素

请生成一个改进版本的对话。"""

def get_quality_label(score):
    """根据分数获取质量标签"""
    if score >= 9.0:
        return "excellent"
    elif score >= 8.0:
        return "good"
    elif score >= 7.0:
        return "average"
    elif score >= 6.0:
        return "below_average"
    else:
        return "poor"

def get_dataset_description(choice):
    """获取数据集描述"""
    descriptions = {
        '1': "高质量对话数据集",
        '2': "问题对话改进数据集",
        '3': "角色一致性训练集",
        '4': "创意性增强训练集",
        '5': "完整对话质量数据集"
    }
    return descriptions.get(choice, "未知类型")

def generate_dataset_statistics(training_data, data_type):
    """生成数据集统计信息"""
    print(f"\n数据集统计信息:")
    
    if data_type == '1':  # 高质量数据集
        speakers = {}
        for item in training_data:
            speaker = item['speaker']
            speakers[speaker] = speakers.get(speaker, 0) + 1
        
        print(f"  - 角色分布:")
        for speaker, count in speakers.items():
            print(f"    • {speaker}: {count}条对话")
    
    elif data_type == '5':  # 完整数据集
        quality_dist = {}
        for item in training_data:
            label = item['quality_label']
            quality_dist[label] = quality_dist.get(label, 0) + 1
        
        print(f"  - 质量分布:")
        for label, count in quality_dist.items():
            print(f"    • {label}: {count}条对话")

def run_model_optimization():
    """运行模型迭代优化"""
    print("\n" + "="*60)
    print("模型迭代优化")
    print("="*60)
    
    try:
        from dual_ai_dialogue_system import ConversationManager
        import sqlite3
        import json
        import os
        from datetime import datetime
        
        conv_mgr = ConversationManager("./conversation_data/conversations.db")
        
        # 生成LoRA训练脚本
        print("\n=== 使用LoRA训练脚本 ===")
        import iterative_lora_training
        iterative_lora_training.main()
      
            
    except Exception as e:
        print(f"✗ 模型优化失败: {e}")
        import traceback
        traceback.print_exc()



def generate_training_command(training_file_path):
    """生成训练命令"""
    return f"python ./scripts/iterative_lora_training.py --data {training_file_path} --output ./output/optimized_model_v{datetime.now().strftime('%Y%m%d')}"

def show_scoring_statistics():
    """显示对话评分统计"""
    print("\n" + "="*60)
    print("对话评分统计")
    print("="*60)
    
    try:
        from dual_ai_dialogue_system import ConversationManager
        import json
        
        conv_mgr = ConversationManager("./conversation_data/conversations.db")
        
        # 查询评分数据
        import sqlite3
        with sqlite3.connect(conv_mgr.db_path) as conn:
            # 总体统计
            cursor = conn.execute("""
                SELECT 
                    COUNT(*) as total_turns,
                    AVG(dialogue_score) as avg_score,
                    MAX(dialogue_score) as max_score,
                    MIN(dialogue_score) as min_score
                FROM dialogue_turns 
                WHERE dialogue_score > 0
            """)
            
            stats = cursor.fetchone()
            if stats and stats[0] > 0:
                total_turns, avg_score, max_score, min_score = stats
                print(f"总体统计:")
                print(f"  - 已评分对话轮数: {total_turns}")
                print(f"  - 平均分数: {avg_score:.2f}")
                print(f"  - 最高分数: {max_score:.2f}")
                print(f"  - 最低分数: {min_score:.2f}")
            else:
                print("暂无评分数据")
                return
            
            # 按角色统计
            print(f"\n按角色统计:")
            cursor = conn.execute("""
                SELECT 
                    speaker,
                    COUNT(*) as turns,
                    AVG(dialogue_score) as avg_score,
                    MAX(dialogue_score) as max_score
                FROM dialogue_turns 
                WHERE dialogue_score > 0
                GROUP BY speaker
                ORDER BY avg_score DESC
            """)
            
            for row in cursor.fetchall():
                speaker, turns, avg_score, max_score = row
                print(f"  - {speaker}: 平均{avg_score:.2f}分 (最高{max_score:.2f}分, {turns}轮对话)")
            
            # 最近高分对话
            print(f"\n最近高分对话 (分数≥8.0):")
            cursor = conn.execute("""
                SELECT speaker, content, dialogue_score, score_feedback, timestamp
                FROM dialogue_turns 
                WHERE dialogue_score >= 8.0
                ORDER BY timestamp DESC
                LIMIT 5
            """)
            
            high_score_turns = cursor.fetchall()
            if high_score_turns:
                for speaker, content, score, feedback, timestamp in high_score_turns:
                    print(f"  [{timestamp[:16]}] {speaker} ({score:.2f}分)")
                    print(f"    内容: {content[:80]}...")
                    if feedback:
                        print(f"    评价: {feedback[:60]}...")
                    print()
            else:
                print("  暂无高分对话")
                
            # 分数分布统计
            print(f"\n分数分布:")
            cursor = conn.execute("""
                SELECT 
                    CASE 
                        WHEN dialogue_score >= 9.0 THEN '优秀 (9-10分)'
                        WHEN dialogue_score >= 8.0 THEN '良好 (8-9分)'
                        WHEN dialogue_score >= 7.0 THEN '中等 (7-8分)'
                        WHEN dialogue_score >= 6.0 THEN '及格 (6-7分)'
                        ELSE '待改进 (<6分)'
                    END as score_range,
                    COUNT(*) as count
                FROM dialogue_turns 
                WHERE dialogue_score > 0
                GROUP BY score_range
                ORDER BY MIN(dialogue_score) DESC
            """)
            
            for score_range, count in cursor.fetchall():
                percentage = (count / total_turns) * 100
                print(f"  - {score_range}: {count}轮 ({percentage:.1f}%)")
        
    except Exception as e:
        print(f"✗ 评分统计查询失败: {e}")

def show_system_status():
    """显示系统状态"""
    print("\n" + "="*60)
    print("系统状态检查")
    print("="*60)
    
    # 检查文件
    files_to_check = [
        ("./knowledge_base/worldview_template_coc.json", "世界观模板"),
        ("./knowledge_base/character_template_detective.json", "侦探角色"),
        ("./knowledge_base/character_template_professor.json", "教授角色"),
        ("./pdf_to_rag_processor.py", "PDF处理器"),
        ("./dual_ai_dialogue_system.py", "对话系统"),
        ("./npc_dialogue_generator.py", "NPC生成器")
    ]
    
    print("\n文件检查:")
    for file_path, description in files_to_check:
        if os.path.exists(file_path):
            print(f"✓ {description}: {file_path}")
        else:
            print(f"✗ {description}: {file_path} (不存在)")
    
    # 检查目录
    print("\n目录检查:")
    directories = ["./knowledge_base", "./rag_knowledge", "./conversation_data"]
    for dir_path in directories:
        if os.path.exists(dir_path):
            file_count = len([f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f))])
            print(f"✓ {dir_path}: {file_count} 个文件")
        else:
            print(f"✗ {dir_path}: 不存在")
    
    # 检查对话会话
    try:
        from dual_ai_dialogue_system import ConversationManager
        conv_mgr = ConversationManager("./conversation_data/conversations.db")
        sessions = conv_mgr.list_sessions()
        print(f"\n✓ 对话会话: {len(sessions)} 个")
    except Exception as e:
        print(f"\n✗ 对话会话检查失败: {e}")

def main():
    """主控制程序"""
    print("="*70)
    print("         双AI角色对话系统 - 主控制程序")
    print("         基于RAG的世界观增强对话引擎")
    print("="*70)
    
    # 检查依赖
    if not check_dependencies():
        return
    
    # 设置目录
    # setup_directories()
    # copy_demo_files()
    
    while True:
        print("\n" + "="*50)
        print("主菜单 - 请选择操作:")
        print("1. 处理PDF世界观文档 (转换为RAG格式)")
        print("2. 查看角色设定信息")
        print("3. 启动双AI对话系统 (开启AI打分)")
        print("4. 启动双AI对话系统 (关闭AI打分)")
        print("5. 启动双AI对话系统 (开启人工打分)")
        print("6. 系统状态检查")
        print("7. 查看对话评分统计")
        print("8. 生成训练数据集")
        print("9. 模型迭代优化")
        print("0. 退出")
        print("="*50)
        
        choice = input("请输入选择 (0-9): ").strip()
        
        if choice == '0':
            print("\n感谢使用双AI角色对话系统！")
            break
            
        elif choice == '1':
            process_pdf_workflow()
            
        elif choice == '2':
            show_character_info()
            
        elif choice == '3':
            run_dialogue_system(enableScore = True)
            
        elif choice == '4':
            run_dialogue_system(enableScore = False)
            
        elif choice == '5':
            run_dialogue_system(enableScore = True, useManualScoring = True)
            
        elif choice == '6':
            show_system_status()
            
        elif choice == '7':
            show_scoring_statistics()
            
        elif choice == '8':
            generate_training_dataset()
            
        elif choice == '9':
            run_model_optimization()
            
        elif choice == '11':
            show_usage_guide()
            
        else:
            print("❌ 无效选择，请重新输入")

def show_usage_guide():
    """显示使用说明"""
    print("\n" + "="*60)
    print("系统使用说明")
    print("="*60)
    
    guide = """
🚀 快速开始:
1. 首次使用建议先运行"创建演示对话场景"
2. 如有PDF世界观文档，选择"处理PDF世界观文档"
3. 通过"启动双AI对话系统"开始角色对话

📁 文档格式说明:
- 世界观文档: worldview_template_coc.json (参考COC设定)
- 角色设定: character_template_*.json (包含详细人设)

🔧 系统功能:
- PDF自动转换为RAG知识库
- 基于向量相似度的上下文检索
- 持久化对话历史存储
- 角色设定一致性保持

📝 自定义角色:
1. 参考 character_template_*.json 格式
2. 保存到 knowledge_base/ 目录
3. 重启对话系统加载新角色

💾 对话数据:
- 历史对话保存在 conversation_data/ 目录
- 支持会话恢复和历史查看
- 自动记录使用的上下文信息

⚠️  注意事项:
- 确保模型路径正确设置
- 首次运行需要下载向量化模型
- PDF处理需要足够内存
"""
    print(guide)

if __name__ == '__main__':
    main()