#!/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/NPC_Dialogue_LoRA/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") # print("模型优化选项:") # print("1. 生成LoRA训练脚本") # print("2. 执行增量训练") # choice = input("请输入选择 (1-5): ").strip() # if choice == '1': # 生成LoRA训练脚本 print("\n=== 生成LoRA训练脚本 ===") script_content = generate_lora_training_script() script_path = "./scripts/iterative_lora_training.py" os.makedirs("./scripts", exist_ok=True) with open(script_path, 'w', encoding='utf-8') as f: f.write(script_content) print(f"✓ LoRA训练脚本已生成: {script_path}") print("使用方法:") print(" 1. 先运行训练数据生成 (选项8)") print(" 2. 修改脚本中的路径配置") print(f" 3. 运行: python {script_path}") # elif choice == '2': # # 执行增量训练 # print("\n=== 执行增量训练 ===") # # 检查训练数据 # training_dir = "./training_data" # if not os.path.exists(training_dir): # print("❌ 训练数据目录不存在，请先生成训练数据 (选项8)") # return # training_files = [f for f in os.listdir(training_dir) if f.endswith('.json')] # if not training_files: # print("❌ 未找到训练数据文件，请先生成训练数据 (选项8)") # return # print(f"找到训练数据文件:") # for i, file in enumerate(training_files, 1): # print(f" {i}. {file}") # file_idx = input(f"选择训练数据文件 (1-{len(training_files)}): ").strip() # try: # selected_file = training_files[int(file_idx) - 1] # training_file_path = os.path.join(training_dir, selected_file) # print(f"将使用训练文件: {selected_file}") # print("⚠ 注意：实际训练需要配置正确的模型路径和计算资源") # # 生成训练命令 # training_command = generate_training_command(training_file_path) # print(f"建议训练命令:") # print(f" {training_command}") # # 可选：执行训练（需要用户确认） # confirm = input("是否现在执行训练？(y/N): ").strip().lower() # if confirm == 'y': # print("开始增量训练...") # # 这里可以添加实际的训练执行逻辑 # print("⚠ 训练功能需要根据实际环境配置") # except (ValueError, IndexError): # print("❌ 无效的文件选择") # else: # print("❌ 无效选择") except Exception as e: print(f"✗ 模型优化失败: {e}") import traceback traceback.print_exc() def generate_lora_training_script(): """生成LoRA训练脚本""" return '''#!/usr/bin/env python # -*- coding: utf-8 -*- """ 基于评分数据的LoRA增量训练脚本 自动生成 - 请根据实际环境调整配置 """ import json import torch from transformers import AutoModelForCausalLM, AutoTokenizer from peft import LoraConfig, get_peft_model, TaskType import os class IterativeLoRATrainer: def __init__(self, base_model_path, training_data_path, output_path): self.base_model_path = base_model_path self.training_data_path = training_data_path self.output_path = output_path # LoRA配置 self.lora_config = LoraConfig( task_type=TaskType.CAUSAL_LM, inference_mode=False, r=16, # LoRA rank lora_alpha=32, lora_dropout=0.1, target_modules=["q_proj", "v_proj", "k_proj", "o_proj"] ) def load_training_data(self): """加载训练数据""" with open(self.training_data_path, 'r', encoding='utf-8') as f: data = json.load(f) return data['data'] def prepare_training_samples(self, data): """准备训练样本""" samples = [] for item in data: if item.get('label') == 'positive' or item.get('overall_score', 0) >= 8.0: # 高质量样本 sample = { 'input': f"角色: {item['speaker']}\\n请生成高质量对话:", 'output': item['content'], 'quality_score': item.get('overall_score', 8.0) } samples.append(sample) return samples def train(self): """执行训练""" print("开始LoRA增量训练...") # 加载模型和分词器 tokenizer = AutoTokenizer.from_pretrained(self.base_model_path) model = AutoModelForCausalLM.from_pretrained( self.base_model_path, torch_dtype=torch.bfloat16, device_map="auto" ) # 应用LoRA model = get_peft_model(model, self.lora_config) # 加载训练数据 training_data = self.load_training_data() training_samples = self.prepare_training_samples(training_data) print(f"训练样本数量: {len(training_samples)}") # 这里添加实际的训练循环 # 建议使用transformers的Trainer或自定义训练循环 # 保存模型 model.save_pretrained(self.output_path) tokenizer.save_pretrained(self.output_path) print(f"训练完成，模型保存到: {self.output_path}") if __name__ == '__main__': # 配置参数 - 请根据实际情况修改 BASE_MODEL_PATH = '/mnt/e/AI/Project02/AITrain/Qwen/Qwen3-4B' TRAINING_DATA_PATH = './training_data/high_quality_dialogues_latest.json' OUTPUT_PATH = './output/iterative_lora_v2' trainer = IterativeLoRATrainer(BASE_MODEL_PATH, TRAINING_DATA_PATH, OUTPUT_PATH) trainer.train() ''' 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 (0-11): ").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()