import logging import random import time from typing import Dict, List from hello_agents import SimpleAgent, HelloAgentsLLM, Message from config import get_config from game_logic import GameSession logger = logging.getLogger("game.agent") # 人物候选池，用于随机注入 system prompt，强制 LLM 从不同方向发散 _FIGURE_DOMAINS = [ "中国古代帝王（如汉武帝、唐太宗、武则天、康熙等）", "中国古代文人墨客（如李白、杜甫、苏轼、王羲之等）", "中国古代军事家（如岳飞、霍去病、戚继光、韩信等）", "中国神话人物（如女娲、嫦娥、哪吒、二郎神等）", "西游记人物（如孙悟空、猪八戒、唐僧、沙僧等）", "三国人物（如诸葛亮、曹操、刘备、关羽、周瑜等）", "西方历史人物（如拿破仑、凯撒、亚历山大、牛顿等）", "西方神话人物（如宙斯、雅典娜、赫拉克勒斯、阿喀琉斯等）", "世界科学家（如爱因斯坦、居里夫人、达芬奇、伽利略等）", "知名虚构角色（如哈利·波特、福尔摩斯、哆啦A梦、白雪公主等）", "现代体育明星（如姚明、李娜、迈克尔·乔丹、贝利等）", "中国近现代人物（如鲁迅、梁启超、郑成功、林则徐等）", "网络红人与UP主（如李子柒、papi酱、散打哥、罗翔等知名网络人物）", ] def _build_random_figure_prompt() -> str: """Dynamically build a system prompt with a random domain and seed to avoid LLM caching.""" domain = random.choice(_FIGURE_DOMAINS) seed = random.randint(10000, 99999) return f"""你是一个随机知名人物生成器。随机种子：{seed} 本次请从【{domain}】这个方向随机选择一个人物。 要求： 1. 必须是大众熟知、有足够信息可供猜测的人物 2. 必须是人物（真实或虚构），不要选建筑、动植物、自然景观等物体 3. 输出格式严格如下（两行，不要多余内容）： 名称：<人物名称> 简介：<一句话概括其性格特点与主要成就，50字以内> 4. 每次必须随机选择，不要总是选同一个""" _HINT_SYSTEM_PROMPT = """你是一位博学的助手。 根据提供的搜索资料，生成3条适合猜谜游戏的提示。 要求： 1. 每条提示单独一行，格式为：提示N：<内容> 2. 提示由模糊到具体，第1条最模糊，第3条最具体 3. 不能直接说出答案的名称 4. 只输出3行提示，不要其他内容""" _SEMANTIC_MATCH_PROMPT = """你是一位知识渊博的助手。请判断以下两个名称是否指代同一个人物或事物。 只需回答 "是" 或 "否"，不要输出任何其他内容。 名称A：{guess} 名称B：{actual}""" _ROLEPLAY_SYSTEM_PROMPT = """你正在参与一个猜谜游戏，扮演一个神秘人物（代号：【谜底】）。 ## 人物背景（仅供你参考，不可直接透露）： {bio} ## 对话规则： 1. 以该人物的第一人称身份回答，语气、措辞符合其性格特点与所处时代/背景 2. 用户会通过提问来猜测你的身份，**必须直接针对用户的问题给出明确回应**（如"是的"/"不是"/"确实如此"等），不能回避或答非所问 3. 在给出明确回应的基础上，可以用符合人物身份的语气补充一句，增加趣味性 4. 每次回答要简短（1-2句话），不要长篇大论 5. 回答内容要基于该人物真实的生平、性格、成就，不要编造 6. **严禁在任何情况下说出该人物的名称**（包括姓名、字号、封号、外号等一切称谓） 7. 如果用户的问题与该人物完全无关，可以用符合人物身份的方式婉转说明""" class HistoricalFigureAgent: """GuessWhoAmI game Agent wrapper""" def __init__(self, game_session: GameSession): """ Initialize Agent: use LLM to randomly generate a subject (person/object/landmark etc.) with brief intro, then use TavilySearchTool to pre-generate 3 hints, finally create role-play Agent. Args: game_session: game session object to store current subject info """ self.game_session = game_session config = get_config() logger.info(f"[AGENT] Initializing LLM: model={config.LLM_MODEL_ID} base_url={config.LLM_BASE_URL}") self._llm = HelloAgentsLLM( model=config.LLM_MODEL_ID, api_key=config.LLM_API_KEY, base_url=config.LLM_BASE_URL, timeout=config.LLM_TIMEOUT, provider="modelscope" ) self._config = config # Register search tool self._search_tool = None if config.TAVILY_API_KEY: from tools.tavily_search_tool import TavilySearchTool self._search_tool = TavilySearchTool(api_key=config.TAVILY_API_KEY) logger.info("[AGENT] TavilySearchTool registered") else: logger.warning("[AGENT] TAVILY_API_KEY not set, search tool disabled") # Register Wikipedia image tool (no API key required) from tools.search_image_tool import SearchImageTool self._image_tool = SearchImageTool() logger.info("[AGENT] SearchImageTool (Wikipedia) registered") # Step 1: LLM generates subject name + brief intro figure = self._generate_figure() self.game_session.current_figure = figure logger.info(f"[AGENT] Subject loaded: {figure}") # Step 2: pre-generate 3 hints via tavily search hints = self._generate_hints(figure["name"]) self.game_session.hints = hints logger.info(f"[AGENT] Hints pre-generated: {hints}") # Step 3: create role-play Agent self.agent = self._create_roleplay_agent() # ── Subject generation ──────────────────────────────────────────────────── def _generate_figure(self) -> Dict[str, str]: """Use LLM to randomly generate a subject (person/object/landmark) with brief intro.""" try: system_prompt = _build_random_figure_prompt() ts = int(time.time() * 1000) messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": f"请随机给我一个（时间戳：{ts}，随机数：{random.randint(1, 9999)}）"}, ] raw = self._llm.invoke(messages).strip() logger.info(f"[AGENT] LLM generated subject raw: {raw!r}") return self._parse_figure(raw) except Exception as e: logger.error(f"[AGENT] Failed to generate subject via LLM: {e}", exc_info=True) return self._fallback_figure() def _parse_figure(self, raw: str) -> Dict[str, str]: """Parse LLM output into {name, bio} dict.""" name = "" bio = "" for line in raw.splitlines(): line = line.strip() if line.startswith("名称：") or line.startswith("名称:") or line.startswith("姓名：") or line.startswith("姓名:"): name = line.split("：", 1)[-1].split(":", 1)[-1].strip() elif line.startswith("简介：") or line.startswith("简介:"): bio = line.split("：", 1)[-1].split(":", 1)[-1].strip() if not name: logger.warning("[AGENT] Failed to parse subject name, using fallback") return self._fallback_figure() return {"name": name, "bio": bio} def _fallback_figure(self) -> Dict[str, str]: """Return a minimal fallback person when LLM fails.""" persons = [ ("孔子", "春秋时期思想家、教育家，儒家学派创始人，性格温和而坚定，一生致力于礼乐仁义"), ("孙悟空", "《西游记》中的神话英雄，天性顽皮好斗、嫉恶如仇，七十二变，大闹天宫"), ("武则天", "中国历史上唯一的女皇帝，铁腕治国，心思缜密，开创武周政权"), ("诸葛亮", "三国时期蜀汉丞相，足智多谋、鞠躬尽瘁，以隆中对和空城计闻名"), ("哈利·波特", "《哈利·波特》系列中的魔法师主角，勇敢善良，最终击败伏地魔"), ] name, bio = random.choice(persons) return {"name": name, "bio": bio} # ── Hint generation ─────────────────────────────────────────────────────── def _generate_hints(self, name: str) -> List[str]: """Use TavilySearchTool to search subject info, then LLM generates 3 hints.""" if not self._search_tool: return self._fallback_hints(name) try: search_results = self._search_tool.run( {"query": f"{name} 简介 特点 介绍"} ) logger.info(f"[AGENT] Search results for hints, length: {len(search_results)} chars") messages = [ {"role": "system", "content": _HINT_SYSTEM_PROMPT}, {"role": "user", "content": f"答案：{name}\n\n搜索资料：\n{search_results}\n\n请生成3条提示："}, ] raw = self._llm.invoke(messages).strip() logger.info(f"[AGENT] LLM hint raw output: {raw!r}") return self._parse_hints(raw, name) except Exception as e: logger.error(f"[AGENT] Hint generation failed: {e}", exc_info=True) return self._fallback_hints(name) def _parse_hints(self, raw: str, name: str) -> List[str]: """Parse LLM hint output into a list of 3 hint strings.""" hints = [] for line in raw.splitlines(): line = line.strip() if not line: continue # Remove prefix like "提示1：" / "提示1:" / "1." etc. import re cleaned = re.sub(r'^(提示\d[：:]\s*|\d+[\.、]\s*)', '', line).strip() if cleaned: hints.append(cleaned) # Ensure exactly 3 hints if len(hints) >= 3: return hints[:3] # Pad with fallback if not enough fallback = self._fallback_hints(name) hints.extend(fallback[len(hints):]) return hints[:3] def _fallback_hints(self, name: str) -> List[str]: """Return fallback hints when search/LLM fails.""" return [ "这是一个广为人知的事物", "它在各自的领域中具有重要地位或影响力", "它的名字在国内外都有很高的知名度", ] # ── Role-play Agent ─────────────────────────────────────────────────────── def _create_roleplay_agent(self) -> SimpleAgent: """Create the role-play SimpleAgent (no tools, conversation only)""" system_prompt = self._create_system_prompt() agent = SimpleAgent( name="guess_who_agent", llm=self._llm, system_prompt=system_prompt, enable_tool_calling=False, ) subject_name = self.game_session.current_figure.get("name", "未知") logger.info(f"[AGENT] Role-play agent created | subject={subject_name}") return agent def _create_system_prompt(self) -> str: """Create dynamic system prompt based on current subject""" figure = self.game_session.current_figure return _ROLEPLAY_SYSTEM_PROMPT.format( bio=figure["bio"], ) # ── Guess ───────────────────────────────────────────────────────────────── def make_guess(self, guess_name: str) -> Dict: """Process a guess: semantic match via self._llm, then delegate to game_session. If correct, fetch figure portrait via SearchImageTool (Wikipedia). """ result = self.game_session.make_guess( guess_name, semantic_match_fn=self._semantic_match ) # If guessed correctly, fetch portrait images via Wikipedia if result.get("correct") and self._image_tool: figure_name = self.game_session.current_figure.get("name", guess_name) logger.info(f"[AGENT] Fetching portrait images for {figure_name!r}") photos = self._image_tool.search_photos(figure_name, per_page=3) result["portrait_images"] = photos logger.info(f"[AGENT] Portrait images fetched: {len(photos)} results") return result def _semantic_match(self, guess: str, actual: str) -> bool: """Use LLM to semantically judge whether guess and actual refer to the same subject.""" try: prompt = _SEMANTIC_MATCH_PROMPT.format(guess=guess.strip(), actual=actual) result = self._llm.invoke([{"role": "user", "content": prompt}]).strip() logger.info(f"[AGENT] Semantic match | guess={guess!r} actual={actual!r} llm_answer={result!r}") return result.startswith("是") except Exception as e: logger.error(f"[AGENT] Semantic match failed: {e}", exc_info=True) return False # ── Chat ────────────────────────────────────────────────────────────────── def chat(self, user_message: str) -> str: """ Process user message and return Agent reply Args: user_message: user input message Returns: Agent reply content """ try: logger.info(f"[AGENT] Calling LLM | user={user_message!r}") response = self.agent.run(user_message) logger.info(f"[AGENT] LLM response received | response={response!r}") # Update game state (increment question count) self.game_session.ask_question() return response except Exception as e: logger.error(f"[AGENT] LLM call failed: {e}", exc_info=True) return "抱歉，我现在有些恍惚，请再问一次吧。" def get_conversation_history(self) -> List[Message]: """Get full conversation history""" return self.agent.get_history() def reset_conversation(self): """Reset conversation history and reload subject""" self.agent.clear_history() # Reload a new subject figure = self._generate_figure() self.game_session.current_figure = figure # Re-generate hints hints = self._generate_hints(figure["name"]) self.game_session.hints = hints # Rebuild system prompt system_prompt = self._create_system_prompt() self.agent.system_prompt = system_prompt logger.info("[AGENT] Conversation reset and new subject loaded") # ── Utility functions ───────────────────────────────────────────────────────── def check_guess(guess: str, actual_name: str) -> bool: """ Check if user guess is correct Args: guess: user guessed name actual_name: actual subject name Returns: bool: whether guess is correct """ return guess.strip().lower() == actual_name.lower() def provide_hint(figure: Dict, hints: List[str], hint_index: int = 0) -> str: """ Provide hint about the subject Args: figure: subject info dict hints: pre-generated hint list hint_index: which hint to return (0-based) Returns: str: hint message """ if hints and hint_index < len(hints): return hints[hint_index] return "这是一个广为人知的事物"