From 1076b509a307bd4bb22f2e9613eb9009552340ce Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Wed, 19 Mar 2025 15:22:34 +0800 Subject: [PATCH] =?UTF-8?q?secret=20=E7=A5=9E=E7=A7=98=E5=B0=8F=E5=8A=9F?= =?UTF-8?q?=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- results/personality_result.json | 46 +++++++ src/plugins/chat/prompt_builder.py | 11 -- src/plugins/personality/offline_llm.py | 128 ++++++++++++++++++ src/plugins/personality/renqingziji.py | 175 +++++++++++++++++++++++++ 4 files changed, 349 insertions(+), 11 deletions(-) create mode 100644 results/personality_result.json create mode 100644 src/plugins/personality/offline_llm.py diff --git a/results/personality_result.json b/results/personality_result.json new file mode 100644 index 000000000..6424598b9 --- /dev/null +++ b/results/personality_result.json @@ -0,0 +1,46 @@ +{ + "final_scores": { + "开放性": 5.5, + "尽责性": 5.0, + "外向性": 6.0, + "宜人性": 1.5, + "神经质": 6.0 + }, + "scenarios": [ + { + "场景": "在团队项目中，你发现一个同事的工作质量明显低于预期，这可能会影响整个项目的进度。", + "评估维度": [ + "尽责性", + "宜人性" + ] + }, + { + "场景": "你被邀请参加一个完全陌生的社交活动，现场都是不认识的人。", + "评估维度": [ + "外向性", + "神经质" + ] + }, + { + "场景": "你的朋友向你推荐了一个新的艺术展览，但风格与你平时接触的完全不同。", + "评估维度": [ + "开放性", + "外向性" + ] + }, + { + "场景": "在工作中，你遇到了一个技术难题，需要学习全新的技术栈。", + "评估维度": [ + "开放性", + "尽责性" + ] + }, + { + "场景": "你的朋友因为个人原因情绪低落，向你寻求帮助。", + "评估维度": [ + "宜人性", + "神经质" + ] + } + ] +} \ No newline at end of file diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 892559f52..65edf6c8e 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -27,17 +27,6 @@ class PromptBuilder: message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None) -> tuple[str, str]: - """构建prompt - - Args: - message_txt: 消息文本 - sender_name: 发送者昵称 - # relationship_value: 关系值 - group_id: 群组ID - - Returns: - str: 构建好的prompt - """ # 关系（载入当前聊天记录里部分人的关系） who_chat_in_group = [chat_stream] who_chat_in_group += get_recent_group_speaker( diff --git a/src/plugins/personality/offline_llm.py b/src/plugins/personality/offline_llm.py new file mode 100644 index 000000000..ac89ddb25 --- /dev/null +++ b/src/plugins/personality/offline_llm.py @@ -0,0 +1,128 @@ +import asyncio +import os +import time +from typing import Tuple, Union + +import aiohttp +import requests +from src.common.logger import get_module_logger + +logger = get_module_logger("offline_llm") + +class LLMModel: + def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs): + self.model_name = model_name + self.params = kwargs + self.api_key = os.getenv("SILICONFLOW_KEY") + self.base_url = os.getenv("SILICONFLOW_BASE_URL") + + if not self.api_key or not self.base_url: + raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置") + + logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url + + def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]: + """根据输入的提示生成模型的响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0.5, + **self.params + } + + # 发送请求到完整的 chat/completions 端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + logger.info(f"Request URL: {api_url}") # 记录请求的 URL + + max_retries = 3 + base_wait_time = 15 # 基础等待时间（秒） + + for retry in range(max_retries): + try: + response = requests.post(api_url, headers=headers, json=data) + + if response.status_code == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + time.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + time.sleep(wait_time) + else: + logger.error(f"请求失败: {str(e)}") + return f"请求失败: {str(e)}", "" + + logger.error("达到最大重试次数，请求仍然失败") + return "达到最大重试次数，请求仍然失败", "" + + async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: + """异步方式根据输入的提示生成模型的响应""" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + # 构建请求体 + data = { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0.5, + **self.params + } + + # 发送请求到完整的 chat/completions 端点 + api_url = f"{self.base_url.rstrip('/')}/chat/completions" + logger.info(f"Request URL: {api_url}") # 记录请求的 URL + + max_retries = 3 + base_wait_time = 15 + + async with aiohttp.ClientSession() as session: + for retry in range(max_retries): + try: + async with session.post(api_url, headers=headers, json=data) as response: + if response.status == 429: + wait_time = base_wait_time * (2 ** retry) # 指数退避 + logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() # 检查其他响应状态 + + result = await response.json() + if "choices" in result and len(result["choices"]) > 0: + content = result["choices"][0]["message"]["content"] + reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") + return content, reasoning_content + return "没有返回结果", "" + + except Exception as e: + if retry < max_retries - 1: # 如果还有重试机会 + wait_time = base_wait_time * (2 ** retry) + logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") + await asyncio.sleep(wait_time) + else: + logger.error(f"请求失败: {str(e)}") + return f"请求失败: {str(e)}", "" + + logger.error("达到最大重试次数，请求仍然失败") + return "达到最大重试次数，请求仍然失败", "" diff --git a/src/plugins/personality/renqingziji.py b/src/plugins/personality/renqingziji.py index e69de29bb..679d555bf 100644 --- a/src/plugins/personality/renqingziji.py +++ b/src/plugins/personality/renqingziji.py @@ -0,0 +1,175 @@ +from typing import Dict, List +import json +import os +import random +from pathlib import Path +from dotenv import load_dotenv +import sys + +current_dir = Path(__file__).resolve().parent +# 获取项目根目录（上三层目录） +project_root = current_dir.parent.parent.parent +# env.dev文件路径 +env_path = project_root / ".env.prod" + +root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) +sys.path.append(root_path) + +from src.plugins.personality.offline_llm import LLMModel + +# 加载环境变量 +if env_path.exists(): + print(f"从 {env_path} 加载环境变量") + load_dotenv(env_path) +else: + print(f"未找到环境变量文件: {env_path}") + print("将使用默认配置") + + +class PersonalityEvaluator: + def __init__(self): + self.personality_traits = { + "开放性": 0, + "尽责性": 0, + "外向性": 0, + "宜人性": 0, + "神经质": 0 + } + self.scenarios = [ + { + "场景": "在团队项目中，你发现一个同事的工作质量明显低于预期，这可能会影响整个项目的进度。", + "评估维度": ["尽责性", "宜人性"] + }, + { + "场景": "你被邀请参加一个完全陌生的社交活动，现场都是不认识的人。", + "评估维度": ["外向性", "神经质"] + }, + { + "场景": "你的朋友向你推荐了一个新的艺术展览，但风格与你平时接触的完全不同。", + "评估维度": ["开放性", "外向性"] + }, + { + "场景": "在工作中，你遇到了一个技术难题，需要学习全新的技术栈。", + "评估维度": ["开放性", "尽责性"] + }, + { + "场景": "你的朋友因为个人原因情绪低落，向你寻求帮助。", + "评估维度": ["宜人性", "神经质"] + } + ] + self.llm = LLMModel() + + def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]: + """ + 使用 DeepSeek AI 评估用户对特定场景的反应 + """ + prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（0-10分）。 +场景：{scenario} +用户描述：{response} + +需要评估的维度：{', '.join(dimensions)} + +请按照以下格式输出评估结果（仅输出JSON格式）： +{{ + "维度1": 分数, + "维度2": 分数 +}} + +评估标准： +- 开放性：对新事物的接受程度和创造性思维 +- 尽责性：计划性、组织性和责任感 +- 外向性：社交倾向和能量水平 +- 宜人性：同理心、合作性和友善程度 +- 神经质：情绪稳定性和压力应对能力 + +请确保分数在0-10之间，并给出合理的评估理由。""" + + try: + ai_response, _ = self.llm.generate_response(prompt) + # 尝试从AI响应中提取JSON部分 + start_idx = ai_response.find('{') + end_idx = ai_response.rfind('}') + 1 + if start_idx != -1 and end_idx != 0: + json_str = ai_response[start_idx:end_idx] + scores = json.loads(json_str) + # 确保所有分数在0-10之间 + return {k: max(0, min(10, float(v))) for k, v in scores.items()} + else: + print("AI响应格式不正确，使用默认评分") + return {dim: 5.0 for dim in dimensions} + except Exception as e: + print(f"评估过程出错：{str(e)}") + return {dim: 5.0 for dim in dimensions} + +def main(): + print("欢迎使用人格形象创建程序！") + print("接下来，您将面对一系列场景。请根据您想要创建的角色形象，描述在该场景下可能的反应。") + print("每个场景都会评估不同的人格维度，最终得出完整的人格特征评估。") + print("\n准备好了吗？按回车键开始...") + input() + + evaluator = PersonalityEvaluator() + final_scores = { + "开放性": 0, + "尽责性": 0, + "外向性": 0, + "宜人性": 0, + "神经质": 0 + } + dimension_counts = {trait: 0 for trait in final_scores.keys()} + + for i, scenario_data in enumerate(evaluator.scenarios, 1): + print(f"\n场景 {i}/{len(evaluator.scenarios)}:") + print("-" * 50) + print(scenario_data["场景"]) + print("\n请描述您的角色在这种情况下会如何反应：") + response = input().strip() + + if not response: + print("反应描述不能为空！") + continue + + print("\n正在评估您的描述...") + scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"]) + + # 更新最终分数 + for dimension, score in scores.items(): + final_scores[dimension] += score + dimension_counts[dimension] += 1 + + print("\n当前评估结果：") + print("-" * 30) + for dimension, score in scores.items(): + print(f"{dimension}: {score}/10") + + if i < len(evaluator.scenarios): + print("\n按回车键继续下一个场景...") + input() + + # 计算平均分 + for dimension in final_scores: + if dimension_counts[dimension] > 0: + final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2) + + print("\n最终人格特征评估结果：") + print("-" * 30) + for trait, score in final_scores.items(): + print(f"{trait}: {score}/10") + + # 保存结果 + result = { + "final_scores": final_scores, + "scenarios": evaluator.scenarios + } + + # 确保目录存在 + os.makedirs("results", exist_ok=True) + + # 保存到文件 + with open("results/personality_result.json", "w", encoding="utf-8") as f: + json.dump(result, f, ensure_ascii=False, indent=2) + + print("\n结果已保存到 results/personality_result.json") + +if __name__ == "__main__": + main()