secret 神秘小功能
This commit is contained in:
SengokuCola
46
results/personality_result.json
Normal file
46
results/personality_result.json
Normal file
@@ -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"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -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(
|
||||
|
||||
128
src/plugins/personality/offline_llm.py
Normal file
128
src/plugins/personality/offline_llm.py
Normal file
@@ -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", ""
|
||||
@@ -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()
|
||||
|
||||
Reference in New Issue
Block a user