156 lines
6.5 KiB
Python
156 lines
6.5 KiB
Python
import os
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from openai import OpenAI
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import time
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import random
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class AI_Agent():
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"""AI代理类，负责与AI模型交互生成文本内容"""
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def __init__(self, base_url, model_name, api, timeout=30, max_retries=3):
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self.url_list = {
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"ali": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"kimi": "https://api.moonshot.cn/v1",
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"doubao": "https://ark.cn-beijing.volces.com/api/v3/",
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"deepseek": "https://api.deepseek.com",
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"vllm": "http://localhost:8000/v1",
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}
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self.base_url = self.url_list.get(base_url, base_url)
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self.api = api
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self.model_name = model_name
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self.timeout = timeout
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self.max_retries = max_retries
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print(f"Initializing AI Agent with timeout={self.timeout}s, max_retries={self.max_retries}")
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self.client = OpenAI(
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api_key=self.api,
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base_url=self.base_url,
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timeout=self.timeout
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)
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def generate_text(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
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"""生成文本内容，并返回完整响应和token估计值"""
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print("系统提示词:")
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print(system_prompt)
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print("\n用户提示词:")
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print(user_prompt)
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print(f"\nAPI Key: {self.api}")
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print(f"Base URL: {self.base_url}")
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print(f"Model: {self.model_name}")
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time.sleep(random.random())
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retry_count = 0
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max_retry_wait = 10 # 最大重试等待时间（秒）
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while retry_count <= self.max_retries:
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=[{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}],
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temperature=temperature,
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top_p=top_p,
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presence_penalty=presence_penalty,
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stream=True,
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max_tokens=8192,
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timeout=self.timeout, # 设置请求超时
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extra_body={
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"repetition_penalty": 1.05,
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},
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)
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# 收集完整的输出内容
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full_response = ""
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try:
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for chunk in response:
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if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
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content = chunk.choices[0].delta.content
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full_response += content
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print(content, end="", flush=True)
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if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].finish_reason == "stop":
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break
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# 成功完成，跳出重试循环
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break
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except:
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# 处理流式响应中的超时
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print(f"\n接收响应时超时")
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if len(full_response) > 0:
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print(f"已接收部分响应({len(full_response)}字符)")
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# 如果已接收足够内容，可以考虑使用已有内容
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if len(full_response) > 100: # 假设至少需要100个字符才有意义
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print("使用已接收的部分内容继续处理")
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break
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# 否则准备重试
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retry_count += 1
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if retry_count <= self.max_retries:
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wait_time = min(2 ** retry_count + random.random(), max_retry_wait) # 指数退避
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print(f"\n等待 {wait_time:.2f} 秒后重试({retry_count}/{self.max_retries})...")
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time.sleep(wait_time)
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continue
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except Exception as e:
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print(f"\n请求发生错误: {e}")
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retry_count += 1
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if retry_count <= self.max_retries:
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wait_time = min(2 ** retry_count + random.random(), max_retry_wait) # 指数退避
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print(f"\n等待 {wait_time:.2f} 秒后重试({retry_count}/{self.max_retries})...")
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time.sleep(wait_time)
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else:
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print(f"已达到最大重试次数({self.max_retries})，放弃请求")
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return "请求失败，无法生成内容。", 0
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print("\n完成生成，正在处理结果...")
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# 由于使用流式输出，无法获取真实的token计数，因此返回估计值
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estimated_tokens = len(full_response.split()) * 1.3 # 简单估算token数量
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return full_response, estimated_tokens
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def read_folder(self, file_folder):
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"""读取指定文件夹下的所有文件内容"""
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if not os.path.exists(file_folder):
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return ""
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context = ""
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for file in os.listdir(file_folder):
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file_path = os.path.join(file_folder, file)
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if os.path.isfile(file_path):
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with open(file_path, "r", encoding="utf-8") as f:
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context += f"文件名: {file}\n"
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for line in f.readlines():
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context += line
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context += "\n"
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return context
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def work(self, system_prompt, user_prompt, file_folder, temperature, top_p, presence_penalty):
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"""完整的工作流程：生成文本并返回结果"""
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# 生成时间戳
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date_time = time.strftime("%Y-%m-%d_%H-%M-%S")
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result_file = f"/root/autodl-tmp/xhsTweetGene/result/{date_time}.md"
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# 如果提供了参考文件夹，则读取其内容
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if file_folder:
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context = self.read_folder(file_folder)
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if context:
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user_prompt = f"{user_prompt}\n参考资料:\n{context}"
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# 计时
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time_start = time.time()
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# 生成文本
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result, tokens = self.generate_text(system_prompt, user_prompt, temperature, top_p, presence_penalty)
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# 计算耗时
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time_end = time.time()
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time_cost = time_end - time_start
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return result, system_prompt, user_prompt, file_folder, result_file, tokens, time_cost
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def close(self):
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self.client.close()
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## del self.client
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del self |