582 lines
29 KiB
Python
582 lines
29 KiB
Python
import os
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from openai import OpenAI, APITimeoutError, APIConnectionError, RateLimitError, APIStatusError
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import time
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import random
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import traceback
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import logging
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import tiktoken
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import asyncio
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from asyncio import TimeoutError as AsyncTimeoutError
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# Configure basic logging for this module (or rely on root logger config)
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# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# logger = logging.getLogger(__name__) # Alternative: use named logger
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# Constants
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MAX_RETRIES = 3 # Maximum number of retries for API calls
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INITIAL_BACKOFF = 1 # Initial backoff time in seconds
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MAX_BACKOFF = 16 # Maximum backoff time in seconds
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STREAM_CHUNK_TIMEOUT = 10 # Timeout in seconds for receiving a chunk in stream
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# 自定义超时异常
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class Timeout(Exception):
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"""Raised when a stream chunk timeout occurs."""
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def __init__(self, message="Stream chunk timeout occurred."):
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self.message = message
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super().__init__(self.message)
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def __str__(self):
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return self.message
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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, stream_chunk_timeout=10):
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"""
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初始化 AI 代理。
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Args:
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base_url (str): 模型服务的基础 URL 或预设名称 ('deepseek', 'vllm')。
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model_name (str): 要使用的模型名称。
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api (str): API 密钥。
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timeout (int, optional): 单次 API 请求的超时时间 (秒)。 Defaults to 30.
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max_retries (int, optional): API 请求失败时的最大重试次数。 Defaults to 3.
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stream_chunk_timeout (int, optional): 流式响应中两个数据块之间的最大等待时间 (秒)。 Defaults to 10.
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"""
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logging.info("Initializing AI Agent")
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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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self.stream_chunk_timeout = stream_chunk_timeout
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logging.info(f"AI Agent Settings: base_url={self.base_url}, model={self.model_name}, timeout={self.timeout}s, max_retries={self.max_retries}, stream_chunk_timeout={self.stream_chunk_timeout}s")
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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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# try:
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# self.encoding = tiktoken.encoding_for_model(self.model_name)
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# except KeyError:
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# logging.warning(f"Encoding for model '{self.model_name}' not found. Using 'cl100k_base' encoding.")
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# self.encoding = tiktoken.get_encoding("cl100k_base")
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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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logging.info("Starting text generation process...")
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# logging.debug(f"System Prompt (first 100): {system_prompt[:100]}...")
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# logging.debug(f"User Prompt (first 100): {user_prompt[:100]}...") # Avoid logging potentially huge prompts
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logging.info(f"Generation Params: temp={temperature}, top_p={top_p}, presence_penalty={presence_penalty}")
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retry_count = 0
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max_retry_wait = 10 # Max wait time between retries
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full_response = ""
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while retry_count <= self.max_retries:
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call_start_time = None # Initialize start time
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try:
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# --- Added Logging ---
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user_prompt_size = len(user_prompt)
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logging.info(f"Attempt {retry_count + 1}/{self.max_retries + 1}: Preparing API request. User prompt size: {user_prompt_size} chars.")
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call_start_time = time.time()
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# --- End Added Logging ---
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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=False, # Ensure this is False for non-streaming method
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max_tokens=8192,
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timeout=self.timeout,
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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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# --- Added Logging ---
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call_end_time = time.time()
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logging.info(f"Attempt {retry_count + 1}/{self.max_retries + 1}: API request function returned successfully after {call_end_time - call_start_time:.2f} seconds.")
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# --- End Added Logging ---
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if response.choices and response.choices[0].message:
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full_response = response.choices[0].message.content
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logging.info(f"Received successful response. Content length: {len(full_response)} chars.")
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break # Success, exit retry loop
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else:
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logging.warning("API response structure unexpected or empty content.")
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full_response = "[Error: Empty or invalid response structure]"
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# Decide if this specific case should retry or fail immediately
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retry_count += 1 # Example: Treat as retryable
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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)
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logging.warning(f"Retrying due to unexpected response structure ({retry_count}/{self.max_retries}), waiting {wait_time:.2f}s...")
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time.sleep(wait_time)
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continue
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except (APITimeoutError, APIConnectionError, RateLimitError, APIStatusError) as e:
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# --- Added Logging ---
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if call_start_time:
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call_fail_time = time.time()
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logging.warning(f"Attempt {retry_count + 1}/{self.max_retries + 1}: API call failed/timed out after {call_fail_time - call_start_time:.2f} seconds.")
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else:
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logging.warning(f"Attempt {retry_count + 1}/{self.max_retries + 1}: API call failed before or during initiation.")
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# --- End Added Logging ---
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logging.warning(f"API Error occurred: {e}")
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should_retry = False
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if isinstance(e, (APITimeoutError, APIConnectionError, RateLimitError)):
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should_retry = True
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elif isinstance(e, APIStatusError) and e.status_code >= 500:
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should_retry = True
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if should_retry:
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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)
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logging.warning(f"Retrying API call ({retry_count}/{self.max_retries}) after error, waiting {wait_time:.2f}s...")
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time.sleep(wait_time)
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else:
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logging.error(f"Max retries ({self.max_retries}) reached for API errors. Aborting.")
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return "请求失败，无法生成内容。", 0
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else:
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logging.error(f"Non-retriable API error: {e}. Aborting.")
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return "请求失败，发生不可重试错误。", 0
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except Exception as e:
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logging.exception(f"Unexpected error during API call setup/execution:")
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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)
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logging.warning(f"Retrying API call ({retry_count}/{self.max_retries}) after unexpected error, waiting {wait_time:.2f}s...")
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time.sleep(wait_time)
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else:
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logging.error(f"Max retries ({self.max_retries}) reached after unexpected errors. Aborting.")
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return "请求失败，发生未知错误。", 0
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logging.info("Text generation completed.")
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estimated_tokens = len(full_response.split()) * 1.3
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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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logging.warning(f"Referenced folder does not exist: {file_folder}")
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return ""
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context = ""
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try:
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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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try:
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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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context += f.read()
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context += "\n\n"
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except Exception as read_err:
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logging.error(f"Failed to read file {file_path}: {read_err}")
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except Exception as list_err:
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logging.error(f"Failed to list directory {file_folder}: {list_err}")
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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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logging.info(f"Starting 'work' process. File folder: {file_folder}")
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if file_folder:
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logging.info(f"Reading context from folder: {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.strip()}\n\n--- 参考资料 ---\n{context.strip()}"
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else:
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logging.warning(f"Folder {file_folder} provided but no content read.")
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time_start = time.time()
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result = self.generate_text_stream(system_prompt, user_prompt, temperature, top_p, presence_penalty)
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# result, tokens = self.generate_text(system_prompt, user_prompt, temperature, top_p, presence_penalty)
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time_end = time.time()
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time_cost = time_end - time_start
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tokens = len(result) * 1.3
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logging.info(f"'work' completed in {time_cost:.2f}s. Estimated tokens: {tokens}")
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return result, tokens, time_cost
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def close(self):
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try:
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logging.info("Closing AI Agent (client resources will be garbage collected).")
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self.client = None
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||
except Exception as e:
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logging.error(f"Error during AI Agent close: {e}")
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# --- Streaming Methods ---
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def generate_text_stream(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
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"""
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Generates text based on prompts using a streaming connection. Handles retries with exponential backoff.
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Args:
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system_prompt: The system prompt for the AI.
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user_prompt: The user prompt for the AI.
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temperature: Sampling temperature.
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top_p: Nucleus sampling parameter.
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presence_penalty: Presence penalty parameter.
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Returns:
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str: The complete generated text.
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Raises:
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Exception: If the API call fails after all retries.
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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logging.info(f"Generating text stream with model: {self.model_name}")
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retries = 0
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backoff_time = INITIAL_BACKOFF
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last_exception = None
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full_response = ""
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while retries < self.max_retries:
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try:
|
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logging.debug(f"Attempt {retries + 1}/{self.max_retries} to generate text stream.")
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stream = self.client.chat.completions.create(
|
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model=self.model_name,
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messages=messages,
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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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timeout=self.timeout # Overall request timeout
|
||
)
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chunk_iterator = iter(stream)
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last_chunk_time = time.time()
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||
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while True:
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try:
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||
# Check for timeout since last received chunk
|
||
if time.time() - last_chunk_time > self.stream_chunk_timeout:
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||
raise Timeout(f"No chunk received for {self.stream_chunk_timeout} seconds.")
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||
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chunk = next(chunk_iterator)
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last_chunk_time = time.time() # Reset timer on successful chunk receipt
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||
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||
if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
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content = chunk.choices[0].delta.content
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full_response += content
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||
# logging.debug(f"Received chunk: {content}") # Potentially very verbose
|
||
elif chunk.choices and chunk.choices[0].finish_reason == 'stop':
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||
logging.info("Stream finished.")
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||
return full_response # Return complete response
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||
# Handle other finish reasons if needed, e.g., 'length'
|
||
|
||
except StopIteration:
|
||
logging.info("Stream iterator exhausted.")
|
||
return full_response # Return complete response
|
||
except Timeout as e:
|
||
logging.warning(f"Stream chunk timeout: {e}. Retrying if possible ({retries + 1}/{self.max_retries}).")
|
||
last_exception = e
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||
break # Break inner loop to retry the stream creation
|
||
except (APITimeoutError, APIConnectionError, RateLimitError) as e:
|
||
logging.warning(f"API error during streaming: {type(e).__name__} - {e}. Retrying if possible ({retries + 1}/{self.max_retries}).")
|
||
last_exception = e
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||
break # Break inner loop to retry the stream creation
|
||
except Exception as e:
|
||
logging.error(f"Unexpected error during streaming: {traceback.format_exc()}")
|
||
# Decide if this unexpected error should be retried or raised immediately
|
||
last_exception = e
|
||
# Option 1: Raise immediately
|
||
# raise e
|
||
# Option 2: Treat as retryable (use with caution)
|
||
break # Break inner loop to retry
|
||
|
||
# If we broke from the inner loop due to an error that needs retry
|
||
retries += 1
|
||
if retries < self.max_retries:
|
||
logging.info(f"Retrying stream in {backoff_time} seconds...")
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||
time.sleep(backoff_time + random.uniform(0, 1)) # Add jitter
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||
backoff_time = min(backoff_time * 2, MAX_BACKOFF)
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||
else:
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||
logging.error(f"Stream generation failed after {self.max_retries} retries.")
|
||
raise last_exception or Exception("Stream generation failed after max retries.")
|
||
|
||
except (Timeout, APITimeoutError, APIConnectionError, RateLimitError) as e:
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||
retries += 1
|
||
last_exception = e
|
||
logging.warning(f"Attempt {retries}/{self.max_retries} failed: {type(e).__name__} - {e}")
|
||
if retries < self.max_retries:
|
||
logging.info(f"Retrying in {backoff_time} seconds...")
|
||
time.sleep(backoff_time + random.uniform(0, 1)) # Add jitter
|
||
backoff_time = min(backoff_time * 2, MAX_BACKOFF)
|
||
else:
|
||
logging.error(f"API call failed after {self.max_retries} retries.")
|
||
return f"[生成内容失败: {str(last_exception)}]" # Return error message as string
|
||
except Exception as e:
|
||
# Catch unexpected errors during stream setup
|
||
logging.error(f"Unexpected error setting up stream: {traceback.format_exc()}")
|
||
return f"[生成内容失败: {str(e)}]" # Return error message as string
|
||
|
||
# Should not be reached if logic is correct, but as a safeguard:
|
||
logging.error("Exited stream generation loop unexpectedly.")
|
||
return f"[生成内容失败: 超过最大重试次数]" # Return error message as string
|
||
|
||
def generate_text_stream_with_callback(self, system_prompt, user_prompt,
|
||
callback_fn, temperature=0.7, top_p=0.9,
|
||
presence_penalty=0.0):
|
||
"""生成文本流并通过回调函数处理每个块
|
||
|
||
Args:
|
||
system_prompt: 系统提示词
|
||
user_prompt: 用户提示词
|
||
callback_fn: 处理每个文本块的回调函数，接收(content, is_last, is_timeout, is_error, error)参数
|
||
temperature: 温度参数
|
||
top_p: 核采样参数
|
||
presence_penalty: 存在惩罚参数
|
||
|
||
Returns:
|
||
str: 完整的响应文本
|
||
"""
|
||
messages = [
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt},
|
||
]
|
||
logging.info(f"Generating text stream with callback using model: {self.model_name}")
|
||
|
||
retries = 0
|
||
backoff_time = INITIAL_BACKOFF
|
||
last_exception = None
|
||
full_response = ""
|
||
|
||
while retries < self.max_retries:
|
||
try:
|
||
logging.debug(f"Attempt {retries + 1}/{self.max_retries} to generate text stream with callback.")
|
||
stream = self.client.chat.completions.create(
|
||
model=self.model_name,
|
||
messages=messages,
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
presence_penalty=presence_penalty,
|
||
stream=True,
|
||
timeout=self.timeout
|
||
)
|
||
|
||
chunk_iterator = iter(stream)
|
||
last_chunk_time = time.time()
|
||
|
||
try:
|
||
while True:
|
||
try:
|
||
# 检查上次接收块以来的超时
|
||
if time.time() - last_chunk_time > self.stream_chunk_timeout:
|
||
callback_fn("", is_last=True, is_timeout=True, is_error=False, error=None)
|
||
raise Timeout(f"No chunk received for {self.stream_chunk_timeout} seconds.")
|
||
|
||
chunk = next(chunk_iterator)
|
||
last_chunk_time = time.time() # 成功接收块后重置计时器
|
||
|
||
content = ""
|
||
is_last = False
|
||
|
||
if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
|
||
content = chunk.choices[0].delta.content
|
||
full_response += content
|
||
|
||
if chunk.choices and chunk.choices[0].finish_reason == 'stop':
|
||
is_last = True
|
||
|
||
# 调用回调函数处理块
|
||
callback_fn(content, is_last=is_last, is_timeout=False, is_error=False, error=None)
|
||
|
||
if is_last:
|
||
logging.info("Stream with callback finished normally.")
|
||
return full_response # 成功完成，返回完整响应
|
||
|
||
except StopIteration:
|
||
# 正常结束流
|
||
callback_fn("", is_last=True, is_timeout=False, is_error=False, error=None)
|
||
logging.info("Stream iterator exhausted normally.")
|
||
return full_response
|
||
|
||
except Timeout as e:
|
||
logging.warning(f"Stream chunk timeout: {e}")
|
||
last_exception = e
|
||
# 超时信息已通过回调传递，此处不需要再次调用回调
|
||
except (APITimeoutError, APIConnectionError, RateLimitError) as e:
|
||
logging.warning(f"API error during streaming with callback: {type(e).__name__} - {e}")
|
||
callback_fn("", is_last=True, is_timeout=False, is_error=True, error=str(e))
|
||
last_exception = e
|
||
except Exception as e:
|
||
logging.error(f"Unexpected error during streaming with callback: {traceback.format_exc()}")
|
||
callback_fn("", is_last=True, is_timeout=False, is_error=True, error=str(e))
|
||
last_exception = e
|
||
|
||
# 执行重试逻辑
|
||
retries += 1
|
||
if retries < self.max_retries:
|
||
retry_msg = f"将在 {backoff_time:.2f} 秒后重试..."
|
||
logging.info(f"Retrying stream in {backoff_time} seconds...")
|
||
callback_fn(f"\n[{retry_msg}]\n", is_last=False, is_timeout=False, is_error=False, error=None)
|
||
time.sleep(backoff_time + random.uniform(0, 1)) # 添加随机抖动
|
||
backoff_time = min(backoff_time * 2, MAX_BACKOFF)
|
||
else:
|
||
error_msg = f"API call failed after {self.max_retries} retries: {str(last_exception)}"
|
||
logging.error(error_msg)
|
||
callback_fn(f"\n[{error_msg}]\n", is_last=True, is_timeout=False, is_error=True, error=str(last_exception))
|
||
return full_response # 返回已收集的部分响应
|
||
|
||
except Exception as e:
|
||
logging.error(f"Error setting up stream with callback: {traceback.format_exc()}")
|
||
callback_fn("", is_last=True, is_timeout=False, is_error=True, error=str(e))
|
||
return f"[生成内容失败: {str(e)}]"
|
||
|
||
# 作为安全措施
|
||
error_msg = "超出最大重试次数"
|
||
logging.error(error_msg)
|
||
callback_fn(f"\n[{error_msg}]\n", is_last=True, is_timeout=False, is_error=True, error=error_msg)
|
||
return full_response
|
||
|
||
async def async_generate_text_stream(self, system_prompt, user_prompt, temperature=0.7, top_p=0.9, presence_penalty=0.0):
|
||
"""异步生成文本流
|
||
|
||
Args:
|
||
system_prompt: 系统提示词
|
||
user_prompt: 用户提示词
|
||
temperature: 温度参数
|
||
top_p: 核采样参数
|
||
presence_penalty: 存在惩罚参数
|
||
|
||
Yields:
|
||
str: 生成的文本块
|
||
|
||
Raises:
|
||
Exception: 如果API调用在所有重试后失败
|
||
"""
|
||
messages = [
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt},
|
||
]
|
||
logging.info(f"Asynchronously generating text stream with model: {self.model_name}")
|
||
|
||
retries = 0
|
||
backoff_time = INITIAL_BACKOFF
|
||
last_exception = None
|
||
|
||
while retries < self.max_retries:
|
||
try:
|
||
logging.debug(f"Async attempt {retries + 1}/{self.max_retries} to generate text stream.")
|
||
# 创建新的客户端用于异步操作
|
||
async_client = OpenAI(
|
||
api_key=self.api,
|
||
base_url=self.base_url,
|
||
timeout=self.timeout
|
||
)
|
||
|
||
stream = await async_client.chat.completions.create(
|
||
model=self.model_name,
|
||
messages=messages,
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
presence_penalty=presence_penalty,
|
||
stream=True,
|
||
timeout=self.timeout
|
||
)
|
||
|
||
last_chunk_time = time.time()
|
||
try:
|
||
async for chunk in stream:
|
||
# 检查上次接收块以来的超时
|
||
current_time = time.time()
|
||
if current_time - last_chunk_time > self.stream_chunk_timeout:
|
||
raise Timeout(f"No chunk received for {self.stream_chunk_timeout} seconds.")
|
||
|
||
last_chunk_time = current_time # 成功接收块后重置计时器
|
||
|
||
if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
|
||
content = chunk.choices[0].delta.content
|
||
yield content
|
||
|
||
logging.info("Async stream finished normally.")
|
||
return # 成功完成
|
||
|
||
except AsyncTimeoutError as e:
|
||
logging.warning(f"Async stream timeout: {e}")
|
||
last_exception = e
|
||
except Timeout as e:
|
||
logging.warning(f"Async stream chunk timeout: {e}")
|
||
last_exception = e
|
||
except Exception as e:
|
||
logging.error(f"Error during async streaming: {traceback.format_exc()}")
|
||
last_exception = e
|
||
|
||
# 执行重试逻辑
|
||
retries += 1
|
||
if retries < self.max_retries:
|
||
logging.info(f"Retrying async stream in {backoff_time} seconds...")
|
||
await asyncio.sleep(backoff_time + random.uniform(0, 1)) # 使用异步睡眠
|
||
backoff_time = min(backoff_time * 2, MAX_BACKOFF)
|
||
else:
|
||
logging.error(f"Async API call failed after {self.max_retries} retries.")
|
||
raise last_exception or Exception("Async stream generation failed after max retries.")
|
||
|
||
except (Timeout, AsyncTimeoutError, APITimeoutError, APIConnectionError, RateLimitError) as e:
|
||
retries += 1
|
||
last_exception = e
|
||
logging.warning(f"Async attempt {retries}/{self.max_retries} failed: {type(e).__name__} - {e}")
|
||
if retries < self.max_retries:
|
||
logging.info(f"Retrying async stream in {backoff_time} seconds...")
|
||
await asyncio.sleep(backoff_time + random.uniform(0, 1)) # 使用异步睡眠
|
||
backoff_time = min(backoff_time * 2, MAX_BACKOFF)
|
||
else:
|
||
logging.error(f"Async API call failed after {self.max_retries} retries.")
|
||
raise last_exception
|
||
except Exception as e:
|
||
logging.error(f"Unexpected error setting up async stream: {traceback.format_exc()}")
|
||
raise e # 立即重新引发意外错误
|
||
|
||
# 作为安全措施
|
||
logging.error("Exited async stream generation loop unexpectedly.")
|
||
raise last_exception or Exception("Async stream generation failed.")
|
||
|
||
async def async_work_stream(self, system_prompt, user_prompt, file_folder, temperature, top_p, presence_penalty):
|
||
"""异步完整工作流程：读取文件夹（如果提供），然后生成文本流"""
|
||
logging.info(f"Starting async 'work_stream' process. File folder: {file_folder}")
|
||
if file_folder:
|
||
logging.info(f"Reading context from folder: {file_folder}")
|
||
context = self.read_folder(file_folder)
|
||
if context:
|
||
user_prompt = f"{user_prompt.strip()}\n\n--- 参考资料 ---\n{context.strip()}"
|
||
else:
|
||
logging.warning(f"Folder {file_folder} provided but no content read.")
|
||
|
||
logging.info("Calling async_generate_text_stream...")
|
||
return self.async_generate_text_stream(system_prompt, user_prompt, temperature, top_p, presence_penalty)
|
||
|
||
def work_stream(self, system_prompt, user_prompt, file_folder, temperature, top_p, presence_penalty):
|
||
"""完整的工作流程（流式）：读取文件夹（如果提供），然后生成文本流"""
|
||
logging.info(f"Starting 'work_stream' process. File folder: {file_folder}")
|
||
if file_folder:
|
||
logging.info(f"Reading context from folder: {file_folder}")
|
||
context = self.read_folder(file_folder)
|
||
if context:
|
||
user_prompt = f"{user_prompt.strip()}\n\n--- 参考资料 ---\n{context.strip()}"
|
||
else:
|
||
logging.warning(f"Folder {file_folder} provided but no content read.")
|
||
|
||
full_response = self.generate_text_stream(system_prompt, user_prompt, temperature, top_p, presence_penalty)
|
||
return full_response
|
||
# --- End Streaming Methods --- |