TravelContentCreator/core/ai_agent.py

import os
from openai import OpenAI, APITimeoutError, APIConnectionError, RateLimitError, APIStatusError
import time
import random
import traceback
import logging
import tiktoken

# Configure basic logging for this module (or rely on root logger config)
# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# logger = logging.getLogger(__name__) # Alternative: use named logger

# Constants
MAX_RETRIES = 3  # Maximum number of retries for API calls
INITIAL_BACKOFF = 1  # Initial backoff time in seconds
MAX_BACKOFF = 16  # Maximum backoff time in seconds
STREAM_CHUNK_TIMEOUT = 10  # Timeout in seconds for receiving a chunk in stream

class AI_Agent():
    """AI代理类，负责与AI模型交互生成文本内容"""
    
    def __init__(self, base_url, model_name, api, timeout=30, max_retries=3, stream_chunk_timeout=10):
        """
        初始化 AI 代理。

        Args:
            base_url (str): 模型服务的基础 URL 或预设名称 ('deepseek', 'vllm')。
            model_name (str): 要使用的模型名称。
            api (str): API 密钥。
            timeout (int, optional): 单次 API 请求的超时时间 (秒)。 Defaults to 30.
            max_retries (int, optional): API 请求失败时的最大重试次数。 Defaults to 3.
            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.
        """
        logging.info("Initializing AI Agent")
        self.url_list = {
            "ali": "https://dashscope.aliyuncs.com/compatible-mode/v1",
            "kimi": "https://api.moonshot.cn/v1",
            "doubao": "https://ark.cn-beijing.volces.com/api/v3/",
            "deepseek": "https://api.deepseek.com",
            "vllm": "http://localhost:8000/v1",
        }
        
        self.base_url = self.url_list.get(base_url, base_url)
        self.api = api
        self.model_name = model_name
        self.timeout = timeout
        self.max_retries = max_retries
        self.stream_chunk_timeout = stream_chunk_timeout
        
        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")
        
        self.client = OpenAI(
            api_key=self.api,
            base_url=self.base_url,
            timeout=self.timeout
        )
        
        try:
            self.encoding = tiktoken.encoding_for_model(self.model_name)
        except KeyError:
            logging.warning(f"Encoding for model '{self.model_name}' not found. Using 'cl100k_base' encoding.")
            self.encoding = tiktoken.get_encoding("cl100k_base")
        
    def generate_text(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
        """生成文本内容，并返回完整响应和token估计值"""
        logging.info(f"Generating text with model: {self.model_name}, temp={temperature}, top_p={top_p}, presence_penalty={presence_penalty}")
        logging.debug(f"System Prompt (first 100 chars): {system_prompt[:100]}...")
        logging.debug(f"User Prompt (first 100 chars): {user_prompt[:100]}...")

        time.sleep(random.random())
        retry_count = 0
        max_retry_wait = 10

        while retry_count <= self.max_retries:
            try:
                logging.info(f"Attempting API call (try {retry_count + 1}/{self.max_retries + 1})")
                response = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=[{"role": "system", "content": system_prompt}, 
                             {"role": "user", "content": user_prompt}],
                    temperature=temperature, 
                    top_p=top_p,
                    presence_penalty=presence_penalty,
                    stream=True,
                    max_tokens=8192,
                    timeout=self.timeout,  
                    extra_body={
                        "repetition_penalty": 1.05,
                    },
                )
                
                full_response = ""
                stream_timed_out = False
                try:
                    for chunk in response:
                        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
                            content = chunk.choices[0].delta.content
                            full_response += content
                        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].finish_reason == "stop":
                            break
                    # Successfully finished stream
                    break
                
                except Exception as stream_err:
                    logging.warning(f"Exception during stream processing: {stream_err}")
                    stream_timed_out = True

                if stream_timed_out:
                    if len(full_response) > 100:
                        logging.warning(f"Stream interrupted, but received {len(full_response)} characters. Using partial content.")
                        break
                    
                    retry_count += 1
                    if retry_count <= self.max_retries:
                        wait_time = min(2 ** retry_count + random.random(), max_retry_wait)
                        logging.warning(f"Stream error/timeout. Waiting {wait_time:.2f}s before retry ({retry_count}/{self.max_retries})...")
                        time.sleep(wait_time)
                    continue
            
            except (APITimeoutError, APIConnectionError, RateLimitError, APIStatusError) as e:
                logging.warning(f"API Error occurred: {e}")
                should_retry = False
                if isinstance(e, (APITimeoutError, APIConnectionError, RateLimitError)):
                    should_retry = True
                elif isinstance(e, APIStatusError) and e.status_code >= 500:
                    should_retry = True
                
                if should_retry:
                    retry_count += 1
                    if retry_count <= self.max_retries:
                        wait_time = min(2 ** retry_count + random.random(), max_retry_wait)
                        logging.warning(f"Retrying API call ({retry_count}/{self.max_retries}) after error, waiting {wait_time:.2f}s...")
                        time.sleep(wait_time)
                    else:
                        logging.error(f"Max retries ({self.max_retries}) reached for API errors. Aborting.")
                        return "请求失败，无法生成内容。", 0 
                else:
                    logging.error(f"Non-retriable API error: {e}. Aborting.")
                    return "请求失败，发生不可重试错误。", 0
            except Exception as e:
                logging.exception(f"Unexpected error during API call setup/execution:")
                retry_count += 1
                if retry_count <= self.max_retries:
                    wait_time = min(2 ** retry_count + random.random(), max_retry_wait)
                    logging.warning(f"Retrying API call ({retry_count}/{self.max_retries}) after unexpected error, waiting {wait_time:.2f}s...")
                    time.sleep(wait_time)
                else:
                    logging.error(f"Max retries ({self.max_retries}) reached after unexpected errors. Aborting.")
                    return "请求失败，发生未知错误。", 0
        
        logging.info("Text generation completed.")
        estimated_tokens = len(full_response.split()) * 1.3
        return full_response, estimated_tokens

    def read_folder(self, file_folder):
        """读取指定文件夹下的所有文件内容"""
        if not os.path.exists(file_folder):
            logging.warning(f"Referenced folder does not exist: {file_folder}")
            return ""
            
        context = ""
        try:
            for file in os.listdir(file_folder):
                file_path = os.path.join(file_folder, file)
                if os.path.isfile(file_path):
                    try:
                        with open(file_path, "r", encoding="utf-8") as f:
                            context += f"文件名: {file}\n"
                            context += f.read()
                            context += "\n\n"
                    except Exception as read_err:
                        logging.error(f"Failed to read file {file_path}: {read_err}")   
        except Exception as list_err:
            logging.error(f"Failed to list directory {file_folder}: {list_err}")                      
        return context
    
    def work(self, system_prompt, user_prompt, file_folder, temperature, top_p, presence_penalty):
        """完整的工作流程：生成文本并返回结果"""
        logging.info(f"Starting 'work' 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.")
        
        time_start = time.time()
        result, tokens = self.generate_text(system_prompt, user_prompt, temperature, top_p, presence_penalty)
        time_end = time.time()
        time_cost = time_end - time_start
        logging.info(f"'work' completed in {time_cost:.2f}s. Estimated tokens: {tokens}")
        
        return result, tokens, time_cost 
    
    def close(self):
        try:
            logging.info("Closing AI Agent (client resources will be garbage collected).")
            self.client = None
        except Exception as e:
            logging.error(f"Error during AI Agent close: {e}")

    # --- Streaming Methods ---
    def generate_text_stream(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
        """
        Generates text based on prompts using a streaming connection. Handles retries with exponential backoff.

        Args:
            system_prompt: The system prompt for the AI.
            user_prompt: The user prompt for the AI.
            temperature: Sampling temperature.
            top_p: Nucleus sampling parameter.

        Yields:
            str: Chunks of the generated text.

        Raises:
            Exception: If the API call fails after all retries.
        """
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ]
        logging.info(f"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"Attempt {retries + 1}/{self.max_retries} to generate text stream.")
                stream = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=messages,
                    temperature=temperature,
                    top_p=top_p,
                    stream=True,
                    timeout=self.timeout # Overall request timeout
                )

                chunk_iterator = iter(stream)
                last_chunk_time = time.time()

                while True:
                    try:
                        # Check for timeout since last received chunk
                        if time.time() - last_chunk_time > self.stream_chunk_timeout:
                             raise Timeout(f"No chunk received for {self.stream_chunk_timeout} seconds.")

                        chunk = next(chunk_iterator)
                        last_chunk_time = time.time() # Reset timer on successful chunk receipt

                        if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
                            content = chunk.choices[0].delta.content
                            # logging.debug(f"Received chunk: {content}") # Potentially very verbose
                            yield content
                        elif chunk.choices and chunk.choices[0].finish_reason == 'stop':
                             logging.info("Stream finished.")
                             return # Successful completion
                        # Handle other finish reasons if needed, e.g., 'length'

                    except StopIteration:
                        logging.info("Stream iterator exhausted.")
                        return # End of stream normally
                    except Timeout as e:
                        logging.warning(f"Stream chunk timeout: {e}. Retrying if possible ({retries + 1}/{self.max_retries}).")
                        last_exception = e
                        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
                        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...")
                    time.sleep(backoff_time + random.uniform(0, 1)) # Add jitter
                    backoff_time = min(backoff_time * 2, MAX_BACKOFF)
                else:
                    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:
                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.")
                    raise last_exception
            except Exception as e:
                 # Catch unexpected errors during stream setup
                 logging.error(f"Unexpected error setting up stream: {traceback.format_exc()}")
                 raise e # Re-raise unexpected errors immediately

        # Should not be reached if logic is correct, but as a safeguard:
        logging.error("Exited stream generation loop unexpectedly.")
        raise last_exception or Exception("Stream generation failed.")


    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.")

        logging.info("Calling generate_text_stream...")
        return self.generate_text_stream(system_prompt, user_prompt, temperature, top_p, presence_penalty)
    # --- End Added Streaming Methods ---