timeout机制，但无效

README.md

@@ -129,12 +129,13 @@ pip install numpy pandas opencv-python pillow openai
 - `content_temperature`, `content_top_p`, `content_presence_penalty`: 内容生成 API 相关参数 (默认为 0.3, 0.4, 1.5)
 - `request_timeout`: AI API 请求的超时时间（秒，默认 30）
 - `max_retries`: 请求超时或可重试网络错误时的最大重试次数（默认 3）
-- `camera_image_subdir`: 存放原始照片的子目录名（相对于 `image_base_dir`，默认 "相机"） - **注意：此项不再用于查找描述文件。**
-- `modify_image_subdir`: 存放处理后/用于拼贴的图片的子目录名（相对于 `image_base_dir`，默认 "modify"）
-- `output_collage_subdir`: 在每个变体输出目录中存放拼贴图的子目录名（默认 "collage_img"）
-- `output_poster_subdir`: 在每个变体输出目录中存放最终海报的子目录名（默认 "poster"）
-- `output_poster_filename`: 输出的最终海报文件名（默认 "poster.jpg"）
-- `poster_target_size`: 海报目标尺寸 `[宽, 高]`（默认 `[900, 1200]`）
+- `stream_chunk_timeout`: 处理流式响应时，允许的两个数据块之间的最大等待时间（秒），用于防止流长时间挂起。
+- `camera_image_subdir`: 存放原始照片的子目录名（相对于 `image_base_dir`，默认 "相机")
+- `modify_image_subdir`: 存放处理后/用于拼贴的图片的子目录名（相对于 `image_base_dir`，默认 "modify")
+- `output_collage_subdir`: 在每个变体输出目录中存放拼贴图的子目录名（默认 "collage_img")
+- `output_poster_subdir`: 在每个变体输出目录中存放最终海报的子目录名（默认 "poster")
+- `output_poster_filename`: 输出的最终海报文件名（默认 "poster.jpg")
+- `poster_target_size`: 海报目标尺寸 `[宽, 高]`（默认 `[900, 1200]`)
 - `text_possibility`: 海报中第二段附加文字出现的概率 (默认 0.3)
 
 项目提供了一个示例配置文件 `example_config.json`，请务必复制并修改：
@@ -178,17 +179,20 @@ except Exception as e:
     print(f"Error loading config: {e}")
     sys.exit(1)
 
-# 2. 初始化 AI Agent (读取超时/重试配置)
+# 2. 初始化 AI Agent (读取超时/重试/流式块超时配置)
 ai_agent = None
 try:
     request_timeout = config.get("request_timeout", 30)
     max_retries = config.get("max_retries", 3)
+    stream_chunk_timeout = config.get("stream_chunk_timeout", 60) # 新增：读取流式块超时
+
     ai_agent = AI_Agent(
         config["api_url"],
         config["model"],
         config["api_key"],
         timeout=request_timeout,
-        max_retries=max_retries
+        max_retries=max_retries,
+        stream_chunk_timeout=stream_chunk_timeout # 新增：传递流式块超时
     )
 
     # 3. 定义提示词和参数
@@ -272,4 +276,68 @@ This refactoring makes it straightforward to add new output handlers in the futu
 
 ### 配置文件说明 (Configuration)
 
-主配置文件为 `poster_gen_config.json` (可以复制 `example_config.json` 并修改)。主要包含以下部分：
+主配置文件为 `poster_gen_config.json` (可以复制 `example_config.json` 并修改)。主要包含以下部分：
+
+#### 1. 基本配置 (Basic)
+
+*   `api_url` (必须): 大语言模型 API 地址 (或预设名称如 'vllm', 'ali', 'kimi', 'doubao', 'deepseek')
+*   `api_key` (必须): API 密钥
+*   `model` (必须): 使用的模型名称
+*   `topic_system_prompt` (必须): 选题生成系统提示词文件路径 (应为要求JSON输出的版本)
+*   `topic_user_prompt` (必须): 选题生成基础用户提示词文件路径
+*   `content_system_prompt` (必须): 内容生成系统提示词文件路径
+*   `resource_dir` (必须): 包含**资源文件信息**的列表。列表中的每个元素是一个字典，包含：
+    *   `type`: 资源类型，目前支持 `"Object"` (景点/对象信息), `"Description"` (对应的描述文件), `"Product"` (关联产品信息)。
+    *   `file_path`: 一个包含该类型所有资源文件**完整路径**的列表。
+*   `prompts_dir` (必须): 存放 Demand/Style/Refer 等提示词片段的目录路径
+*   `output_dir` (必须): 输出结果保存目录路径
+*   `image_base_dir` (必须): **图片资源根目录绝对路径或相对路径** (用于查找源图片)
+*   `poster_assets_base_dir` (必须): **海报素材根目录绝对路径或相对路径** (用于查找字体、边框、贴纸、文本背景等)
+*   `num` (必须): （选题阶段）生成选题数量
+*   `variants` (必须): （内容生成阶段）每个选题生成的变体数量
+
+#### 2. 可选配置 (Optional)
+
+*   `date` (可选, 默认空): 日期标记（用于选题生成提示词）
+*   `topic_temperature` (可选, 默认 0.2): 选题生成 API 温度参数
+*   `topic_top_p` (可选, 默认 0.5): 选题生成 API top-p 参数
+*   `topic_presence_penalty` (可选, 默认 1.5): 选题生成 API presence penalty 参数
+*   `content_temperature` (可选, 默认 0.3): 内容生成 API 温度参数
+*   `content_top_p` (可选, 默认 0.4): 内容生成 API top-p 参数
+*   `content_presence_penalty` (可选, 默认 1.5): 内容生成 API presence penalty 参数
+*   `request_timeout` (可选, 默认 30): 单个 HTTP 请求的超时时间（秒）。
+*   `max_retries` (可选, 默认 3): API 请求失败时的最大重试次数。
+*   `stream_chunk_timeout` (可选, 默认 60): 处理流式响应时，允许的两个数据块之间的最大等待时间（秒），用于防止流长时间挂起。
+*   `camera_image_subdir` (可选, 默认 "相机"): 存放原始照片的子目录名（相对于 `image_base_dir`）
+*   `modify_image_subdir` (可选, 默认 "modify"): 存放处理后/用于拼贴的图片的子目录名（相对于 `image_base_dir`）
+*   `output_collage_subdir` (可选, 默认 "collage_img"): 在每个变体输出目录中存放拼贴图的子目录名
+
+#### 3. 选题与内容生成参数 (Topic & Content Generation)
+
+*   `topic_temperature` (可选, 默认 0.2): 选题生成 API 温度参数
+*   `topic_top_p` (可选, 默认 0.5): 选题生成 API top-p 参数
+*   `topic_presence_penalty` (可选, 默认 1.5): 选题生成 API presence penalty 参数
+*   `content_temperature` (可选, 默认 0.3): 内容生成 API 温度参数
+*   `content_top_p` (可选, 默认 0.4): 内容生成 API top-p 参数
+*   `content_presence_penalty` (可选, 默认 1.5): 内容生成 API presence penalty 参数
+
+#### 4. 图片处理参数 (Image Processing)
+
+*   `camera_image_subdir` (可选, 默认 "相机"): 存放原始照片的子目录名（相对于 `image_base_dir`）
+*   `modify_image_subdir` (可选, 默认 "modify"): 存放处理后/用于拼贴的图片的子目录名（相对于 `image_base_dir`）
+*   `output_collage_subdir` (可选, 默认 "collage_img"): 在每个变体输出目录中存放拼贴图的子目录名
+
+#### 5. 海报生成参数 (Poster Generation)
+
+*   `output_poster_subdir` (可选, 默认 "poster"): 在每个变体输出目录中存放最终海报的子目录名
+*   `output_poster_filename` (可选, 默认 "poster.jpg"): 输出的最终海报文件名
+*   `poster_target_size` (可选, 默认 [900, 1200]): 海报目标尺寸 `[宽, 高]`
+*   `text_possibility` (可选, 默认 0.3): 海报中第二段附加文字出现的概率
+
+#### 6. 其他参数 (Miscellaneous)
+
+*   `request_timeout` (可选, 默认 30): 单个 HTTP 请求的超时时间（秒）。
+*   `max_retries` (可选, 默认 3): API 请求失败时的最大重试次数。
+*   `stream_chunk_timeout` (可选, 默认 60): 处理流式响应时，允许的两个数据块之间的最大等待时间（秒），用于防止流长时间挂起。
+
+项目提供了一个示例配置文件 `example_config.json`，请务必复制并修改：

core/ai_agent.py

@@ -4,15 +4,34 @@ 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):
+    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",
@@ -26,8 +45,9 @@ class AI_Agent():
         self.model_name = model_name
         self.timeout = timeout
         self.max_retries = max_retries
+        self.stream_chunk_timeout = stream_chunk_timeout
         
-        print(f"Initializing AI Agent with base_url={self.base_url}, model={self.model_name}, timeout={self.timeout}s, max_retries={self.max_retries}")
+        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,
@@ -35,6 +55,12 @@ class AI_Agent():
             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}")
@@ -176,98 +202,118 @@ class AI_Agent():
 
     # --- Streaming Methods ---
     def generate_text_stream(self, system_prompt, user_prompt, temperature, top_p, presence_penalty):
-        """生成文本内容，并以生成器方式 yield 文本块"""
-        logging.info("Streaming Generation Started...")
-        logging.debug(f"Streaming System Prompt (first 100 chars): {system_prompt[:100]}...")
-        logging.debug(f"Streaming User Prompt (first 100 chars): {user_prompt[:100]}...")
-        logging.info(f"Streaming Params: temp={temperature}, top_p={top_p}, presence_penalty={presence_penalty}")
+        """
+        Generates text based on prompts using a streaming connection. Handles retries with exponential backoff.
 
-        retry_count = 0
-        max_retry_wait = 10  
+        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.
 
-        while retry_count <= self.max_retries:
+        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.info(f"Attempting API stream call (try {retry_count + 1}/{self.max_retries + 1})")
-                response = self.client.chat.completions.create(
+                logging.debug(f"Attempt {retries + 1}/{self.max_retries} to generate text stream.")
+                stream = self.client.chat.completions.create(
                     model=self.model_name,
-                    messages=[{"role": "system", "content": system_prompt},
-                              {"role": "user", "content": user_prompt}],
+                    messages=messages,
                     temperature=temperature,
                     top_p=top_p,
-                    presence_penalty=presence_penalty,
                     stream=True,
-                    max_tokens=8192, 
-                    timeout=self.timeout, 
-                    extra_body={"repetition_penalty": 1.05}, 
+                    timeout=self.timeout # Overall request timeout
                 )
 
-                try:
-                    logging.info("Stream connected, receiving content...")
-                    yielded_something = False 
-                    for chunk in response:
-                        if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
+                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
-                            yielded_something = True
+                        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'
 
-                    if yielded_something:
-                        logging.info("Stream finished successfully.")
-                    else:
-                        logging.warning("Stream finished, but no content was yielded.")
-                    return
+                    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
 
-                except APIConnectionError as stream_err: 
-                    logging.warning(f"Stream connection error occurred: {stream_err}")
-                    retry_count += 1
-                    if retry_count <= self.max_retries:
-                        wait_time = min(2 ** retry_count + random.random(), max_retry_wait)
-                        logging.warning(f"Retrying connection ({retry_count}/{self.max_retries}), waiting {wait_time:.2f}s...")
-                        time.sleep(wait_time)
-                        continue 
-                    else:
-                        logging.error("Max retries reached after stream connection error.")
-                        yield f"[STREAM_ERROR: Max retries reached after connection error: {stream_err}]" 
-                        return 
-
-                except Exception as stream_err: 
-                    logging.exception("Error occurred during stream processing:")
-                    yield f"[STREAM_ERROR: {stream_err}]" 
-                    return 
-
-            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)
-                        continue
-                    else:
-                        logging.error(f"Max retries ({self.max_retries}) reached for API errors. Aborting stream.")
-                        yield "[API_ERROR: Max retries reached]"
-                        return
+                # 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"Non-retriable API error: {e}. Aborting stream.")
-                    yield f"[API_ERROR: Non-retriable status {e.status_code if isinstance(e, APIStatusError) else 'Unknown'}]"
-                    return
-            except Exception as e: 
-                logging.exception("Non-retriable error occurred during API call setup:")
-                yield f"[FATAL_ERROR: {e}]"
-                return 
+                    logging.error(f"Stream generation failed after {self.max_retries} retries.")
+                    raise last_exception or Exception("Stream generation failed after max retries.")
 
-        logging.error("Stream generation failed after exhausting all retries.")
-        yield "[ERROR: Failed after all 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):
-        """工作流程的流式版本：返回文本生成器"""
+        """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}")

example_config.json

@@ -48,9 +48,11 @@
     "content_presence_penalty": 1.5,
     "request_timeout": 30,
     "max_retries": 3,
+    "stream_chunk_timeout": 60,
     "output_collage_subdir": "collage_img",
     "output_poster_subdir": "poster",
     "output_poster_filename": "poster.jpg",
     "poster_target_size": [900, 1200],
-    "text_possibility": 0.3
+    "text_possibility": 0.3,
+    "description_filename": "description.txt"
 } 

examples/test_pipeline_steps.py

@@ -117,6 +117,7 @@ def main_test():
         ai_api_key = config.get("api_key")
         request_timeout = config.get("request_timeout", 30)
         max_retries = config.get("max_retries", 3)
+        stream_chunk_timeout = config.get("stream_chunk_timeout", 60) # Read stream timeout
         if not all([ai_api_url, ai_model, ai_api_key]):
             raise ValueError("Missing required AI configuration (api_url, model, api_key)")
         logging.info("Initializing AI Agent for content generation test...")
@@ -125,7 +126,8 @@ def main_test():
             model_name=ai_model,
             api=ai_api_key,
             timeout=request_timeout,
-            max_retries=max_retries
+            max_retries=max_retries,
+            stream_chunk_timeout=stream_chunk_timeout # Pass stream timeout
         )
 
         total_topics = len(topics_list)

examples/test_stream.py

@@ -66,6 +66,7 @@ def main():
         ai_api_key = config.get("api_key")
         request_timeout = config.get("request_timeout", 30)
         max_retries = config.get("max_retries", 3)
+        stream_chunk_timeout = config.get("stream_chunk_timeout", 60) # Get stream chunk timeout
         
         # Check for required AI params
         if not all([ai_api_url, ai_model, ai_api_key]):
@@ -80,7 +81,8 @@ def main():
             model=ai_model,      # Use extracted var
             api_key=ai_api_key,       # Use extracted var
             timeout=request_timeout,
-            max_retries=max_retries
+            max_retries=max_retries,
+            stream_chunk_timeout=stream_chunk_timeout # Pass it here
         )
 
         # Example call to work_stream

main.py

@@ -91,14 +91,16 @@ def generate_content_and_posters_step(config, run_id, topics_list, output_handle
     ai_agent = None
     try:
         # --- Initialize AI Agent for Content Generation --- 
-        request_timeout = config.get("request_timeout", 30) # Get timeout from config
-        max_retries = config.get("max_retries", 3)         # Get max_retries from config
+        request_timeout = config.get("request_timeout", 30) # Default 30 seconds
+        max_retries = config.get("max_retries", 3) # Default 3 retries
+        stream_chunk_timeout = config.get("stream_chunk_timeout", 60) # Default 60 seconds for stream chunk
         ai_agent = AI_Agent(
             config["api_url"],
             config["model"],
             config["api_key"],
             timeout=request_timeout,
-            max_retries=max_retries
+            max_retries=max_retries,
+            stream_chunk_timeout=stream_chunk_timeout
         )
         logging.info("AI Agent for content generation initialized.")