mirror of
https://github.com/linyqh/NarratoAI.git
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7309208282
更新generate_narration_script.py、base.py和generate_short_summary.py文件，重构解说文案生成和视觉分析器的实现，优先使用新的LLM服务架构。添加回退机制以确保兼容性，增强系统的稳定性和用户体验。
252 lines
8.4 KiB
Python
252 lines
8.4 KiB
Python
"""
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硅基流动API提供商实现
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支持硅基流动的视觉模型和文本生成模型
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"""
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import asyncio
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import base64
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import io
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from typing import List, Dict, Any, Optional, Union
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from pathlib import Path
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import PIL.Image
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from openai import OpenAI
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from loguru import logger
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from ..base import VisionModelProvider, TextModelProvider
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from ..exceptions import APICallError
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class SiliconflowVisionProvider(VisionModelProvider):
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"""硅基流动视觉模型提供商"""
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@property
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def provider_name(self) -> str:
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return "siliconflow"
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@property
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def supported_models(self) -> List[str]:
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return [
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"Qwen/Qwen2.5-VL-32B-Instruct",
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"Qwen/Qwen2-VL-72B-Instruct",
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"deepseek-ai/deepseek-vl2",
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"OpenGVLab/InternVL2-26B"
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]
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def _initialize(self):
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"""初始化硅基流动客户端"""
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if not self.base_url:
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self.base_url = "https://api.siliconflow.cn/v1"
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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async def analyze_images(self,
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images: List[Union[str, Path, PIL.Image.Image]],
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prompt: str,
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batch_size: int = 10,
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**kwargs) -> List[str]:
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"""
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使用硅基流动API分析图片
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Args:
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images: 图片列表
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prompt: 分析提示词
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batch_size: 批处理大小
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**kwargs: 其他参数
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Returns:
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分析结果列表
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"""
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logger.info(f"开始分析 {len(images)} 张图片，使用硅基流动")
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# 预处理图片
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processed_images = self._prepare_images(images)
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# 分批处理
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results = []
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for i in range(0, len(processed_images), batch_size):
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batch = processed_images[i:i + batch_size]
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logger.info(f"处理第 {i//batch_size + 1} 批，共 {len(batch)} 张图片")
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try:
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result = await self._analyze_batch(batch, prompt)
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results.append(result)
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except Exception as e:
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logger.error(f"批次 {i//batch_size + 1} 处理失败: {str(e)}")
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results.append(f"批次处理失败: {str(e)}")
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return results
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async def _analyze_batch(self, batch: List[PIL.Image.Image], prompt: str) -> str:
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"""分析一批图片"""
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# 构建消息内容
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content = [{"type": "text", "text": prompt}]
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# 添加图片
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for img in batch:
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base64_image = self._image_to_base64(img)
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content.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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})
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# 构建消息
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messages = [{
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"role": "user",
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"content": content
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}]
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# 调用API
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response = await asyncio.to_thread(
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self.client.chat.completions.create,
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model=self.model_name,
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messages=messages,
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max_tokens=4000,
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temperature=1.0
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)
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if response.choices and len(response.choices) > 0:
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return response.choices[0].message.content
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else:
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raise APICallError("硅基流动API返回空响应")
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def _image_to_base64(self, img: PIL.Image.Image) -> str:
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"""将PIL图片转换为base64编码"""
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img_buffer = io.BytesIO()
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img.save(img_buffer, format='JPEG', quality=85)
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img_bytes = img_buffer.getvalue()
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return base64.b64encode(img_bytes).decode('utf-8')
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async def _make_api_call(self, payload: Dict[str, Any]) -> Dict[str, Any]:
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"""执行API调用 - 由于使用OpenAI SDK，这个方法主要用于兼容基类"""
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pass
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class SiliconflowTextProvider(TextModelProvider):
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"""硅基流动文本生成提供商"""
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@property
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def provider_name(self) -> str:
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return "siliconflow"
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@property
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def supported_models(self) -> List[str]:
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return [
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"deepseek-ai/DeepSeek-R1",
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"deepseek-ai/DeepSeek-V3",
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"Qwen/Qwen2.5-72B-Instruct",
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"Qwen/Qwen2.5-32B-Instruct",
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"meta-llama/Llama-3.1-70B-Instruct",
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"meta-llama/Llama-3.1-8B-Instruct",
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"01-ai/Yi-1.5-34B-Chat"
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]
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def _initialize(self):
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"""初始化硅基流动客户端"""
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if not self.base_url:
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self.base_url = "https://api.siliconflow.cn/v1"
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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async def generate_text(self,
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prompt: str,
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system_prompt: Optional[str] = None,
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temperature: float = 1.0,
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max_tokens: Optional[int] = None,
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response_format: Optional[str] = None,
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**kwargs) -> str:
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"""
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使用硅基流动API生成文本
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Args:
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prompt: 用户提示词
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system_prompt: 系统提示词
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temperature: 生成温度
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max_tokens: 最大token数
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response_format: 响应格式 ('json' 或 None)
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**kwargs: 其他参数
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Returns:
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生成的文本内容
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"""
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# 构建消息列表
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messages = self._build_messages(prompt, system_prompt)
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# 构建请求参数
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request_params = {
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"model": self.model_name,
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"messages": messages,
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"temperature": temperature
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}
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if max_tokens:
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request_params["max_tokens"] = max_tokens
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# 处理JSON格式输出
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if response_format == "json":
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if self._supports_response_format():
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request_params["response_format"] = {"type": "json_object"}
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else:
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# 对于不支持response_format的模型，在提示词中添加约束
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messages[-1]["content"] += "\n\n请确保输出严格的JSON格式，不要包含任何其他文字或标记。"
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try:
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# 发送API请求
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response = await asyncio.to_thread(
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self.client.chat.completions.create,
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**request_params
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)
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# 提取生成的内容
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if response.choices and len(response.choices) > 0:
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content = response.choices[0].message.content
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# 对于不支持response_format的模型，清理输出
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if response_format == "json" and not self._supports_response_format():
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content = self._clean_json_output(content)
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logger.debug(f"硅基流动API调用成功，消耗tokens: {response.usage.total_tokens if response.usage else 'N/A'}")
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return content
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else:
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raise APICallError("硅基流动API返回空响应")
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except Exception as e:
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logger.error(f"硅基流动API调用失败: {str(e)}")
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raise APICallError(f"硅基流动API调用失败: {str(e)}")
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def _supports_response_format(self) -> bool:
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"""检查模型是否支持response_format参数"""
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# DeepSeek R1 和 V3 不支持 response_format=json_object
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unsupported_models = [
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"deepseek-ai/deepseek-r1",
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"deepseek-ai/deepseek-v3"
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]
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return not any(unsupported in self.model_name.lower() for unsupported in unsupported_models)
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def _clean_json_output(self, output: str) -> str:
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"""清理JSON输出，移除markdown标记等"""
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import re
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# 移除可能的markdown代码块标记
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output = re.sub(r'^```json\s*', '', output, flags=re.MULTILINE)
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output = re.sub(r'^```\s*$', '', output, flags=re.MULTILINE)
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output = re.sub(r'^```.*$', '', output, flags=re.MULTILINE)
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# 移除前后空白字符
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output = output.strip()
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return output
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async def _make_api_call(self, payload: Dict[str, Any]) -> Dict[str, Any]:
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"""执行API调用 - 由于使用OpenAI SDK，这个方法主要用于兼容基类"""
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pass
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