mirror of
https://github.com/linyqh/NarratoAI.git
synced 2026-06-20 06:12:06 +00:00
e6e39d2dcd
- 新增短剧解说全流程四类提示词模板：解说文案生成、片段规划、文案画面匹配、脚本修复 - 重构原有脚本生成提示词至v2.1，改为基于上游规划片段生成合规解说脚本 - 为LLM基础服务层新增流式文本生成接口，完善OpenAI兼容提供商的流式实现，支持流式回调与推理内容提取 - 重构OpenAI兼容文本提供商的生成逻辑，提取公共参数构建方法 - 新增多语言国际化文案，覆盖解说语言、短剧类型、原片占比等配置项与交互提示 - 新增多套单元测试，覆盖脚本校验、适配器流程、工具函数等模块 - 封装SubtitleAnalyzerAdapter，统一短剧解说脚本生成的整套业务接口 - 新增前端交互所需的解说文案审核相关提示文案
213 lines
7.1 KiB
Python
213 lines
7.1 KiB
Python
"""
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大模型服务提供商基类定义
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定义了统一的大模型服务接口，包括视觉模型和文本生成模型的抽象基类
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"""
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from abc import ABC, abstractmethod
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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 loguru import logger
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from .exceptions import LLMServiceError, ConfigurationError
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class BaseLLMProvider(ABC):
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"""大模型服务提供商基类"""
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def __init__(self,
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api_key: str,
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model_name: str,
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base_url: Optional[str] = None,
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**kwargs):
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"""
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初始化大模型服务提供商
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Args:
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api_key: API密钥
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model_name: 模型名称
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base_url: API基础URL
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**kwargs: 其他配置参数
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"""
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self.api_key = api_key
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self.model_name = model_name
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self.base_url = base_url
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self.config = kwargs
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# 验证必要配置
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self._validate_config()
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# 初始化提供商特定设置
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self._initialize()
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@property
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@abstractmethod
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def provider_name(self) -> str:
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"""供应商名称"""
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pass
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@property
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@abstractmethod
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def supported_models(self) -> List[str]:
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"""支持的模型列表"""
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pass
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def _validate_config(self):
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"""验证配置参数"""
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if not self.api_key:
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raise ConfigurationError("API密钥不能为空", "api_key")
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if not self.model_name:
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raise ConfigurationError("模型名称不能为空", "model_name")
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# 检查模型支持情况
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self._validate_model_support()
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def _validate_model_support(self):
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"""验证模型支持情况（宽松模式，仅记录警告）"""
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from loguru import logger
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# OpenAI 兼容网关的模型数量较多，运行时由远端完成最终校验
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if self.model_name not in self.supported_models:
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logger.warning(
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f"模型 {self.model_name} 未在供应商 {self.provider_name} 的预定义支持列表中。"
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f"支持的模型列表: {self.supported_models}"
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)
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def _initialize(self):
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"""初始化提供商特定设置，子类可重写"""
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pass
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@abstractmethod
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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，子类必须实现"""
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pass
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def _handle_api_error(self, status_code: int, response_text: str) -> LLMServiceError:
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"""处理API错误，返回适当的异常"""
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from .exceptions import APICallError, RateLimitError, AuthenticationError
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if status_code == 401:
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return AuthenticationError()
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elif status_code == 429:
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return RateLimitError()
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elif status_code in [502, 503, 504]:
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return APICallError(f"服务器错误 HTTP {status_code}", status_code, response_text)
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elif status_code == 524:
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return APICallError(f"服务器处理超时 HTTP {status_code}", status_code, response_text)
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else:
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return APICallError(f"HTTP {status_code}", status_code, response_text)
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class VisionModelProvider(BaseLLMProvider):
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"""视觉模型提供商基类"""
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@abstractmethod
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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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max_concurrency: int = 1,
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**kwargs) -> List[str]:
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"""
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分析图片并返回结果
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Args:
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images: 图片路径列表或PIL图片对象列表
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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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max_concurrency: 最大并发批次数（实现支持时生效）
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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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pass
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def _prepare_images(self, images: List[Union[str, Path, PIL.Image.Image]]) -> List[PIL.Image.Image]:
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"""预处理图片，统一转换为PIL.Image对象"""
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processed_images = []
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for img in images:
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try:
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if isinstance(img, (str, Path)):
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pil_img = PIL.Image.open(img)
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elif isinstance(img, PIL.Image.Image):
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pil_img = img
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else:
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logger.warning(f"不支持的图片类型: {type(img)}")
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continue
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# 调整图片大小以优化性能
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if pil_img.size[0] > 1024 or pil_img.size[1] > 1024:
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pil_img.thumbnail((1024, 1024), PIL.Image.Resampling.LANCZOS)
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processed_images.append(pil_img)
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except Exception as e:
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logger.error(f"加载图片失败 {img}: {str(e)}")
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continue
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return processed_images
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class TextModelProvider(BaseLLMProvider):
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"""文本生成模型提供商基类"""
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@abstractmethod
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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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生成文本内容
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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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pass
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async def generate_text_stream(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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on_chunk=None,
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**kwargs) -> str:
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"""生成文本内容并尽可能回调流式片段；默认退化为一次性输出。"""
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result = await self.generate_text(
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prompt=prompt,
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system_prompt=system_prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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response_format=response_format,
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**kwargs,
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)
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if on_chunk:
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on_chunk({"type": "content", "text": result})
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return result
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def _build_messages(self, prompt: str, system_prompt: Optional[str] = None) -> List[Dict[str, str]]:
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"""构建消息列表"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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return messages
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