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https://github.com/linyqh/NarratoAI.git
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d10c2ff7c5
- 新增影视解说专属提示词模块，覆盖剧情分析、文案生成、片段规划、脚本匹配与修复全流程 - 注册影视解说模块到全局提示词系统，更新初始化加载逻辑 - 重构Tavily搜索服务，拆分通用搜索函数适配短剧和影视两类作品 - 更新WebUI界面，新增影视解说配置项、多语言翻译与版本号展示 - 升级项目版本号从0.7.9到0.8.1 - 调整LLM服务与适配器逻辑，支持自定义prompt分类适配不同解说类型 - 完善相关工具类与单元测试，覆盖影视解说场景调用流程
304 lines
11 KiB
Python
304 lines
11 KiB
Python
"""
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统一的大模型服务接口
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提供简化的API接口，方便现有代码迁移到新的架构
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"""
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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 .manager import LLMServiceManager
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from .validators import OutputValidator
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from .exceptions import LLMServiceError
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from app.services.prompts import PromptManager
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# 提供商注册由 webui.py:main() 显式调用（见 LLM 提供商注册机制重构）
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# 这样更可靠，错误也更容易调试
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class UnifiedLLMService:
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"""统一的大模型服务接口"""
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@staticmethod
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async def analyze_images(images: List[Union[str, Path, PIL.Image.Image]],
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prompt: str,
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provider: Optional[str] = None,
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batch_size: int = 10,
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**kwargs) -> List[str]:
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"""
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分析图片内容
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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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provider: 视觉模型提供商名称，如果不指定则使用配置中的默认值
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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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Raises:
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LLMServiceError: 服务调用失败时抛出
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"""
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try:
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# 获取视觉模型提供商
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vision_provider = LLMServiceManager.get_vision_provider(provider)
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# 执行图片分析
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results = await vision_provider.analyze_images(
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images=images,
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prompt=prompt,
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batch_size=batch_size,
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**kwargs
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)
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logger.info(f"图片分析完成，共处理 {len(images)} 张图片，生成 {len(results)} 个结果")
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return results
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except Exception as e:
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logger.error(f"图片分析失败: {str(e)}")
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raise LLMServiceError(f"图片分析失败: {str(e)}")
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@staticmethod
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async def generate_text(prompt: str,
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system_prompt: Optional[str] = None,
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provider: 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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provider: 文本模型提供商名称，如果不指定则使用配置中的默认值
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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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Raises:
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LLMServiceError: 服务调用失败时抛出
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"""
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try:
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# 获取文本模型提供商
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text_provider = LLMServiceManager.get_text_provider(provider)
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# 执行文本生成
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result = await text_provider.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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logger.info(f"文本生成完成，生成内容长度: {len(result)} 字符")
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return result
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except Exception as e:
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logger.error(f"文本生成失败: {str(e)}")
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raise LLMServiceError(f"文本生成失败: {str(e)}")
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@staticmethod
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async def generate_text_stream(prompt: str,
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system_prompt: Optional[str] = None,
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provider: 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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"""
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流式生成文本内容；不支持流式的 provider 会退化为一次性返回。
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"""
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try:
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text_provider = LLMServiceManager.get_text_provider(provider)
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result = await text_provider.generate_text_stream(
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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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on_chunk=on_chunk,
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**kwargs
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)
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logger.info(f"流式文本生成完成，生成内容长度: {len(result)} 字符")
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return result
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except Exception as e:
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logger.error(f"流式文本生成失败: {str(e)}")
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raise LLMServiceError(f"流式文本生成失败: {str(e)}")
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@staticmethod
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async def generate_narration_script(prompt: str,
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provider: Optional[str] = None,
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temperature: float = 1.0,
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validate_output: bool = True,
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**kwargs) -> List[Dict[str, Any]]:
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"""
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生成解说文案
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Args:
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prompt: 提示词
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provider: 文本模型提供商名称
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temperature: 生成温度
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validate_output: 是否验证输出格式
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**kwargs: 其他参数
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Returns:
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解说文案列表
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Raises:
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LLMServiceError: 服务调用失败时抛出
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"""
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try:
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# 生成文本
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result = await UnifiedLLMService.generate_text(
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prompt=prompt,
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provider=provider,
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temperature=temperature,
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response_format="json",
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**kwargs
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)
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# 验证输出格式
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if validate_output:
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narration_items = OutputValidator.validate_narration_script(result)
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logger.info(f"解说文案生成并验证完成，共 {len(narration_items)} 个片段")
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return narration_items
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else:
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# 简单的JSON解析
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import json
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parsed_result = json.loads(result)
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if "items" in parsed_result:
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return parsed_result["items"]
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else:
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return parsed_result
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except Exception as e:
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logger.error(f"解说文案生成失败: {str(e)}")
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raise LLMServiceError(f"解说文案生成失败: {str(e)}")
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@staticmethod
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async def analyze_subtitle(subtitle_content: str,
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provider: Optional[str] = None,
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temperature: float = 1.0,
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prompt_category: str = "short_drama_narration",
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validate_output: bool = True,
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**kwargs) -> str:
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"""
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分析字幕内容
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Args:
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subtitle_content: 字幕内容
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provider: 文本模型提供商名称
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temperature: 生成温度
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validate_output: 是否验证输出格式
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**kwargs: 其他参数
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Returns:
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分析结果
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Raises:
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LLMServiceError: 服务调用失败时抛出
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"""
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try:
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prompt = PromptManager.get_prompt(
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category=prompt_category,
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name="plot_analysis",
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parameters={"subtitle_content": subtitle_content},
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)
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prompt_object = PromptManager.get_prompt_object(
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category=prompt_category,
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name="plot_analysis",
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)
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system_prompt = prompt_object.get_system_prompt()
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# 生成分析结果
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result = await UnifiedLLMService.generate_text(
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prompt=prompt,
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system_prompt=system_prompt,
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provider=provider,
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temperature=temperature,
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**kwargs
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)
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# 验证输出格式
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if validate_output:
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validated_result = OutputValidator.validate_subtitle_analysis(result)
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logger.info("字幕分析完成并验证通过")
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return validated_result
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else:
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return result
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except Exception as e:
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logger.error(f"字幕分析失败: {str(e)}")
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raise LLMServiceError(f"字幕分析失败: {str(e)}")
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@staticmethod
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def get_provider_info() -> Dict[str, Any]:
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"""
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获取所有提供商信息
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Returns:
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提供商信息字典
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"""
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return LLMServiceManager.get_provider_info()
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@staticmethod
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def list_vision_providers() -> List[str]:
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"""
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列出所有视觉模型提供商
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Returns:
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提供商名称列表
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"""
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return LLMServiceManager.list_vision_providers()
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@staticmethod
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def list_text_providers() -> List[str]:
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"""
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列出所有文本模型提供商
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Returns:
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提供商名称列表
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"""
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return LLMServiceManager.list_text_providers()
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@staticmethod
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def clear_cache():
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"""清空提供商实例缓存"""
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LLMServiceManager.clear_cache()
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logger.info("已清空大模型服务缓存")
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# 为了向后兼容，提供一些便捷函数
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async def analyze_images_unified(images: List[Union[str, Path, PIL.Image.Image]],
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prompt: str,
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provider: Optional[str] = None,
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batch_size: int = 10) -> List[str]:
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"""便捷的图片分析函数"""
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return await UnifiedLLMService.analyze_images(images, prompt, provider, batch_size)
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async def generate_text_unified(prompt: str,
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system_prompt: Optional[str] = None,
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provider: Optional[str] = None,
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temperature: float = 1.0,
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response_format: Optional[str] = None) -> str:
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"""便捷的文本生成函数"""
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return await UnifiedLLMService.generate_text(
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prompt, system_prompt, provider, temperature, response_format=response_format
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)
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