mirror of
https://github.com/linyqh/NarratoAI.git
synced 2026-07-21 05:28:02 +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
430 lines
17 KiB
Python
430 lines
17 KiB
Python
"""
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OpenAI 兼容提供商实现
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使用 OpenAI 官方 SDK 调用 OpenAI 兼容接口，支持文本和视觉模型。
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"""
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import asyncio
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import io
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import base64
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import re
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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import PIL.Image
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from loguru import logger
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from openai import (
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APIError as OpenAIAPIError,
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AsyncOpenAI,
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AuthenticationError as OpenAIAuthError,
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BadRequestError as OpenAIBadRequestError,
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RateLimitError as OpenAIRateLimitError,
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)
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from app.config import config
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from app.config.defaults import DEFAULT_LLM_GENERATION_CONFIG, normalize_openai_compatible_model_name
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from .base import TextModelProvider, VisionModelProvider
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from .exceptions import APICallError, AuthenticationError, ContentFilterError, RateLimitError
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def _normalize_model_name(model_name: str) -> str:
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"""仅剥离误保存的 openai/ 前缀，保留完整模型名称。"""
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return normalize_openai_compatible_model_name(model_name)
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def _is_response_format_error(message: str) -> bool:
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return "response_format" in (message or "").lower()
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def _is_content_filter_error(message: str) -> bool:
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lowered = (message or "").lower()
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return "content_filter" in lowered or "safety" in lowered
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def _clean_json_output(output: str) -> str:
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"""清理 JSON 输出中的 markdown 包裹。"""
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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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return output.strip()
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class _OpenAICompatibleBase:
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"""OpenAI 兼容 provider 共享逻辑。"""
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@property
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def provider_name(self) -> str:
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return "openai"
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@property
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def supported_models(self) -> List[str]:
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# 兼容网关模型数量很多，运行时校验由远端完成。
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return []
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def _validate_model_support(self):
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logger.debug(f"OpenAI 兼容模型已配置: {self.model_name}")
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def _initialize(self):
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# SDK client 按请求参数动态构建，这里无需初始化全局状态。
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pass
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def _generation_config_value(self, model_type: str, param_name: str, override: Any = None) -> Any:
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if override is not None:
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return override
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return config.app.get(
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f"{model_type}_openai_{param_name}",
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DEFAULT_LLM_GENERATION_CONFIG[param_name],
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)
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def _build_chat_completion_options(
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self,
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model_type: str,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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**kwargs,
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) -> Dict[str, Any]:
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"""Build common OpenAI-compatible generation options from config and overrides."""
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options: Dict[str, Any] = {
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"temperature": float(self._generation_config_value(model_type, "temperature", temperature)),
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}
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top_p = float(self._generation_config_value(model_type, "top_p", kwargs.get("top_p")))
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options["top_p"] = top_p
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configured_max_tokens = self._generation_config_value(model_type, "max_tokens", max_tokens)
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if configured_max_tokens is not None and int(configured_max_tokens) > 0:
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options["max_tokens"] = int(configured_max_tokens)
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extra_body: Dict[str, Any] = {}
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thinking_level = str(
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self._generation_config_value(model_type, "thinking_level", kwargs.get("thinking_level")) or "auto"
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)
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if thinking_level in {"low", "medium", "high"}:
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extra_body["reasoning_effort"] = thinking_level
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if extra_body:
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options["extra_body"] = extra_body
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return options
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def _build_client(
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self,
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api_key_override: Optional[str] = None,
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base_url_override: Optional[str] = None,
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timeout_override: Optional[float] = None,
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max_retries_override: Optional[int] = None,
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) -> AsyncOpenAI:
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"""按请求构建 AsyncOpenAI 客户端，支持动态覆盖 api_key / base_url。"""
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api_key = api_key_override or self.api_key
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base_url = base_url_override or self.base_url or None
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timeout_seconds: float = timeout_override or config.app.get("llm_text_timeout", 180)
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max_retries: int = max_retries_override or config.app.get("llm_max_retries", 3)
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return AsyncOpenAI(
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api_key=api_key,
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base_url=base_url,
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timeout=timeout_seconds,
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max_retries=max_retries,
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)
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class OpenAICompatibleVisionProvider(_OpenAICompatibleBase, VisionModelProvider):
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"""OpenAI 兼容视觉模型提供商。"""
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async def analyze_images(
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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,
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) -> List[str]:
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logger.info(f"开始使用 OpenAI 兼容接口 ({self.model_name}) 分析 {len(images)} 张图片")
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processed_images = self._prepare_images(images)
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if not processed_images:
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return []
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bounded_concurrency = max(1, int(max_concurrency))
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semaphore = asyncio.Semaphore(bounded_concurrency)
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batches = [
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(index // batch_size, processed_images[index : index + batch_size])
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for index in range(0, len(processed_images), batch_size)
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]
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async def run_batch(batch_index: int, batch: List[PIL.Image.Image]) -> tuple[int, str]:
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logger.info(f"处理第 {batch_index + 1} 批，共 {len(batch)} 张图片")
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async with semaphore:
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try:
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result = await self._analyze_batch(batch, prompt, **kwargs)
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return batch_index, result
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except Exception as exc:
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logger.error(f"批次 {batch_index + 1} 处理失败: {exc}")
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return batch_index, f"批次处理失败: {exc}"
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completed = await asyncio.gather(*(run_batch(index, batch) for index, batch in batches))
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completed.sort(key=lambda item: item[0])
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return [result for _, result in completed]
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async def _analyze_batch(self, batch: List[PIL.Image.Image], prompt: str, **kwargs) -> str:
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content = [{"type": "text", "text": prompt}]
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for img in batch:
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content.append(
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{self._image_to_base64(img)}"},
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}
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)
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messages = [{"role": "user", "content": content}]
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model_name = _normalize_model_name(self.model_name)
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client = self._build_client(
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api_key_override=kwargs.get("api_key"),
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base_url_override=kwargs.get("api_base"),
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timeout_override=config.app.get("llm_vision_timeout", 120),
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)
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try:
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generation_overrides = dict(kwargs)
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completion_options = self._build_chat_completion_options(
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"vision",
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temperature=generation_overrides.pop("temperature", None),
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max_tokens=generation_overrides.pop("max_tokens", None),
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**generation_overrides,
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)
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response = await client.chat.completions.create(
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model=model_name,
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messages=messages,
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**completion_options,
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)
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if response.choices and response.choices[0].message and response.choices[0].message.content:
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return response.choices[0].message.content
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raise APICallError("OpenAI 兼容接口返回空响应")
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except OpenAIAuthError as exc:
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logger.error(f"OpenAI 兼容接口认证失败: {exc}")
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raise AuthenticationError(str(exc))
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except OpenAIRateLimitError as exc:
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logger.error(f"OpenAI 兼容接口速率限制: {exc}")
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raise RateLimitError(str(exc))
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except OpenAIBadRequestError as exc:
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error_msg = str(exc)
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if _is_content_filter_error(error_msg):
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raise ContentFilterError(f"内容被安全过滤器阻止: {error_msg}")
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raise APICallError(f"请求错误: {error_msg}")
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except OpenAIAPIError as exc:
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logger.error(f"OpenAI 兼容接口 API 错误: {exc}")
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raise APICallError(f"API 错误: {exc}")
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except Exception as exc:
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logger.error(f"OpenAI 兼容接口调用失败: {exc}")
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raise APICallError(f"调用失败: {exc}")
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def _image_to_base64(self, img: PIL.Image.Image) -> str:
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img_buffer = io.BytesIO()
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img.save(img_buffer, format="JPEG", quality=85)
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return base64.b64encode(img_buffer.getvalue()).decode("utf-8")
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async def _make_api_call(self, payload: Dict[str, Any]) -> Dict[str, Any]:
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return payload
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class OpenAICompatibleTextProvider(_OpenAICompatibleBase, TextModelProvider):
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"""OpenAI 兼容文本模型提供商。"""
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def _build_text_completion_kwargs(
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self,
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messages: List[Dict[str, str]],
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temperature: float,
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max_tokens: Optional[int],
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response_format: Optional[str],
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kwargs: Dict[str, Any],
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) -> Dict[str, Any]:
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model_name = _normalize_model_name(self.model_name)
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generation_kwargs = dict(kwargs)
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temperature_override = generation_kwargs.pop("temperature", None)
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if temperature_override is None and temperature != 1.0:
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temperature_override = temperature
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completion_kwargs: Dict[str, Any] = {
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"model": model_name,
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"messages": messages,
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}
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completion_kwargs.update(
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self._build_chat_completion_options(
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"text",
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temperature=temperature_override,
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max_tokens=generation_kwargs.pop("max_tokens", max_tokens),
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**generation_kwargs,
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)
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)
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if response_format == "json":
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completion_kwargs["response_format"] = {"type": "json_object"}
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return completion_kwargs
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@staticmethod
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def _emit_stream_chunk(on_chunk, chunk_type: str, text: str):
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if not on_chunk or not text:
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return
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try:
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on_chunk({"type": chunk_type, "text": text})
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except Exception as exc:
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logger.debug(f"流式回调更新失败: {exc}")
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@staticmethod
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def _extract_reasoning_delta(delta: Any) -> str:
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if delta is None:
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return ""
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if hasattr(delta, "reasoning_content"):
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value = getattr(delta, "reasoning_content")
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if value:
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return str(value)
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if hasattr(delta, "model_dump"):
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data = delta.model_dump(exclude_none=True)
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for key in ("reasoning_content", "reasoning", "thinking"):
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value = data.get(key)
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if value:
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return str(value)
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return ""
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async def generate_text(
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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,
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) -> str:
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messages = self._build_messages(prompt, system_prompt)
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client = self._build_client(
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api_key_override=kwargs.get("api_key"),
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base_url_override=kwargs.get("api_base"),
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timeout_override=config.app.get("llm_text_timeout", 180),
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)
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completion_kwargs = self._build_text_completion_kwargs(
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messages,
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temperature,
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max_tokens,
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response_format,
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kwargs,
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)
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try:
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response = await client.chat.completions.create(**completion_kwargs)
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if response.choices and response.choices[0].message and response.choices[0].message.content:
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return response.choices[0].message.content
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raise APICallError("OpenAI 兼容接口返回空响应")
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except OpenAIBadRequestError as exc:
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error_msg = str(exc)
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# 某些网关不支持 response_format，回退到提示词约束模式
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if response_format == "json" and _is_response_format_error(error_msg):
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logger.warning("目标网关不支持 response_format，回退为提示词约束 JSON 输出")
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completion_kwargs.pop("response_format", None)
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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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retry_response = await client.chat.completions.create(**completion_kwargs)
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if retry_response.choices and retry_response.choices[0].message and retry_response.choices[0].message.content:
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return _clean_json_output(retry_response.choices[0].message.content)
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raise APICallError("OpenAI 兼容接口返回空响应")
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if _is_content_filter_error(error_msg):
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raise ContentFilterError(f"内容被安全过滤器阻止: {error_msg}")
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raise APICallError(f"请求错误: {error_msg}")
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except OpenAIAuthError as exc:
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logger.error(f"OpenAI 兼容接口认证失败: {exc}")
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raise AuthenticationError(str(exc))
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except OpenAIRateLimitError as exc:
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logger.error(f"OpenAI 兼容接口速率限制: {exc}")
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raise RateLimitError(str(exc))
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except OpenAIAPIError as exc:
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logger.error(f"OpenAI 兼容接口 API 错误: {exc}")
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raise APICallError(f"API 错误: {exc}")
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except Exception as exc:
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logger.error(f"OpenAI 兼容接口调用失败: {exc}")
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raise APICallError(f"调用失败: {exc}")
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async def generate_text_stream(
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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,
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) -> str:
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messages = self._build_messages(prompt, system_prompt)
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client = self._build_client(
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api_key_override=kwargs.get("api_key"),
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base_url_override=kwargs.get("api_base"),
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timeout_override=config.app.get("llm_text_timeout", 180),
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)
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completion_kwargs = self._build_text_completion_kwargs(
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messages,
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temperature,
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max_tokens,
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response_format,
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kwargs,
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)
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completion_kwargs["stream"] = True
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async def collect_stream() -> str:
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content_parts: List[str] = []
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stream = await client.chat.completions.create(**completion_kwargs)
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async for chunk in stream:
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if not getattr(chunk, "choices", None):
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continue
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delta = chunk.choices[0].delta
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reasoning_delta = self._extract_reasoning_delta(delta)
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if reasoning_delta:
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self._emit_stream_chunk(on_chunk, "reasoning", reasoning_delta)
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content_delta = getattr(delta, "content", None) if delta is not None else None
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if content_delta:
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content_parts.append(content_delta)
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self._emit_stream_chunk(on_chunk, "content", content_delta)
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result = "".join(content_parts).strip()
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if result:
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self._emit_stream_chunk(on_chunk, "done", "")
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return result
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raise APICallError("OpenAI 兼容接口返回空响应")
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try:
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return await collect_stream()
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except OpenAIBadRequestError as exc:
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error_msg = str(exc)
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if response_format == "json" and _is_response_format_error(error_msg):
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logger.warning("目标网关不支持流式 response_format，回退为提示词约束 JSON 输出")
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completion_kwargs.pop("response_format", None)
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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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result = await collect_stream()
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return _clean_json_output(result)
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if _is_content_filter_error(error_msg):
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raise ContentFilterError(f"内容被安全过滤器阻止: {error_msg}")
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raise APICallError(f"请求错误: {error_msg}")
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except OpenAIAuthError as exc:
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logger.error(f"OpenAI 兼容接口认证失败: {exc}")
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raise AuthenticationError(str(exc))
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except OpenAIRateLimitError as exc:
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logger.error(f"OpenAI 兼容接口速率限制: {exc}")
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raise RateLimitError(str(exc))
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except OpenAIAPIError as exc:
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logger.error(f"OpenAI 兼容接口 API 错误: {exc}")
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raise APICallError(f"API 错误: {exc}")
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except Exception as exc:
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logger.error(f"OpenAI 兼容接口流式调用失败: {exc}")
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raise APICallError(f"流式调用失败: {exc}")
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async def _make_api_call(self, payload: Dict[str, Any]) -> Dict[str, Any]:
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return payload
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