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| """DeepSeek API 客户端""" | |
| import json | |
| from typing import AsyncGenerator | |
| from app.utils.logger import logger | |
| from .base_client import BaseClient | |
| class DeepSeekClient(BaseClient): | |
| def __init__(self, api_key: str, api_url: str = "https://api.siliconflow.cn/v1/chat/completions", provider: str = "deepseek"): | |
| """初始化 DeepSeek 客户端 | |
| Args: | |
| api_key: DeepSeek API密钥 | |
| api_url: DeepSeek API地址 | |
| """ | |
| super().__init__(api_key, api_url) | |
| self.provider = provider | |
| def _process_think_tag_content(self, content: str) -> tuple[bool, str]: | |
| """处理包含 think 标签的内容 | |
| Args: | |
| content: 需要处理的内容字符串 | |
| Returns: | |
| tuple[bool, str]: | |
| bool: 是否检测到完整的 think 标签对 | |
| str: 处理后的内容 | |
| """ | |
| has_start = "<think>" in content | |
| has_end = "</think>" in content | |
| if has_start and has_end: | |
| return True, content | |
| elif has_start: | |
| return False, content | |
| elif not has_start and not has_end: | |
| return False, content | |
| else: | |
| return True, content | |
| async def stream_chat(self, messages: list, model: str = "deepseek-ai/DeepSeek-R1", is_origin_reasoning: bool = True) -> AsyncGenerator[tuple[str, str], None]: | |
| """流式对话 | |
| Args: | |
| messages: 消息列表 | |
| model: 模型名称 | |
| Yields: | |
| tuple[str, str]: (内容类型, 内容) | |
| 内容类型: "reasoning" 或 "content" | |
| 内容: 实际的文本内容 | |
| """ | |
| headers = { | |
| "Authorization": f"Bearer {self.api_key}", | |
| "Content-Type": "application/json", | |
| "Accept": "text/event-stream", | |
| } | |
| data = { | |
| "model": model, | |
| "messages": messages, | |
| "stream": True, | |
| } | |
| logger.debug(f"开始流式对话：{data}") | |
| accumulated_content = "" | |
| is_collecting_think = False | |
| async for chunk in self._make_request(headers, data): | |
| chunk_str = chunk.decode('utf-8') | |
| try: | |
| lines = chunk_str.splitlines() | |
| for line in lines: | |
| if line.startswith("data: "): | |
| json_str = line[len("data: "):] | |
| if json_str == "[DONE]": | |
| return | |
| data = json.loads(json_str) | |
| if data and data.get("choices") and data["choices"][0].get("delta"): | |
| delta = data["choices"][0]["delta"] | |
| if is_origin_reasoning: | |
| # 处理 reasoning_content | |
| if delta.get("reasoning_content"): | |
| content = delta["reasoning_content"] | |
| logger.debug(f"提取推理内容：{content}") | |
| yield "reasoning", content | |
| if delta.get("reasoning_content") is None and delta.get("content"): | |
| content = delta["content"] | |
| logger.info(f"提取内容信息，推理阶段结束: {content}") | |
| yield "content", content | |
| else: | |
| # 处理其他模型的输出 | |
| if delta.get("content"): | |
| content = delta["content"] | |
| if content == "": # 只跳过完全空的字符串 | |
| continue | |
| logger.debug(f"非原生推理内容：{content}") | |
| accumulated_content += content | |
| # 检查累积的内容是否包含完整的 think 标签对 | |
| is_complete, processed_content = self._process_think_tag_content(accumulated_content) | |
| if "<think>" in content and not is_collecting_think: | |
| # 开始收集推理内容 | |
| logger.debug(f"开始收集推理内容：{content}") | |
| is_collecting_think = True | |
| yield "reasoning", content | |
| elif is_collecting_think: | |
| if "</think>" in content: | |
| # 推理内容结束 | |
| logger.debug(f"推理内容结束：{content}") | |
| is_collecting_think = False | |
| yield "reasoning", content | |
| # 输出空的 content 来触发 Claude 处理 | |
| yield "content", "" | |
| # 重置累积内容 | |
| accumulated_content = "" | |
| else: | |
| # 继续收集推理内容 | |
| yield "reasoning", content | |
| else: | |
| # 普通内容 | |
| yield "content", content | |
| except json.JSONDecodeError as e: | |
| logger.error(f"JSON 解析错误: {e}") | |
| except Exception as e: | |
| logger.error(f"处理 chunk 时发生错误: {e}") | |