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https://github.com/Zippland/Snap-Solver.git
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debug deepseek
This commit is contained in:
Zylan
@@ -94,55 +94,38 @@ class DeepSeekModel(BaseModel):
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print("无法打印chunk")
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try:
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# 同时处理两种不同的内容，确保正确区分思考内容和最终内容
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delta = chunk.choices[0].delta
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# 处理推理模型的思考内容
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if hasattr(chunk.choices[0].delta, 'reasoning_content'):
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content = chunk.choices[0].delta.reasoning_content
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if content:
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# 累积思考内容
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thinking_buffer += content
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# 只在积累一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 20 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "thinking",
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"content": thinking_buffer
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}
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# 处理常规内容
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elif hasattr(chunk.choices[0].delta, 'content'):
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content = chunk.choices[0].delta.content
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if content:
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# 累积响应内容
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response_buffer += content
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print(f"累积响应内容: '{content}', 当前buffer: '{response_buffer}'")
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# 只在积累一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 尝试直接从message内容获取
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elif hasattr(chunk.choices[0], 'message') and hasattr(chunk.choices[0].message, 'content'):
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content = chunk.choices[0].message.content
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if content:
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response_buffer += content
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print(f"从message获取内容: '{content}'")
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 检查是否有finish_reason，表示生成结束
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elif hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason:
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print(f"生成结束，原因: {chunk.choices[0].finish_reason}")
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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content = delta.reasoning_content
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thinking_buffer += content
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# 如果没有内容但有思考内容，把思考内容作为正文显示
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if not response_buffer and thinking_buffer:
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response_buffer = thinking_buffer
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# 发送思考内容更新
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if len(content) >= 20 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "thinking",
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"content": thinking_buffer
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}
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# 处理最终结果内容 - 即使在推理模型中也会有content字段
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if hasattr(delta, 'content') and delta.content:
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content = delta.content
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response_buffer += content
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print(f"累积响应内容: '{content}', 当前buffer: '{response_buffer}'")
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# 发送结果内容更新
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 处理消息结束
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if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason:
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print(f"生成结束，原因: {chunk.choices[0].finish_reason}")
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# 注意：不要在这里把思考内容作为正文，因为这可能导致重复内容
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except Exception as e:
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print(f"解析响应chunk时出错: {str(e)}")
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continue
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@@ -154,23 +137,26 @@ class DeepSeekModel(BaseModel):
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"content": thinking_buffer
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}
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# 如果推理完成后没有正文内容，则使用思考内容作为最终响应
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if not response_buffer and thinking_buffer:
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response_buffer = thinking_buffer
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# 发送最终响应内容
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if response_buffer:
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yield {
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"status": "streaming",
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"status": "completed",
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"content": response_buffer
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}
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# 发送完成状态
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yield {
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"status": "completed",
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"content": response_buffer
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}
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# 如果没有正常的响应内容，但有思考内容，则将思考内容作为最终结果
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elif thinking_buffer:
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yield {
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"status": "completed",
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"content": thinking_buffer
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}
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else:
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# 如果两者都没有，返回一个空结果
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yield {
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"status": "completed",
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"content": "没有获取到内容"
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}
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except Exception as e:
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error_msg = str(e)
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print(f"DeepSeek API调用出错: {error_msg}")
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@@ -284,54 +270,37 @@ class DeepSeekModel(BaseModel):
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print("无法打印chunk")
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try:
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# 同时处理两种不同的内容，确保正确区分思考内容和最终内容
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delta = chunk.choices[0].delta
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# 处理推理模型的思考内容
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if hasattr(chunk.choices[0].delta, 'reasoning_content'):
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content = chunk.choices[0].delta.reasoning_content
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if content:
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# 累积思考内容
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thinking_buffer += content
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# 只在积累一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 20 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "thinking",
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"content": thinking_buffer
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}
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# 处理常规内容
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elif hasattr(chunk.choices[0].delta, 'content'):
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content = chunk.choices[0].delta.content
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if content:
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# 累积响应内容
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response_buffer += content
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print(f"累积图像响应内容: '{content}', 当前buffer: '{response_buffer}'")
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# 只在积累一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 尝试直接从message内容获取
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elif hasattr(chunk.choices[0], 'message') and hasattr(chunk.choices[0].message, 'content'):
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content = chunk.choices[0].message.content
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if content:
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response_buffer += content
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print(f"从message获取图像内容: '{content}'")
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 检查是否有finish_reason，表示生成结束
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elif hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason:
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print(f"图像生成结束，原因: {chunk.choices[0].finish_reason}")
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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content = delta.reasoning_content
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thinking_buffer += content
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# 如果没有内容但有思考内容，把思考内容作为正文显示
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if not response_buffer and thinking_buffer:
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response_buffer = thinking_buffer
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# 发送思考内容更新
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if len(content) >= 20 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "thinking",
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"content": thinking_buffer
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}
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# 处理最终结果内容 - 即使在推理模型中也会有content字段
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if hasattr(delta, 'content') and delta.content:
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content = delta.content
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response_buffer += content
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print(f"累积图像响应内容: '{content}', 当前buffer: '{response_buffer}'")
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# 发送结果内容更新
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 处理消息结束
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if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason:
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print(f"图像生成结束，原因: {chunk.choices[0].finish_reason}")
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except Exception as e:
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print(f"解析图像响应chunk时出错: {str(e)}")
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continue
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@@ -343,22 +312,12 @@ class DeepSeekModel(BaseModel):
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"content": thinking_buffer
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}
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# 如果推理完成后没有正文内容，则使用思考内容作为最终响应
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if not response_buffer and thinking_buffer:
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response_buffer = thinking_buffer
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# 发送最终响应内容
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if response_buffer:
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yield {
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"status": "streaming",
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"status": "completed",
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"content": response_buffer
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}
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# 发送完成状态
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yield {
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"status": "completed",
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"content": response_buffer
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}
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except Exception as e:
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error_msg = str(e)
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