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yangdx
@@ -541,13 +541,13 @@ def create_app(args):
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async def stream_generator():
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if isinstance(response, str):
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# 如果是字符串，一次性发送
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# If it's a string, send it all at once
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yield f"{json.dumps({'response': response})}\n"
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else:
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# 如果是异步生成器，逐块发送
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# If it's an async generator, send chunks one by one
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try:
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async for chunk in response:
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if chunk: # 只发送非空内容
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if chunk: # Only send non-empty content
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yield f"{json.dumps({'response': chunk})}\n"
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except Exception as e:
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logging.error(f"Streaming error: {str(e)}")
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@@ -563,7 +563,7 @@ def create_app(args):
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "POST, OPTIONS",
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"Access-Control-Allow-Headers": "Content-Type",
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"X-Accel-Buffering": "no", # 禁用 Nginx 缓冲
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"X-Accel-Buffering": "no", # Disable Nginx buffering
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},
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)
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except Exception as e:
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@@ -760,7 +760,6 @@ def create_app(args):
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last_chunk_time = first_chunk_time
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total_response = response
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# 第一次发送查询内容
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data = {
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"model": LIGHTRAG_MODEL,
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"created_at": LIGHTRAG_CREATED_AT,
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@@ -773,41 +772,35 @@ def create_app(args):
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}
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yield f"{json.dumps(data, ensure_ascii=False)}\n"
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# 计算各项指标
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completion_tokens = estimate_tokens(total_response)
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total_time = last_chunk_time - start_time # 总时间
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total_time = last_chunk_time - start_time
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prompt_eval_time = (
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first_chunk_time - start_time
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) # 首个响应之前的时间
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)
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eval_time = (
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last_chunk_time - first_chunk_time
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) # 生成响应的时间
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)
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# 第二次发送统计信息
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data = {
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"model": LIGHTRAG_MODEL,
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"created_at": LIGHTRAG_CREATED_AT,
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"done": True,
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"total_duration": total_time, # 总时间
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"load_duration": 0, # 加载时间为0
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"prompt_eval_count": prompt_tokens, # 输入token数
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"prompt_eval_duration": prompt_eval_time, # 首个响应之前的时间
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"eval_count": completion_tokens, # 输出token数
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"eval_duration": eval_time, # 生成响应的时间
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"total_duration": total_time,
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"load_duration": 0,
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"prompt_eval_count": prompt_tokens,
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"prompt_eval_duration": prompt_eval_time,
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"eval_count": completion_tokens,
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"eval_duration": eval_time,
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}
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yield f"{json.dumps(data, ensure_ascii=False)}\n"
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else:
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# 流式响应
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async for chunk in response:
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if chunk: # 只发送非空内容
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# 记录第一个chunk的时间
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if chunk:
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if first_chunk_time is None:
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first_chunk_time = time.time_ns()
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# 更新最后一个chunk的时间
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last_chunk_time = time.time_ns()
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# 累积响应内容
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total_response += chunk
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data = {
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"model": LIGHTRAG_MODEL,
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@@ -821,30 +814,28 @@ def create_app(args):
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}
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yield f"{json.dumps(data, ensure_ascii=False)}\n"
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# 计算各项指标
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completion_tokens = estimate_tokens(total_response)
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total_time = last_chunk_time - start_time # 总时间
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total_time = last_chunk_time - start_time
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prompt_eval_time = (
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first_chunk_time - start_time
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) # 首个响应之前的时间
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)
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eval_time = (
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last_chunk_time - first_chunk_time
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) # 生成响应的时间
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)
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# 发送完成标记，包含性能统计信息
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data = {
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"model": LIGHTRAG_MODEL,
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"created_at": LIGHTRAG_CREATED_AT,
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"done": True,
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"total_duration": total_time, # 总时间
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"load_duration": 0, # 加载时间为0
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"prompt_eval_count": prompt_tokens, # 输入token数
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"prompt_eval_duration": prompt_eval_time, # 首个响应之前的时间
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"eval_count": completion_tokens, # 输出token数
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"eval_duration": eval_time, # 生成响应的时间
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"total_duration": total_time,
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"load_duration": 0,
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"prompt_eval_count": prompt_tokens,
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"prompt_eval_duration": prompt_eval_time,
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"eval_count": completion_tokens,
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"eval_duration": eval_time,
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}
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yield f"{json.dumps(data, ensure_ascii=False)}\n"
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return # 确保生成器在发送完成标记后立即结束
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return # Ensure the generator ends immediately after sending the completion marker
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except Exception as e:
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logging.error(f"Error in stream_generator: {str(e)}")
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raise
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@@ -862,37 +853,33 @@ def create_app(args):
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},
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)
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else:
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# 非流式响应
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first_chunk_time = time.time_ns()
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response_text = await rag.aquery(cleaned_query, param=query_param)
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last_chunk_time = time.time_ns()
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# 确保响应不为空
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if not response_text:
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response_text = "No response generated"
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# 计算各项指标
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completion_tokens = estimate_tokens(str(response_text))
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total_time = last_chunk_time - start_time # 总时间
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prompt_eval_time = first_chunk_time - start_time # 首个响应之前的时间
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eval_time = last_chunk_time - first_chunk_time # 生成响应的时间
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total_time = last_chunk_time - start_time
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prompt_eval_time = first_chunk_time - start_time
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eval_time = last_chunk_time - first_chunk_time
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# 构造响应，包含性能统计信息
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return {
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"model": LIGHTRAG_MODEL,
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"created_at": LIGHTRAG_CREATED_AT,
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"message": {
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"role": "assistant",
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"content": str(response_text), # 确保转换为字符串
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"content": str(response_text),
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"images": None,
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},
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"done": True,
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"total_duration": total_time, # 总时间
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"load_duration": 0, # 加载时间为0
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"prompt_eval_count": prompt_tokens, # 输入token数
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"prompt_eval_duration": prompt_eval_time, # 首个响应之前的时间
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"eval_count": completion_tokens, # 输出token数
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"eval_duration": eval_time, # 生成响应的时间
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"total_duration": total_time,
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"load_duration": 0,
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"prompt_eval_count": prompt_tokens,
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"prompt_eval_duration": prompt_eval_time,
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"eval_count": completion_tokens,
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"eval_duration": eval_time,
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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