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将日志系统从 app/logger/ 移至 app/log/ 目录 将路由配置从 routers.py 重命名为 routes.py 将硬编码配置值移至 constants.py 中的默认常量 统一代码格式和导入排序 优化函数参数对齐方式
149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
# app/services/chat/stream_optimizer.py
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import asyncio
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import math
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from typing import Any, AsyncGenerator, Callable, List
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from app.config.config import settings
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from app.core.constants import (
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DEFAULT_STREAM_CHUNK_SIZE,
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DEFAULT_STREAM_LONG_TEXT_THRESHOLD,
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DEFAULT_STREAM_MAX_DELAY,
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DEFAULT_STREAM_MIN_DELAY,
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DEFAULT_STREAM_SHORT_TEXT_THRESHOLD,
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)
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from app.log.logger import get_gemini_logger, get_openai_logger
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logger_openai = get_openai_logger()
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logger_gemini = get_gemini_logger()
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class StreamOptimizer:
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"""流式输出优化器
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提供流式输出优化功能,包括智能延迟调整和长文本分块输出。
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"""
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def __init__(
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self,
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logger=None,
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min_delay: float = DEFAULT_STREAM_MIN_DELAY,
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max_delay: float = DEFAULT_STREAM_MAX_DELAY,
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short_text_threshold: int = DEFAULT_STREAM_SHORT_TEXT_THRESHOLD,
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long_text_threshold: int = DEFAULT_STREAM_LONG_TEXT_THRESHOLD,
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chunk_size: int = DEFAULT_STREAM_CHUNK_SIZE,
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):
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"""初始化流式输出优化器
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参数:
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logger: 日志记录器
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min_delay: 最小延迟时间(秒)
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max_delay: 最大延迟时间(秒)
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short_text_threshold: 短文本阈值(字符数)
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long_text_threshold: 长文本阈值(字符数)
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chunk_size: 长文本分块大小(字符数)
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"""
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self.logger = logger
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self.min_delay = min_delay
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self.max_delay = max_delay
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self.short_text_threshold = short_text_threshold
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self.long_text_threshold = long_text_threshold
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self.chunk_size = chunk_size
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def calculate_delay(self, text_length: int) -> float:
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"""根据文本长度计算延迟时间
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参数:
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text_length: 文本长度
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返回:
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延迟时间(秒)
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"""
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if text_length <= self.short_text_threshold:
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# 短文本使用较大延迟
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return self.max_delay
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elif text_length >= self.long_text_threshold:
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# 长文本使用较小延迟
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return self.min_delay
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else:
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# 中等长度文本使用线性插值计算延迟
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# 使用对数函数使延迟变化更平滑
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ratio = math.log(text_length / self.short_text_threshold) / math.log(
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self.long_text_threshold / self.short_text_threshold
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)
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return self.max_delay - ratio * (self.max_delay - self.min_delay)
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def split_text_into_chunks(self, text: str) -> List[str]:
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"""将文本分割成小块
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参数:
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text: 要分割的文本
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返回:
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文本块列表
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"""
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return [
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text[i : i + self.chunk_size] for i in range(0, len(text), self.chunk_size)
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]
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async def optimize_stream_output(
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self,
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text: str,
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create_response_chunk: Callable[[str], Any],
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format_chunk: Callable[[Any], str],
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) -> AsyncGenerator[str, None]:
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"""优化流式输出
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参数:
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text: 要输出的文本
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create_response_chunk: 创建响应块的函数,接收文本,返回响应块
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format_chunk: 格式化响应块的函数,接收响应块,返回格式化后的字符串
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返回:
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异步生成器,生成格式化后的响应块
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"""
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if not text:
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return
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# 计算智能延迟时间
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delay = self.calculate_delay(len(text))
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if self.logger:
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self.logger.info(f"Text length: {len(text)}, delay: {delay:.4f}s")
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# 根据文本长度决定输出方式
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if len(text) >= self.long_text_threshold:
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# 长文本:分块输出
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chunks = self.split_text_into_chunks(text)
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if self.logger:
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self.logger.info(f"Long text: splitting into {len(chunks)} chunks")
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for chunk_text in chunks:
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chunk_response = create_response_chunk(chunk_text)
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yield format_chunk(chunk_response)
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await asyncio.sleep(delay)
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else:
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# 短文本:逐字符输出
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for char in text:
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char_chunk = create_response_chunk(char)
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yield format_chunk(char_chunk)
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await asyncio.sleep(delay)
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# 创建默认的优化器实例,可以直接导入使用
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openai_optimizer = StreamOptimizer(
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logger=logger_openai,
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min_delay=settings.STREAM_MIN_DELAY,
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max_delay=settings.STREAM_MAX_DELAY,
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short_text_threshold=settings.STREAM_SHORT_TEXT_THRESHOLD,
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long_text_threshold=settings.STREAM_LONG_TEXT_THRESHOLD,
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chunk_size=settings.STREAM_CHUNK_SIZE,
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)
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gemini_optimizer = StreamOptimizer(
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logger=logger_gemini,
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min_delay=settings.STREAM_MIN_DELAY,
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max_delay=settings.STREAM_MAX_DELAY,
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short_text_threshold=settings.STREAM_SHORT_TEXT_THRESHOLD,
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long_text_threshold=settings.STREAM_LONG_TEXT_THRESHOLD,
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chunk_size=settings.STREAM_CHUNK_SIZE,
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)
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