118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
# omni_client.py
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# -*- coding: utf-8 -*-
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import os, base64, asyncio, threading
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from typing import AsyncGenerator, Dict, Any, List, Optional, Tuple
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from openai import OpenAI
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# ===== OpenAI 兼容（达摩院 DashScope 兼容模式）=====
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API_KEY = os.getenv("DASHSCOPE_API_KEY", "sk-a9440db694924559ae4ebdc2023d2b9a")
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if not API_KEY:
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raise RuntimeError("未设置 DASHSCOPE_API_KEY")
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QWEN_MODEL = "qwen-omni-turbo"
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# 兼容模式
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oai_client = OpenAI(
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api_key=API_KEY,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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)
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class OmniStreamPiece:
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"""对外的统一增量数据：text/audio 二选一或同时。"""
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def __init__(self, text_delta: Optional[str] = None, audio_b64: Optional[str] = None):
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self.text_delta = text_delta
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self.audio_b64 = audio_b64
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async def stream_chat(
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content_list: List[Dict[str, Any]],
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voice: str = "Cherry",
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audio_format: str = "wav",
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) -> AsyncGenerator[OmniStreamPiece, None]:
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"""
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发起一轮 Omni-Turbo ChatCompletions 流式对话：
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- content_list: OpenAI chat 的 content，多模态（image_url/text）
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- 以 stream=True 返回
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- 增量产出：OmniStreamPiece(text_delta=?, audio_b64=?)
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Day 13 修复：使用队列+线程解耦同步 API 调用，避免阻塞事件循环
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"""
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# 使用 asyncio.Queue 在线程和异步之间传递数据
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queue: asyncio.Queue = asyncio.Queue()
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loop = asyncio.get_running_loop()
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def _sync_stream():
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"""在独立线程中运行同步 API 调用"""
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try:
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# Day 21 优化：添加 system prompt 让 AI 回答简洁
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# 导盲眼镜场景需要快速、简短的回答
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system_prompt = """你是一个视障辅助AI助手，安装在智能导盲眼镜上。
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请用极简短的语言回答，每次回答不超过2-3句话。
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避免冗长解释，只提供最关键的信息。
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语气友好但简洁。"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": content_list}
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]
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completion = oai_client.chat.completions.create(
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model=QWEN_MODEL,
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messages=messages,
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modalities=["text", "audio"],
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audio={"voice": voice, "format": audio_format},
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stream=True,
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stream_options={"include_usage": True},
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)
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for chunk in completion:
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text_delta: Optional[str] = None
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audio_b64: Optional[str] = None
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if getattr(chunk, "choices", None):
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c0 = chunk.choices[0]
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delta = getattr(c0, "delta", None)
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# 文本增量
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if delta and getattr(delta, "content", None):
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piece = delta.content
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if piece:
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text_delta = piece
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# 音频分片
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if delta and getattr(delta, "audio", None):
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aud = delta.audio
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audio_b64 = aud.get("data") if isinstance(aud, dict) else getattr(aud, "data", None)
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if audio_b64 is None:
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msg = getattr(c0, "message", None)
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if msg and getattr(msg, "audio", None):
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ma = msg.audio
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audio_b64 = ma.get("data") if isinstance(ma, dict) else getattr(ma, "data", None)
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if (text_delta is not None) or (audio_b64 is not None):
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# 线程安全地放入队列
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loop.call_soon_threadsafe(
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queue.put_nowait,
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OmniStreamPiece(text_delta=text_delta, audio_b64=audio_b64)
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)
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except Exception as e:
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# 发生异常时也要通知
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loop.call_soon_threadsafe(queue.put_nowait, e)
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finally:
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# 发送结束标记
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loop.call_soon_threadsafe(queue.put_nowait, None)
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# 在独立线程中启动同步 API 调用
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thread = threading.Thread(target=_sync_stream, daemon=True)
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thread.start()
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# 异步消费队列
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while True:
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item = await queue.get()
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if item is None:
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# 流结束
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break
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if isinstance(item, Exception):
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# 发生异常
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raise item
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yield item
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