mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-27 01:23:55 +08:00
@@ -118,8 +118,17 @@ async def process_intent_result(
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请根据以上信息回答用户的问题:{original_text}"""
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请根据以上信息回答用户的问题:{original_text}"""
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response = conn.intent.replyResult(context_prompt, original_text)
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# 使用异步调用避免阻塞事件循环,影响其他设备的音频播放
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speak_txt(conn, response)
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try:
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response = asyncio.run_coroutine_threadsafe(
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conn.intent.replyResult(context_prompt, original_text),
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conn.loop,
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).result()
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except Exception as e:
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conn.logger.bind(tag=TAG).error(f"LLM生成回复失败: {e}")
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response = None
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if response:
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speak_txt(conn, response)
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conn.executor.submit(process_context_result)
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conn.executor.submit(process_context_result)
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return True
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return True
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@@ -184,7 +193,15 @@ async def process_intent_result(
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elif result.action == Action.REQLLM: # 调用函数后再请求llm生成回复
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elif result.action == Action.REQLLM: # 调用函数后再请求llm生成回复
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text = result.result
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text = result.result
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conn.dialogue.put(Message(role="tool", content=text))
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conn.dialogue.put(Message(role="tool", content=text))
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llm_result = conn.intent.replyResult(text, original_text)
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# 使用异步调用避免阻塞事件循环,影响其他设备的音频播放
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try:
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llm_result = asyncio.run_coroutine_threadsafe(
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conn.intent.replyResult(text, original_text),
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conn.loop,
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).result()
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except Exception as e:
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conn.logger.bind(tag=TAG).error(f"LLM生成回复失败: {e}")
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llm_result = text
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if llm_result is None:
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if llm_result is None:
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llm_result = text
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llm_result = text
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speak_txt(conn, llm_result)
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speak_txt(conn, llm_result)
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@@ -1,3 +1,4 @@
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import asyncio
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from typing import List, Dict, TYPE_CHECKING
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from typing import List, Dict, TYPE_CHECKING
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if TYPE_CHECKING:
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if TYPE_CHECKING:
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@@ -120,12 +121,18 @@ class IntentProvider(IntentProviderBase):
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)
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)
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return prompt
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return prompt
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def replyResult(self, text: str, original_text: str):
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async def replyResult(self, text: str, original_text: str):
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"""使用 asyncio.to_thread 避免阻塞事件循环"""
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try:
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try:
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llm_result = self.llm.response_no_stream(
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user_prompt = (
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"请根据以上内容,像人类一样说话的口吻回复用户,要求简洁,请直接返回结果。用户现在说:"
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+ original_text
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)
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# 使用 to_thread 将同步阻塞调用放到线程池中执行,不阻塞事件循环
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llm_result = await asyncio.to_thread(
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self.llm.response_no_stream,
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system_prompt=text,
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system_prompt=text,
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user_prompt="请根据以上内容,像人类一样说话的口吻回复用户,要求简洁,请直接返回结果。用户现在说:"
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user_prompt=user_prompt,
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+ original_text,
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)
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)
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return llm_result
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return llm_result
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except Exception as e:
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except Exception as e:
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@@ -207,8 +214,11 @@ class IntentProvider(IntentProviderBase):
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logger.bind(tag=TAG).debug(f"开始LLM意图识别调用, 模型: {model_info}")
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logger.bind(tag=TAG).debug(f"开始LLM意图识别调用, 模型: {model_info}")
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try:
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try:
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intent = self.llm.response_no_stream(
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# 使用 to_thread 将同步阻塞调用放到线程池中,避免阻塞事件循环
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system_prompt=prompt_music, user_prompt=user_prompt
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intent = await asyncio.to_thread(
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self.llm.response_no_stream,
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system_prompt=prompt_music,
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user_prompt=user_prompt,
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)
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)
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except Exception as e:
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in intent detection LLM call: {e}")
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logger.bind(tag=TAG).error(f"Error in intent detection LLM call: {e}")
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@@ -1,6 +1,7 @@
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import random
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import random
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import httpx
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import httpx
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from markitdown import MarkItDown
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from io import BytesIO
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from markitdown import MarkItDown, StreamInfo
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from config.logger import setup_logging
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from config.logger import setup_logging
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from plugins_func.register import register_function, ToolType, ActionResponse, Action
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from plugins_func.register import register_function, ToolType, ActionResponse, Action
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from typing import TYPE_CHECKING
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from typing import TYPE_CHECKING
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@@ -144,7 +145,14 @@ async def fetch_news_detail(url):
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# 使用MarkItDown清理HTML内容
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# 使用MarkItDown清理HTML内容
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md = MarkItDown(enable_plugins=False)
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md = MarkItDown(enable_plugins=False)
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result = md.convert(response)
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result = md.convert_stream(
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BytesIO(response.content),
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stream_info=StreamInfo(
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mimetype="text/html",
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extension=".html",
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charset=response.encoding or "utf-8",
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),
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)
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# 获取清理后的文本内容
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# 获取清理后的文本内容
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clean_text = result.text_content
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clean_text = result.text_content
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