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https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-22 15:13:55 +08:00
update:使用intent_llm中的LLM进行工具内容回复
This commit is contained in:
@@ -157,7 +157,7 @@ selected_module:
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Memory: nomem
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# 意图识别模块开启后,可以播放音乐、控制音量、识别退出指令。
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# 不想开通意图识别,就设置成:nointent
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# 意图识别可使用intent_llm。优点:通用性强,缺点:增加串行前置意图识别模块,会增加处理时间,这个意图识别暂时不支持控制音量大小等iot操作
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# 意图识别可使用intent_llm。优点:通用性强,缺点:增加串行前置意图识别模块,会增加处理时间,支持控制音量大小等iot操作
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# 意图识别可使用function_call,缺点:需要所选择的LLM支持function_call,优点:按需调用工具、速度快,理论上能全部操作所有iot指令
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# 默认免费的ChatGLMLLM就已经支持function_call,但是如果像追求稳定建议把LLM设置成:DoubaoLLM,使用的具体model_name是:doubao-1-5-pro-32k-250115
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Intent: function_call
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@@ -108,43 +108,21 @@ async def process_intent_result(conn, intent_result, original_text):
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if result.action == Action.RESPONSE: # 直接回复前端
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text = result.response
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if text is not None:
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text_index = (
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conn.tts_last_text_index + 1
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if hasattr(conn, "tts_last_text_index")
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else 0
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)
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conn.recode_first_last_text(text, text_index)
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future = conn.executor.submit(
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conn.speak_and_play, text, text_index
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)
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conn.llm_finish_task = True
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conn.tts_queue.put((future, text_index))
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conn.dialogue.put(Message(role="assistant", content=text))
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speak_and_play(conn, text)
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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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if text is not None and len(text) > 0:
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conn.dialogue.put(Message(role="tool", content=text))
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conn.executor.submit(
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conn.chat_with_function_calling, text, True
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)
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conn.dialogue.put(Message(role="tool", content=text))
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llm_result = conn.intent.replyResult(text)
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if llm_result is None:
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llm_result = text
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speak_and_play(conn, llm_result)
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elif (
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result.action == Action.NOTFOUND
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or result.action == Action.ERROR
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):
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text = result.result
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if text is not None:
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text_index = (
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conn.tts_last_text_index + 1
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if hasattr(conn, "tts_last_text_index")
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else 0
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)
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conn.recode_first_last_text(text, text_index)
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future = conn.executor.submit(
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conn.speak_and_play, text, text_index
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)
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conn.llm_finish_task = True
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conn.tts_queue.put((future, text_index))
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conn.dialogue.put(Message(role="assistant", content=text))
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speak_and_play(conn, text)
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elif function_name != "play_music":
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# For backward compatibility with original code
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# 获取当前最新的文本索引
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@@ -152,18 +130,7 @@ async def process_intent_result(conn, intent_result, original_text):
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if text is None:
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text = result.result
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if text is not None:
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text_index = (
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conn.tts_last_text_index + 1
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if hasattr(conn, "tts_last_text_index")
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else 0
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)
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conn.recode_first_last_text(text, text_index)
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future = conn.executor.submit(
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conn.speak_and_play, text, text_index
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)
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conn.llm_finish_task = True
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conn.tts_queue.put((future, text_index))
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conn.dialogue.put(Message(role="assistant", content=text))
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speak_and_play(conn, text)
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# 将函数执行放在线程池中
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conn.executor.submit(process_function_call)
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@@ -174,21 +141,12 @@ async def process_intent_result(conn, intent_result, original_text):
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return False
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def extract_text_in_brackets(s):
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"""
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从字符串中提取中括号内的文字
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:param s: 输入字符串
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:return: 中括号内的文字,如果不存在则返回空字符串
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"""
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left_bracket_index = s.find("[")
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right_bracket_index = s.find("]")
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if (
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left_bracket_index != -1
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and right_bracket_index != -1
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and left_bracket_index < right_bracket_index
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):
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return s[left_bracket_index + 1 : right_bracket_index]
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else:
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return ""
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def speak_and_play(conn, text):
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text_index = (
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conn.tts_last_text_index + 1 if hasattr(conn, "tts_last_text_index") else 0
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)
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conn.recode_first_last_text(text, text_index)
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future = conn.executor.submit(conn.speak_and_play, text, text_index)
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conn.llm_finish_task = True
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conn.tts_queue.put((future, text_index))
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conn.dialogue.put(Message(role="assistant", content=text))
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@@ -20,6 +20,7 @@ class IntentProvider(IntentProviderBase):
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self.intent_cache = {} # 缓存意图识别结果
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self.cache_expiry = 600 # 缓存有效期10分钟
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self.cache_max_size = 100 # 最多缓存100个意图
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self.history_count = 4 # 默认使用最近4条对话记录
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def get_intent_system_prompt(self, functions_list: str) -> str:
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"""
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@@ -104,6 +105,13 @@ class IntentProvider(IntentProviderBase):
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for key, _ in sorted_items[: len(sorted_items) - self.cache_max_size]:
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del self.intent_cache[key]
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def replyResult(self, text: str):
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llm_result = self.llm.response_no_stream(
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system_prompt=text,
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user_prompt="请总结以上内容,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。",
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)
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return llm_result
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async def detect_intent(self, conn, dialogue_history: List[Dict], text: str) -> str:
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if not self.llm:
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raise ValueError("LLM provider not set")
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@@ -150,14 +158,13 @@ class IntentProvider(IntentProviderBase):
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logger.bind(tag=TAG).debug(f"User prompt: {prompt_music}")
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# 构建用户最后一句话的提示
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# 构建用户对话历史的提示
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msgStr = ""
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# 只使用最后两句即可
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if len(dialogue_history) >= 2:
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# 保证最少有两句话的时候处理
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msgStr += f"{dialogue_history[-2].role}: {dialogue_history[-2].content}\n"
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msgStr += f"{dialogue_history[-1].role}: {dialogue_history[-1].content}\n"
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# 获取最近的对话历史
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start_idx = max(0, len(dialogue_history) - self.history_count)
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for i in range(start_idx, len(dialogue_history)):
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msgStr += f"{dialogue_history[i].role}: {dialogue_history[i].content}\n"
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msgStr += f"User: {text}\n"
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user_prompt = f"current dialogue:\n{msgStr}"
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