241 lines
9.3 KiB
Python
241 lines
9.3 KiB
Python
import json
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import uuid
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import asyncio
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from core.connection import ConnectionHandler
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from core.utils.dialogue import Message
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from core.providers.tts.dto.dto import ContentType
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from core.handle.helloHandle import checkWakeupWords
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from plugins_func.register import Action, ActionResponse
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from core.handle.sendAudioHandle import send_stt_message
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from core.handle.reportHandle import enqueue_tool_report
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from core.utils.util import remove_punctuation_and_length
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from core.providers.tts.dto.dto import TTSMessageDTO, SentenceType
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TAG = __name__
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async def handle_user_intent(conn: "ConnectionHandler", text):
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# 预处理输入文本，处理可能的JSON格式
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try:
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if text.strip().startswith("{") and text.strip().endswith("}"):
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parsed_data = json.loads(text)
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if isinstance(parsed_data, dict) and "content" in parsed_data:
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text = parsed_data["content"] # 提取content用于意图分析
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conn.current_speaker = parsed_data.get("speaker") # 保留说话人信息
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except (json.JSONDecodeError, TypeError):
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pass
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# 检查是否有明确的退出命令
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_, filtered_text = remove_punctuation_and_length(text)
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if await check_direct_exit(conn, filtered_text):
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return True
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# 明确再见不被打断
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if conn.is_exiting:
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return True
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# 检查是否是唤醒词
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if await checkWakeupWords(conn, filtered_text):
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return True
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if conn.intent_type == "function_call":
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# 使用支持function calling的聊天方法,不再进行意图分析
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return False
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# 使用LLM进行意图分析
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intent_result = await analyze_intent_with_llm(conn, text)
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if not intent_result:
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return False
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# 会话开始时生成sentence_id
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conn.sentence_id = str(uuid.uuid4().hex)
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# 处理各种意图
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return await process_intent_result(conn, intent_result, text)
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async def check_direct_exit(conn: "ConnectionHandler", text):
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"""检查是否有明确的退出命令"""
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_, text = remove_punctuation_and_length(text)
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cmd_exit = conn.cmd_exit
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for cmd in cmd_exit:
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if text == cmd:
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conn.logger.bind(tag=TAG).info(f"识别到明确的退出命令: {text}")
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await send_stt_message(conn, text)
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conn.is_exiting = True
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await conn.close()
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return True
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return False
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async def analyze_intent_with_llm(conn: "ConnectionHandler", text):
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"""使用LLM分析用户意图"""
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if not hasattr(conn, "intent") or not conn.intent:
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conn.logger.bind(tag=TAG).warning("意图识别服务未初始化")
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return None
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# 对话历史记录
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dialogue = conn.dialogue
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try:
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intent_result = await conn.intent.detect_intent(conn, dialogue.dialogue, text)
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return intent_result
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except Exception as e:
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conn.logger.bind(tag=TAG).error(f"意图识别失败: {str(e)}")
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return None
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async def process_intent_result(
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conn: "ConnectionHandler", intent_result, original_text
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):
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"""处理意图识别结果"""
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try:
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# 尝试将结果解析为JSON
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intent_data = json.loads(intent_result)
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# 检查是否有function_call
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if "function_call" in intent_data:
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# 直接从意图识别获取了function_call
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conn.logger.bind(tag=TAG).debug(
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f"检测到function_call格式的意图结果: {intent_data['function_call']['name']}"
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)
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function_name = intent_data["function_call"]["name"]
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if function_name == "continue_chat":
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return False
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if function_name == "result_for_context":
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await send_stt_message(conn, original_text)
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conn.client_abort = False
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def process_context_result():
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conn.dialogue.put(Message(role="user", content=original_text))
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from core.utils.current_time import get_current_time_info
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current_time, today_date, today_weekday, lunar_date = (
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get_current_time_info()
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)
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# 构建带上下文的基础提示
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context_prompt = f"""当前时间：{current_time}
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今天日期：{today_date} ({today_weekday})
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今天农历：{lunar_date}
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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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speak_txt(conn, response)
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conn.executor.submit(process_context_result)
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return True
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function_args = {}
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if "arguments" in intent_data["function_call"]:
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function_args = intent_data["function_call"]["arguments"]
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if function_args is None:
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function_args = {}
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# 确保参数是字符串格式的JSON
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if isinstance(function_args, dict):
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function_args = json.dumps(function_args)
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function_call_data = {
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"name": function_name,
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"id": str(uuid.uuid4().hex),
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"arguments": function_args,
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}
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await send_stt_message(conn, original_text)
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conn.client_abort = False
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# 准备工具调用参数
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tool_input = {}
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if function_args:
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if isinstance(function_args, str):
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tool_input = json.loads(function_args) if function_args else {}
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elif isinstance(function_args, dict):
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tool_input = function_args
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# 上报工具调用
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enqueue_tool_report(conn, function_name, tool_input)
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# 使用executor执行函数调用和结果处理
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def process_function_call():
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conn.dialogue.put(Message(role="user", content=original_text))
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# 工具调用超时时间
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tool_call_timeout = int(conn.config.get("tool_call_timeout", 30))
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# 使用统一工具处理器处理所有工具调用
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try:
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result = asyncio.run_coroutine_threadsafe(
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conn.func_handler.handle_llm_function_call(
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conn, function_call_data
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),
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conn.loop,
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).result(timeout=tool_call_timeout)
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except Exception as e:
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conn.logger.bind(tag=TAG).error(f"工具调用失败: {e}")
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result = ActionResponse(
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action=Action.ERROR, result="工具调用超时，请一会再试下哈", response="工具调用超时，请一会再试下哈"
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)
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# 上报工具调用结果
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if result:
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enqueue_tool_report(conn, function_name, tool_input, str(result.result) if result.result else None, report_tool_call=False)
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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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speak_txt(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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conn.dialogue.put(Message(role="tool", content=text))
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llm_result = conn.intent.replyResult(text, original_text)
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if llm_result is None:
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llm_result = text
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speak_txt(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.response if result.response else result.result
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if text is not None:
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speak_txt(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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text = result.response
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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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speak_txt(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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return True
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return False
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except json.JSONDecodeError as e:
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conn.logger.bind(tag=TAG).error(f"处理意图结果时出错: {e}")
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return False
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def speak_txt(conn: "ConnectionHandler", text):
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# 记录文本
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conn.tts_MessageText = text
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conn.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=conn.sentence_id,
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sentence_type=SentenceType.FIRST,
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content_type=ContentType.ACTION,
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)
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)
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conn.tts.tts_one_sentence(conn, ContentType.TEXT, content_detail=text)
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conn.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=conn.sentence_id,
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sentence_type=SentenceType.LAST,
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content_type=ContentType.ACTION,
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)
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)
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conn.dialogue.put(Message(role="assistant", content=text))
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