mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
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Merge pull request #2931 from xinnan-tech/py_test_error_response
Py test error response
This commit is contained in:
wengzh
@@ -0,0 +1 @@
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INSERT INTO `sys_params` (id, param_code, param_value, value_type, param_type, remark) VALUES (502, 'system_error_response', '主人，小智现在有点忙，我们稍后再试吧。', 'string', 1, '系统错误时的回复');
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@@ -508,3 +508,10 @@ databaseChangeLog:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202601261730.sql
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- changeSet:
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id: 202602021555
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author: shengzhou1216
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changes:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202602021555.sql
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@@ -203,6 +203,9 @@ prompt: |
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# 默认系统提示词模板文件
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prompt_template: agent-base-prompt.txt
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# 系统错误时的回复
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system_error_response: "主人，小智现在有点忙，我们稍后再试吧。"
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# 结束语prompt
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end_prompt:
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enable: true # 是否开启结束语
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@@ -40,8 +40,10 @@ from config.logger import setup_logging, build_module_string, create_connection_
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from config.manage_api_client import DeviceNotFoundException, DeviceBindException
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from core.utils.prompt_manager import PromptManager
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from core.utils.voiceprint_provider import VoiceprintProvider
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from core.utils.util import get_system_error_response
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from core.utils import textUtils
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TAG = __name__
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auto_import_modules("plugins_func.functions")
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@@ -135,7 +137,6 @@ class ConnectionHandler:
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self.current_language_tag = None # 存储当前ASR识别的语言标签
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# llm相关变量
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self.llm_finish_task = True
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self.dialogue = Dialogue()
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# tts相关变量
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@@ -799,7 +800,6 @@ class ConnectionHandler:
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# 为最顶层时新建会话ID和发送FIRST请求
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if depth == 0:
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self.llm_finish_task = False
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self.sentence_id = str(uuid.uuid4().hex)
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self.dialogue.put(Message(role="user", content=query))
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self.tts.tts_text_queue.put(
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@@ -874,46 +874,66 @@ class ConnectionHandler:
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content_arguments = ""
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self.client_abort = False
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emotion_flag = True
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for response in llm_responses:
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if self.client_abort:
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break
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if self.intent_type == "function_call" and functions is not None:
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content, tools_call = response
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if "content" in response:
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content = response["content"]
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tools_call = None
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if content is not None and len(content) > 0:
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content_arguments += content
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try:
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for response in llm_responses:
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if self.client_abort:
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break
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if self.intent_type == "function_call" and functions is not None:
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content, tools_call = response
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if "content" in response:
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content = response["content"]
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tools_call = None
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if content is not None and len(content) > 0:
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content_arguments += content
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if not tool_call_flag and content_arguments.startswith("<tool_call>"):
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# print("content_arguments", content_arguments)
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tool_call_flag = True
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if not tool_call_flag and content_arguments.startswith("<tool_call>"):
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# print("content_arguments", content_arguments)
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tool_call_flag = True
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if tools_call is not None and len(tools_call) > 0:
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tool_call_flag = True
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self._merge_tool_calls(tool_calls_list, tools_call)
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else:
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content = response
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if tools_call is not None and len(tools_call) > 0:
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tool_call_flag = True
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self._merge_tool_calls(tool_calls_list, tools_call)
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else:
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content = response
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# 在llm回复中获取情绪表情，一轮对话只在开头获取一次
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if emotion_flag and content is not None and content.strip():
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asyncio.run_coroutine_threadsafe(
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textUtils.get_emotion(self, content),
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self.loop,
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)
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emotion_flag = False
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if content is not None and len(content) > 0:
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if not tool_call_flag:
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response_message.append(content)
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self.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=self.sentence_id,
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sentence_type=SentenceType.MIDDLE,
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content_type=ContentType.TEXT,
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content_detail=content,
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)
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# 在llm回复中获取情绪表情，一轮对话只在开头获取一次
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if emotion_flag and content is not None and content.strip():
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asyncio.run_coroutine_threadsafe(
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textUtils.get_emotion(self, content),
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self.loop,
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)
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emotion_flag = False
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if content is not None and len(content) > 0:
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if not tool_call_flag:
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response_message.append(content)
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self.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=self.sentence_id,
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sentence_type=SentenceType.MIDDLE,
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content_type=ContentType.TEXT,
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content_detail=content,
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)
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)
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except Exception as e:
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self.logger.bind(tag=TAG).error(f"LLM stream processing error: {e}")
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self.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=self.sentence_id,
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sentence_type=SentenceType.MIDDLE,
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content_type=ContentType.TEXT,
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content_detail=get_system_error_response(self.config),
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)
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)
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if depth == 0:
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self.tts.tts_text_queue.put(
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TTSMessageDTO(
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sentence_id=self.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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return
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# 处理function call
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if tool_call_flag:
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bHasError = False
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@@ -994,7 +1014,6 @@ class ConnectionHandler:
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content_type=ContentType.ACTION,
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)
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)
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self.llm_finish_task = True
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# 使用lambda延迟计算，只有在DEBUG级别时才执行get_llm_dialogue()
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self.logger.bind(tag=TAG).debug(
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lambda: json.dumps(
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@@ -2,11 +2,14 @@ from typing import List, Dict
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from ..base import IntentProviderBase
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from plugins_func.functions.play_music import initialize_music_handler
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from config.logger import setup_logging
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from core.utils.util import get_system_error_response
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import re
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import json
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import hashlib
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import time
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TAG = __name__
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logger = setup_logging()
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@@ -115,12 +118,16 @@ class IntentProvider(IntentProviderBase):
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return prompt
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def replyResult(self, text: str, original_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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return llm_result
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try:
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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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return llm_result
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in generating reply result: {e}")
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return get_system_error_response(self.config)
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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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@@ -194,9 +201,13 @@ class IntentProvider(IntentProviderBase):
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llm_start_time = time.time()
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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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intent = self.llm.response_no_stream(
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system_prompt=prompt_music, user_prompt=user_prompt
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)
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try:
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intent = self.llm.response_no_stream(
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system_prompt=prompt_music, user_prompt=user_prompt
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)
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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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return '{"function_call": {"name": "continue_chat"}}'
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# 记录LLM调用完成时间
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llm_time = time.time() - llm_start_time
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@@ -21,83 +21,78 @@ class LLMProvider(LLMProviderBase):
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check_model_key("AliBLLLM", self.api_key)
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def response(self, session_id, dialogue):
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try:
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# 处理dialogue
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if self.is_No_prompt:
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dialogue.pop(0)
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logger.bind(tag=TAG).debug(
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f"【阿里百练API服务】处理后的dialogue: {dialogue}"
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)
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# 构造调用参数
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call_params = {
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"api_key": self.api_key,
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"app_id": self.app_id,
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"session_id": session_id,
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"messages": dialogue,
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# 开启SDK原生流式
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"stream": True,
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}
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if self.memory_id != False:
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# 百练memory需要prompt参数
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prompt = dialogue[-1].get("content")
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call_params["memory_id"] = self.memory_id
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call_params["prompt"] = prompt
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logger.bind(tag=TAG).debug(
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f"【阿里百练API服务】处理后的prompt: {prompt}"
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)
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# 可选地设置自定义API基地址（若配置为兼容模式URL则忽略）
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if self.base_url and ("/api/" in self.base_url):
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dashscope.base_http_api_url = self.base_url
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responses = Application.call(**call_params)
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# 流式处理（SDK在stream=True时返回可迭代对象；否则返回单次响应对象）
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# 处理dialogue
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if self.is_No_prompt:
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dialogue.pop(0)
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logger.bind(tag=TAG).debug(
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f"【阿里百练API服务】构造参数: {dict(call_params, api_key='***')}"
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f"【阿里百练API服务】处理后的dialogue: {dialogue}"
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)
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last_text = ""
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try:
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for resp in responses:
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if resp.status_code != HTTPStatus.OK:
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logger.bind(tag=TAG).error(
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f"code={resp.status_code}, message={resp.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code"
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)
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continue
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current_text = getattr(getattr(resp, "output", None), "text", None)
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if current_text is None:
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continue
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# SDK流式为增量覆盖，计算差量输出
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if len(current_text) >= len(last_text):
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delta = current_text[len(last_text):]
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else:
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# 避免偶发回退
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delta = current_text
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if delta:
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yield delta
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last_text = current_text
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except TypeError:
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# 非流式回落（一次性返回）
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if responses.status_code != HTTPStatus.OK:
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logger.bind(tag=TAG).error(
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f"code={responses.status_code}, message={responses.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code"
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)
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yield "【阿里百练API服务响应异常】"
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else:
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full_text = getattr(getattr(responses, "output", None), "text", "")
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logger.bind(tag=TAG).info(
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f"【阿里百练API服务】完整响应长度: {len(full_text)}"
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)
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for i in range(0, len(full_text), self.streaming_chunk_size):
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chunk = full_text[i:i + self.streaming_chunk_size]
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if chunk:
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yield chunk
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# 构造调用参数
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call_params = {
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"api_key": self.api_key,
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"app_id": self.app_id,
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"session_id": session_id,
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"messages": dialogue,
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# 开启SDK原生流式
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"stream": True,
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}
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if self.memory_id != False:
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# 百练memory需要prompt参数
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prompt = dialogue[-1].get("content")
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call_params["memory_id"] = self.memory_id
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call_params["prompt"] = prompt
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logger.bind(tag=TAG).debug(
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f"【阿里百练API服务】处理后的prompt: {prompt}"
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)
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except Exception as e:
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logger.bind(tag=TAG).error(f"【阿里百练API服务】响应异常: {e}")
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yield "【LLM服务响应异常】"
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# 可选地设置自定义API基地址（若配置为兼容模式URL则忽略）
|
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if self.base_url and ("/api/" in self.base_url):
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dashscope.base_http_api_url = self.base_url
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responses = Application.call(**call_params)
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# 流式处理（SDK在stream=True时返回可迭代对象；否则返回单次响应对象）
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logger.bind(tag=TAG).debug(
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f"【阿里百练API服务】构造参数: {dict(call_params, api_key='***')}"
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)
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last_text = ""
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try:
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for resp in responses:
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if resp.status_code != HTTPStatus.OK:
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logger.bind(tag=TAG).error(
|
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f"code={resp.status_code}, message={resp.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code"
|
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)
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continue
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current_text = getattr(getattr(resp, "output", None), "text", None)
|
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if current_text is None:
|
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continue
|
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# SDK流式为增量覆盖，计算差量输出
|
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if len(current_text) >= len(last_text):
|
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delta = current_text[len(last_text):]
|
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else:
|
||||
# 避免偶发回退
|
||||
delta = current_text
|
||||
if delta:
|
||||
yield delta
|
||||
last_text = current_text
|
||||
except TypeError:
|
||||
# 非流式回落（一次性返回）
|
||||
if responses.status_code != HTTPStatus.OK:
|
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logger.bind(tag=TAG).error(
|
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f"code={responses.status_code}, message={responses.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code"
|
||||
)
|
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yield "【阿里百练API服务响应异常】"
|
||||
else:
|
||||
full_text = getattr(getattr(responses, "output", None), "text", "")
|
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logger.bind(tag=TAG).info(
|
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f"【阿里百练API服务】完整响应长度: {len(full_text)}"
|
||||
)
|
||||
for i in range(0, len(full_text), self.streaming_chunk_size):
|
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chunk = full_text[i:i + self.streaming_chunk_size]
|
||||
if chunk:
|
||||
yield chunk
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
# 阿里百练当前未支持原生的 function call。为保持兼容，这里回退到普通文本流式输出。
|
||||
|
||||
@@ -11,20 +11,15 @@ class LLMProviderBase(ABC):
|
||||
pass
|
||||
|
||||
def response_no_stream(self, system_prompt, user_prompt, **kwargs):
|
||||
try:
|
||||
# 构造对话格式
|
||||
dialogue = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt}
|
||||
]
|
||||
result = ""
|
||||
for part in self.response("", dialogue, **kwargs):
|
||||
result += part
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in Ollama response generation: {e}")
|
||||
return "【LLM服务响应异常】"
|
||||
# 构造对话格式
|
||||
dialogue = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt}
|
||||
]
|
||||
result = ""
|
||||
for part in self.response("", dialogue, **kwargs):
|
||||
result += part
|
||||
return result
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
"""
|
||||
|
||||
@@ -20,76 +20,71 @@ class LLMProvider(LLMProviderBase):
|
||||
logger.bind(tag=TAG).error(model_key_msg)
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
# 取最后一条用户消息
|
||||
last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
|
||||
conversation_id = self.session_conversation_map.get(session_id)
|
||||
# 取最后一条用户消息
|
||||
last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
|
||||
conversation_id = self.session_conversation_map.get(session_id)
|
||||
|
||||
# 发起流式请求
|
||||
# 发起流式请求
|
||||
if self.mode == "chat-messages":
|
||||
request_json = {
|
||||
"query": last_msg["content"],
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
"inputs": {},
|
||||
"conversation_id": conversation_id,
|
||||
}
|
||||
elif self.mode == "workflows/run":
|
||||
request_json = {
|
||||
"inputs": {"query": last_msg["content"]},
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
}
|
||||
elif self.mode == "completion-messages":
|
||||
request_json = {
|
||||
"inputs": {"query": last_msg["content"]},
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
}
|
||||
|
||||
with requests.post(
|
||||
f"{self.base_url}/{self.mode}",
|
||||
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||
json=request_json,
|
||||
stream=True,
|
||||
) as r:
|
||||
if self.mode == "chat-messages":
|
||||
request_json = {
|
||||
"query": last_msg["content"],
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
"inputs": {},
|
||||
"conversation_id": conversation_id,
|
||||
}
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
# 如果没有找到conversation_id，则获取此次conversation_id
|
||||
if not conversation_id:
|
||||
conversation_id = event.get("conversation_id")
|
||||
self.session_conversation_map[session_id] = (
|
||||
conversation_id # 更新映射
|
||||
)
|
||||
# 过滤 message_replace 事件，此事件会全量推一次
|
||||
if event.get("event") != "message_replace" and event.get(
|
||||
"answer"
|
||||
):
|
||||
yield event["answer"]
|
||||
elif self.mode == "workflows/run":
|
||||
request_json = {
|
||||
"inputs": {"query": last_msg["content"]},
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
}
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
if event.get("event") == "workflow_finished":
|
||||
if event["data"]["status"] == "succeeded":
|
||||
yield event["data"]["outputs"]["answer"]
|
||||
else:
|
||||
yield "【服务响应异常】"
|
||||
elif self.mode == "completion-messages":
|
||||
request_json = {
|
||||
"inputs": {"query": last_msg["content"]},
|
||||
"response_mode": "streaming",
|
||||
"user": session_id,
|
||||
}
|
||||
|
||||
with requests.post(
|
||||
f"{self.base_url}/{self.mode}",
|
||||
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||
json=request_json,
|
||||
stream=True,
|
||||
) as r:
|
||||
if self.mode == "chat-messages":
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
# 如果没有找到conversation_id，则获取此次conversation_id
|
||||
if not conversation_id:
|
||||
conversation_id = event.get("conversation_id")
|
||||
self.session_conversation_map[session_id] = (
|
||||
conversation_id # 更新映射
|
||||
)
|
||||
# 过滤 message_replace 事件，此事件会全量推一次
|
||||
if event.get("event") != "message_replace" and event.get(
|
||||
"answer"
|
||||
):
|
||||
yield event["answer"]
|
||||
elif self.mode == "workflows/run":
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
if event.get("event") == "workflow_finished":
|
||||
if event["data"]["status"] == "succeeded":
|
||||
yield event["data"]["outputs"]["answer"]
|
||||
else:
|
||||
yield "【服务响应异常】"
|
||||
elif self.mode == "completion-messages":
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
# 过滤 message_replace 事件，此事件会全量推一次
|
||||
if event.get("event") != "message_replace" and event.get(
|
||||
"answer"
|
||||
):
|
||||
yield event["answer"]
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in response generation: {e}")
|
||||
yield "【服务响应异常】"
|
||||
for line in r.iter_lines():
|
||||
if line.startswith(b"data: "):
|
||||
event = json.loads(line[6:])
|
||||
# 过滤 message_replace 事件，此事件会全量推一次
|
||||
if event.get("event") != "message_replace" and event.get(
|
||||
"answer"
|
||||
):
|
||||
yield event["answer"]
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
if len(dialogue) == 2 and functions is not None and len(functions) > 0:
|
||||
|
||||
@@ -19,53 +19,48 @@ class LLMProvider(LLMProviderBase):
|
||||
logger.bind(tag=TAG).error(model_key_msg)
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
# 取最后一条用户消息
|
||||
last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
|
||||
# 取最后一条用户消息
|
||||
last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
|
||||
|
||||
# 发起流式请求
|
||||
with requests.post(
|
||||
f"{self.base_url}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||
json={
|
||||
"stream": True,
|
||||
"chatId": session_id,
|
||||
"detail": self.detail,
|
||||
"variables": self.variables,
|
||||
"messages": [{"role": "user", "content": last_msg["content"]}],
|
||||
},
|
||||
stream=True,
|
||||
) as r:
|
||||
for line in r.iter_lines():
|
||||
if line:
|
||||
try:
|
||||
if line.startswith(b"data: "):
|
||||
if line[6:].decode("utf-8") == "[DONE]":
|
||||
break
|
||||
# 发起流式请求
|
||||
with requests.post(
|
||||
f"{self.base_url}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {self.api_key}"},
|
||||
json={
|
||||
"stream": True,
|
||||
"chatId": session_id,
|
||||
"detail": self.detail,
|
||||
"variables": self.variables,
|
||||
"messages": [{"role": "user", "content": last_msg["content"]}],
|
||||
},
|
||||
stream=True,
|
||||
) as r:
|
||||
for line in r.iter_lines():
|
||||
if line:
|
||||
try:
|
||||
if line.startswith(b"data: "):
|
||||
if line[6:].decode("utf-8") == "[DONE]":
|
||||
break
|
||||
|
||||
data = json.loads(line[6:])
|
||||
if "choices" in data and len(data["choices"]) > 0:
|
||||
delta = data["choices"][0].get("delta", {})
|
||||
if (
|
||||
delta
|
||||
and "content" in delta
|
||||
and delta["content"] is not None
|
||||
):
|
||||
content = delta["content"]
|
||||
if "<think>" in content:
|
||||
continue
|
||||
if "</think>" in content:
|
||||
continue
|
||||
yield content
|
||||
data = json.loads(line[6:])
|
||||
if "choices" in data and len(data["choices"]) > 0:
|
||||
delta = data["choices"][0].get("delta", {})
|
||||
if (
|
||||
delta
|
||||
and "content" in delta
|
||||
and delta["content"] is not None
|
||||
):
|
||||
content = delta["content"]
|
||||
if "<think>" in content:
|
||||
continue
|
||||
if "</think>" in content:
|
||||
continue
|
||||
yield content
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
continue
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in response generation: {e}")
|
||||
yield "【服务响应异常】"
|
||||
except json.JSONDecodeError as e:
|
||||
continue
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
logger.bind(tag=TAG).error(
|
||||
|
||||
@@ -15,55 +15,49 @@ class LLMProvider(LLMProviderBase):
|
||||
self.api_url = f"{self.base_url}/api/conversation/process" # 拼接完整的 API URL
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
# home assistant语音助手自带意图，无需使用xiaozhi ai自带的，只需要把用户说的话传递给home assistant即可
|
||||
# home assistant语音助手自带意图，无需使用xiaozhi ai自带的，只需要把用户说的话传递给home assistant即可
|
||||
|
||||
# 提取最后一个 role 为 'user' 的 content
|
||||
input_text = None
|
||||
if isinstance(dialogue, list): # 确保 dialogue 是一个列表
|
||||
# 逆序遍历，找到最后一个 role 为 'user' 的消息
|
||||
for message in reversed(dialogue):
|
||||
if message.get("role") == "user": # 找到 role 为 'user' 的消息
|
||||
input_text = message.get("content", "")
|
||||
break # 找到后立即退出循环
|
||||
# 提取最后一个 role 为 'user' 的 content
|
||||
input_text = None
|
||||
if isinstance(dialogue, list): # 确保 dialogue 是一个列表
|
||||
# 逆序遍历，找到最后一个 role 为 'user' 的消息
|
||||
for message in reversed(dialogue):
|
||||
if message.get("role") == "user": # 找到 role 为 'user' 的消息
|
||||
input_text = message.get("content", "")
|
||||
break # 找到后立即退出循环
|
||||
|
||||
# 构造请求数据
|
||||
payload = {
|
||||
"text": input_text,
|
||||
"agent_id": self.agent_id,
|
||||
"conversation_id": session_id, # 使用 session_id 作为 conversation_id
|
||||
}
|
||||
# 设置请求头
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
# 构造请求数据
|
||||
payload = {
|
||||
"text": input_text,
|
||||
"agent_id": self.agent_id,
|
||||
"conversation_id": session_id, # 使用 session_id 作为 conversation_id
|
||||
}
|
||||
# 设置请求头
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
# 发起 POST 请求
|
||||
response = requests.post(self.api_url, json=payload, headers=headers)
|
||||
# 发起 POST 请求
|
||||
response = requests.post(self.api_url, json=payload, headers=headers)
|
||||
|
||||
# 检查请求是否成功
|
||||
response.raise_for_status()
|
||||
# 检查请求是否成功
|
||||
response.raise_for_status()
|
||||
|
||||
# 解析返回数据
|
||||
data = response.json()
|
||||
speech = (
|
||||
data.get("response", {})
|
||||
.get("speech", {})
|
||||
.get("plain", {})
|
||||
.get("speech", "")
|
||||
)
|
||||
# 解析返回数据
|
||||
data = response.json()
|
||||
speech = (
|
||||
data.get("response", {})
|
||||
.get("speech", {})
|
||||
.get("plain", {})
|
||||
.get("speech", "")
|
||||
)
|
||||
|
||||
# 返回生成的内容
|
||||
if speech:
|
||||
yield speech
|
||||
else:
|
||||
logger.bind(tag=TAG).warning("API 返回数据中没有 speech 内容")
|
||||
|
||||
except RequestException as e:
|
||||
logger.bind(tag=TAG).error(f"HTTP 请求错误: {e}")
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"生成响应时出错: {e}")
|
||||
# 返回生成的内容
|
||||
if speech:
|
||||
yield speech
|
||||
else:
|
||||
logger.bind(tag=TAG).warning("API 返回数据中没有 speech 内容")
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
logger.bind(tag=TAG).error(
|
||||
|
||||
@@ -25,151 +25,141 @@ class LLMProvider(LLMProviderBase):
|
||||
self.is_qwen3 = self.model_name and self.model_name.lower().startswith("qwen3")
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
|
||||
if self.is_qwen3:
|
||||
# 复制对话列表，避免修改原始对话
|
||||
dialogue_copy = dialogue.copy()
|
||||
# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
|
||||
if self.is_qwen3:
|
||||
# 复制对话列表，避免修改原始对话
|
||||
dialogue_copy = dialogue.copy()
|
||||
|
||||
# 找到最后一条用户消息
|
||||
for i in range(len(dialogue_copy) - 1, -1, -1):
|
||||
if dialogue_copy[i]["role"] == "user":
|
||||
# 在用户消息前添加/no_think指令
|
||||
dialogue_copy[i]["content"] = (
|
||||
"/no_think " + dialogue_copy[i]["content"]
|
||||
)
|
||||
logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
|
||||
break
|
||||
|
||||
# 使用修改后的对话
|
||||
dialogue = dialogue_copy
|
||||
|
||||
responses = self.client.chat.completions.create(
|
||||
model=self.model_name, messages=dialogue, stream=True
|
||||
)
|
||||
is_active = True
|
||||
# 用于处理跨chunk的标签
|
||||
buffer = ""
|
||||
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
# 找到最后一条用户消息
|
||||
for i in range(len(dialogue_copy) - 1, -1, -1):
|
||||
if dialogue_copy[i]["role"] == "user":
|
||||
# 在用户消息前添加/no_think指令
|
||||
dialogue_copy[i]["content"] = (
|
||||
"/no_think " + dialogue_copy[i]["content"]
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else ""
|
||||
logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
|
||||
break
|
||||
|
||||
if content:
|
||||
# 将内容添加到缓冲区
|
||||
buffer += content
|
||||
# 使用修改后的对话
|
||||
dialogue = dialogue_copy
|
||||
|
||||
# 处理缓冲区中的标签
|
||||
while "<think>" in buffer and "</think>" in buffer:
|
||||
# 找到完整的<think></think>标签并移除
|
||||
pre = buffer.split("<think>", 1)[0]
|
||||
post = buffer.split("</think>", 1)[1]
|
||||
buffer = pre + post
|
||||
responses = self.client.chat.completions.create(
|
||||
model=self.model_name, messages=dialogue, stream=True
|
||||
)
|
||||
is_active = True
|
||||
# 用于处理跨chunk的标签
|
||||
buffer = ""
|
||||
|
||||
# 处理只有开始标签的情况
|
||||
if "<think>" in buffer:
|
||||
is_active = False
|
||||
buffer = buffer.split("<think>", 1)[0]
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else ""
|
||||
|
||||
# 处理只有结束标签的情况
|
||||
if "</think>" in buffer:
|
||||
is_active = True
|
||||
buffer = buffer.split("</think>", 1)[1]
|
||||
if content:
|
||||
# 将内容添加到缓冲区
|
||||
buffer += content
|
||||
|
||||
# 如果当前处于活动状态且缓冲区有内容，则输出
|
||||
if is_active and buffer:
|
||||
yield buffer
|
||||
buffer = "" # 清空缓冲区
|
||||
# 处理缓冲区中的标签
|
||||
while "<think>" in buffer and "</think>" in buffer:
|
||||
# 找到完整的<think></think>标签并移除
|
||||
pre = buffer.split("<think>", 1)[0]
|
||||
post = buffer.split("</think>", 1)[1]
|
||||
buffer = pre + post
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
|
||||
# 处理只有开始标签的情况
|
||||
if "<think>" in buffer:
|
||||
is_active = False
|
||||
buffer = buffer.split("<think>", 1)[0]
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in Ollama response generation: {e}")
|
||||
yield "【Ollama服务响应异常】"
|
||||
# 处理只有结束标签的情况
|
||||
if "</think>" in buffer:
|
||||
is_active = True
|
||||
buffer = buffer.split("</think>", 1)[1]
|
||||
|
||||
# 如果当前处于活动状态且缓冲区有内容，则输出
|
||||
if is_active and buffer:
|
||||
yield buffer
|
||||
buffer = "" # 清空缓冲区
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
try:
|
||||
# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
|
||||
if self.is_qwen3:
|
||||
# 复制对话列表，避免修改原始对话
|
||||
dialogue_copy = dialogue.copy()
|
||||
# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
|
||||
if self.is_qwen3:
|
||||
# 复制对话列表，避免修改原始对话
|
||||
dialogue_copy = dialogue.copy()
|
||||
|
||||
# 找到最后一条用户消息
|
||||
for i in range(len(dialogue_copy) - 1, -1, -1):
|
||||
if dialogue_copy[i]["role"] == "user":
|
||||
# 在用户消息前添加/no_think指令
|
||||
dialogue_copy[i]["content"] = (
|
||||
"/no_think " + dialogue_copy[i]["content"]
|
||||
)
|
||||
logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
|
||||
break
|
||||
|
||||
# 使用修改后的对话
|
||||
dialogue = dialogue_copy
|
||||
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=dialogue,
|
||||
stream=True,
|
||||
tools=functions,
|
||||
)
|
||||
|
||||
is_active = True
|
||||
buffer = ""
|
||||
|
||||
for chunk in stream:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else None
|
||||
tool_calls = (
|
||||
delta.tool_calls if hasattr(delta, "tool_calls") else None
|
||||
# 找到最后一条用户消息
|
||||
for i in range(len(dialogue_copy) - 1, -1, -1):
|
||||
if dialogue_copy[i]["role"] == "user":
|
||||
# 在用户消息前添加/no_think指令
|
||||
dialogue_copy[i]["content"] = (
|
||||
"/no_think " + dialogue_copy[i]["content"]
|
||||
)
|
||||
logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
|
||||
break
|
||||
|
||||
# 如果是工具调用，直接传递
|
||||
if tool_calls:
|
||||
yield None, tool_calls
|
||||
continue
|
||||
# 使用修改后的对话
|
||||
dialogue = dialogue_copy
|
||||
|
||||
# 处理文本内容
|
||||
if content:
|
||||
# 将内容添加到缓冲区
|
||||
buffer += content
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=dialogue,
|
||||
stream=True,
|
||||
tools=functions,
|
||||
)
|
||||
|
||||
# 处理缓冲区中的标签
|
||||
while "<think>" in buffer and "</think>" in buffer:
|
||||
# 找到完整的<think></think>标签并移除
|
||||
pre = buffer.split("<think>", 1)[0]
|
||||
post = buffer.split("</think>", 1)[1]
|
||||
buffer = pre + post
|
||||
is_active = True
|
||||
buffer = ""
|
||||
|
||||
# 处理只有开始标签的情况
|
||||
if "<think>" in buffer:
|
||||
is_active = False
|
||||
buffer = buffer.split("<think>", 1)[0]
|
||||
for chunk in stream:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else None
|
||||
tool_calls = (
|
||||
delta.tool_calls if hasattr(delta, "tool_calls") else None
|
||||
)
|
||||
|
||||
# 处理只有结束标签的情况
|
||||
if "</think>" in buffer:
|
||||
is_active = True
|
||||
buffer = buffer.split("</think>", 1)[1]
|
||||
|
||||
# 如果当前处于活动状态且缓冲区有内容，则输出
|
||||
if is_active and buffer:
|
||||
yield buffer, None
|
||||
buffer = "" # 清空缓冲区
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing function chunk: {e}")
|
||||
# 如果是工具调用，直接传递
|
||||
if tool_calls:
|
||||
yield None, tool_calls
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in Ollama function call: {e}")
|
||||
yield f"【Ollama服务响应异常: {str(e)}】", None
|
||||
# 处理文本内容
|
||||
if content:
|
||||
# 将内容添加到缓冲区
|
||||
buffer += content
|
||||
|
||||
# 处理缓冲区中的标签
|
||||
while "<think>" in buffer and "</think>" in buffer:
|
||||
# 找到完整的<think></think>标签并移除
|
||||
pre = buffer.split("<think>", 1)[0]
|
||||
post = buffer.split("</think>", 1)[1]
|
||||
buffer = pre + post
|
||||
|
||||
# 处理只有开始标签的情况
|
||||
if "<think>" in buffer:
|
||||
is_active = False
|
||||
buffer = buffer.split("<think>", 1)[0]
|
||||
|
||||
# 处理只有结束标签的情况
|
||||
if "</think>" in buffer:
|
||||
is_active = True
|
||||
buffer = buffer.split("</think>", 1)[1]
|
||||
|
||||
# 如果当前处于活动状态且缓冲区有内容，则输出
|
||||
if is_active and buffer:
|
||||
yield buffer, None
|
||||
buffer = "" # 清空缓冲区
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing function chunk: {e}")
|
||||
continue
|
||||
|
||||
@@ -56,87 +56,78 @@ class LLMProvider(LLMProviderBase):
|
||||
return dialogue
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
dialogue = self.normalize_dialogue(dialogue)
|
||||
dialogue = self.normalize_dialogue(dialogue)
|
||||
|
||||
request_params = {
|
||||
"model": self.model_name,
|
||||
"messages": dialogue,
|
||||
"stream": True,
|
||||
}
|
||||
request_params = {
|
||||
"model": self.model_name,
|
||||
"messages": dialogue,
|
||||
"stream": True,
|
||||
}
|
||||
|
||||
# 添加可选参数,只有当参数不为None时才添加
|
||||
optional_params = {
|
||||
"max_tokens": kwargs.get("max_tokens", self.max_tokens),
|
||||
"temperature": kwargs.get("temperature", self.temperature),
|
||||
"top_p": kwargs.get("top_p", self.top_p),
|
||||
"frequency_penalty": kwargs.get("frequency_penalty", self.frequency_penalty),
|
||||
}
|
||||
# 添加可选参数,只有当参数不为None时才添加
|
||||
optional_params = {
|
||||
"max_tokens": kwargs.get("max_tokens", self.max_tokens),
|
||||
"temperature": kwargs.get("temperature", self.temperature),
|
||||
"top_p": kwargs.get("top_p", self.top_p),
|
||||
"frequency_penalty": kwargs.get("frequency_penalty", self.frequency_penalty),
|
||||
}
|
||||
|
||||
for key, value in optional_params.items():
|
||||
if value is not None:
|
||||
request_params[key] = value
|
||||
for key, value in optional_params.items():
|
||||
if value is not None:
|
||||
request_params[key] = value
|
||||
|
||||
responses = self.client.chat.completions.create(**request_params)
|
||||
responses = self.client.chat.completions.create(**request_params)
|
||||
|
||||
is_active = True
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = chunk.choices[0].delta if getattr(chunk, "choices", None) else None
|
||||
content = getattr(delta, "content", "") if delta else ""
|
||||
except IndexError:
|
||||
content = ""
|
||||
if content:
|
||||
if "<think>" in content:
|
||||
is_active = False
|
||||
content = content.split("<think>")[0]
|
||||
if "</think>" in content:
|
||||
is_active = True
|
||||
content = content.split("</think>")[-1]
|
||||
if is_active:
|
||||
yield content
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in response generation: {e}")
|
||||
is_active = True
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = chunk.choices[0].delta if getattr(chunk, "choices", None) else None
|
||||
content = getattr(delta, "content", "") if delta else ""
|
||||
except IndexError:
|
||||
content = ""
|
||||
if content:
|
||||
if "<think>" in content:
|
||||
is_active = False
|
||||
content = content.split("<think>")[0]
|
||||
if "</think>" in content:
|
||||
is_active = True
|
||||
content = content.split("</think>")[-1]
|
||||
if is_active:
|
||||
yield content
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None, **kwargs):
|
||||
try:
|
||||
dialogue = self.normalize_dialogue(dialogue)
|
||||
dialogue = self.normalize_dialogue(dialogue)
|
||||
|
||||
request_params = {
|
||||
"model": self.model_name,
|
||||
"messages": dialogue,
|
||||
"stream": True,
|
||||
"tools": functions,
|
||||
}
|
||||
request_params = {
|
||||
"model": self.model_name,
|
||||
"messages": dialogue,
|
||||
"stream": True,
|
||||
"tools": functions,
|
||||
}
|
||||
|
||||
optional_params = {
|
||||
"max_tokens": kwargs.get("max_tokens", self.max_tokens),
|
||||
"temperature": kwargs.get("temperature", self.temperature),
|
||||
"top_p": kwargs.get("top_p", self.top_p),
|
||||
"frequency_penalty": kwargs.get("frequency_penalty", self.frequency_penalty),
|
||||
}
|
||||
optional_params = {
|
||||
"max_tokens": kwargs.get("max_tokens", self.max_tokens),
|
||||
"temperature": kwargs.get("temperature", self.temperature),
|
||||
"top_p": kwargs.get("top_p", self.top_p),
|
||||
"frequency_penalty": kwargs.get("frequency_penalty", self.frequency_penalty),
|
||||
}
|
||||
|
||||
for key, value in optional_params.items():
|
||||
if value is not None:
|
||||
request_params[key] = value
|
||||
for key, value in optional_params.items():
|
||||
if value is not None:
|
||||
request_params[key] = value
|
||||
|
||||
stream = self.client.chat.completions.create(**request_params)
|
||||
stream = self.client.chat.completions.create(**request_params)
|
||||
|
||||
for chunk in stream:
|
||||
if getattr(chunk, "choices", None):
|
||||
delta = chunk.choices[0].delta
|
||||
content = getattr(delta, "content", "")
|
||||
tool_calls = getattr(delta, "tool_calls", None)
|
||||
yield content, tool_calls
|
||||
elif isinstance(getattr(chunk, "usage", None), CompletionUsage):
|
||||
usage_info = getattr(chunk, "usage", None)
|
||||
logger.bind(tag=TAG).info(
|
||||
f"Token 消耗：输入 {getattr(usage_info, 'prompt_tokens', '未知')}，"
|
||||
f"输出 {getattr(usage_info, 'completion_tokens', '未知')}，"
|
||||
f"共计 {getattr(usage_info, 'total_tokens', '未知')}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in function call streaming: {e}")
|
||||
yield f"【OpenAI服务响应异常: {e}】", None
|
||||
for chunk in stream:
|
||||
if getattr(chunk, "choices", None):
|
||||
delta = chunk.choices[0].delta
|
||||
content = getattr(delta, "content", "")
|
||||
tool_calls = getattr(delta, "tool_calls", None)
|
||||
yield content, tool_calls
|
||||
elif isinstance(getattr(chunk, "usage", None), CompletionUsage):
|
||||
usage_info = getattr(chunk, "usage", None)
|
||||
logger.bind(tag=TAG).info(
|
||||
f"Token 消耗：输入 {getattr(usage_info, 'prompt_tokens', '未知')}，"
|
||||
f"输出 {getattr(usage_info, 'completion_tokens', '未知')}，"
|
||||
f"共计 {getattr(usage_info, 'total_tokens', '未知')}"
|
||||
)
|
||||
|
||||
@@ -31,68 +31,55 @@ class LLMProvider(LLMProviderBase):
|
||||
raise
|
||||
|
||||
def response(self, session_id, dialogue, **kwargs):
|
||||
try:
|
||||
logger.bind(tag=TAG).debug(
|
||||
f"Sending request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}"
|
||||
)
|
||||
responses = self.client.chat.completions.create(
|
||||
model=self.model_name, messages=dialogue, stream=True
|
||||
)
|
||||
is_active = True
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else ""
|
||||
if content:
|
||||
if "<think>" in content:
|
||||
is_active = False
|
||||
content = content.split("<think>")[0]
|
||||
if "</think>" in content:
|
||||
is_active = True
|
||||
content = content.split("</think>")[-1]
|
||||
if is_active:
|
||||
yield content
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in Xinference response generation: {e}")
|
||||
yield "【Xinference服务响应异常】"
|
||||
logger.bind(tag=TAG).debug(
|
||||
f"Sending request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}"
|
||||
)
|
||||
responses = self.client.chat.completions.create(
|
||||
model=self.model_name, messages=dialogue, stream=True
|
||||
)
|
||||
is_active = True
|
||||
for chunk in responses:
|
||||
try:
|
||||
delta = (
|
||||
chunk.choices[0].delta
|
||||
if getattr(chunk, "choices", None)
|
||||
else None
|
||||
)
|
||||
content = delta.content if hasattr(delta, "content") else ""
|
||||
if content:
|
||||
if "<think>" in content:
|
||||
is_active = False
|
||||
content = content.split("<think>")[0]
|
||||
if "</think>" in content:
|
||||
is_active = True
|
||||
content = content.split("</think>")[-1]
|
||||
if is_active:
|
||||
yield content
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
|
||||
|
||||
def response_with_functions(self, session_id, dialogue, functions=None):
|
||||
try:
|
||||
logger.bind(tag=TAG).debug(
|
||||
f"Sending function call request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}"
|
||||
)
|
||||
if functions:
|
||||
logger.bind(tag=TAG).debug(
|
||||
f"Sending function call request to Xinference with model: {self.model_name}, dialogue length: {len(dialogue)}"
|
||||
)
|
||||
if functions:
|
||||
logger.bind(tag=TAG).debug(
|
||||
f"Function calls enabled with: {[f.get('function', {}).get('name') for f in functions]}"
|
||||
)
|
||||
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=dialogue,
|
||||
stream=True,
|
||||
tools=functions,
|
||||
f"Function calls enabled with: {[f.get('function', {}).get('name') for f in functions]}"
|
||||
)
|
||||
|
||||
for chunk in stream:
|
||||
delta = chunk.choices[0].delta
|
||||
content = delta.content
|
||||
tool_calls = delta.tool_calls
|
||||
stream = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=dialogue,
|
||||
stream=True,
|
||||
tools=functions,
|
||||
)
|
||||
|
||||
if content:
|
||||
yield content, tool_calls
|
||||
elif tool_calls:
|
||||
yield None, tool_calls
|
||||
for chunk in stream:
|
||||
delta = chunk.choices[0].delta
|
||||
content = delta.content
|
||||
tool_calls = delta.tool_calls
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error in Xinference function call: {e}")
|
||||
yield {
|
||||
"type": "content",
|
||||
"content": f"【Xinference服务响应异常: {str(e)}】",
|
||||
}
|
||||
if content:
|
||||
yield content, tool_calls
|
||||
elif tool_calls:
|
||||
yield None, tool_calls
|
||||
|
||||
@@ -162,19 +162,19 @@ class MemoryProvider(MemoryProviderBase):
|
||||
msgStr += f"当前时间：{time_str}"
|
||||
|
||||
if self.save_to_file:
|
||||
result = self.llm.response_no_stream(
|
||||
short_term_memory_prompt,
|
||||
msgStr,
|
||||
max_tokens=2000,
|
||||
temperature=0.2,
|
||||
)
|
||||
json_str = extract_json_data(result)
|
||||
try:
|
||||
result = self.llm.response_no_stream(
|
||||
short_term_memory_prompt,
|
||||
msgStr,
|
||||
max_tokens=2000,
|
||||
temperature=0.2,
|
||||
)
|
||||
json_str = extract_json_data(result)
|
||||
json.loads(json_str) # 检查json格式是否正确
|
||||
self.short_memory = json_str
|
||||
self.save_memory_to_file()
|
||||
except Exception as e:
|
||||
print("Error:", e)
|
||||
logger.bind(tag=TAG).error(f"Error in saving memory: {e}")
|
||||
else:
|
||||
# 当save_to_file为False时，调用Java端的聊天记录总结接口
|
||||
summary_id = session_id if session_id else self.role_id
|
||||
|
||||
@@ -590,3 +590,14 @@ def validate_mcp_endpoint(mcp_endpoint: str) -> bool:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_system_error_response(config: dict) -> str:
|
||||
"""获取系统错误时的回复
|
||||
|
||||
Args:
|
||||
config: 配置字典
|
||||
|
||||
Returns:
|
||||
str: 系统错误时的回复
|
||||
"""
|
||||
return config.get("system_error_response", "主人，小智现在有点忙，我们稍后再试吧。")
|
||||
Reference in New Issue
Block a user