feat:合并工具调用模型和心流模型
This commit is contained in:
SengokuCola
@@ -78,13 +78,15 @@ class ChatBot:
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groupinfo = message.message_info.group_info
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userinfo = message.message_info.user_info
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if userinfo.user_id in global_config.ban_user_id:
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logger.debug(f"用户{userinfo.user_id}被禁止回复")
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return
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if groupinfo.group_id not in global_config.talk_allowed_groups:
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logger.debug(f"群{groupinfo.group_id}被禁止回复")
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return
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if groupinfo:
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if groupinfo.group_id not in global_config.talk_allowed_groups:
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logger.trace(f"群{groupinfo.group_id}被禁止回复")
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return
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if message.message_info.template_info and not message.message_info.template_info.template_default:
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template_group_name = message.message_info.template_info.template_name
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@@ -327,8 +327,8 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
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# 提取最终的句子内容
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final_sentences = [content for content, sep in merged_segments if content] # 只保留有内容的段
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# 清理可能引入的空字符串
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final_sentences = [s for s in final_sentences if s]
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# 清理可能引入的空字符串和仅包含空白的字符串
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final_sentences = [s for s in final_sentences if s.strip()] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串
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logger.debug(f"分割并合并后的句子: {final_sentences}")
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return final_sentences
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@@ -2,7 +2,7 @@ import asyncio
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import time
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import traceback
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from typing import List, Optional, Dict, Any, TYPE_CHECKING
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import json
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# import json # 移除，因为使用了json_utils
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from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
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from src.plugins.chat.message import MessageSet, Seg # Local import needed after move
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from src.plugins.chat.chat_stream import ChatStream
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@@ -17,6 +17,7 @@ from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator
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from src.do_tool.tool_use import ToolUser
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from ..chat.message_sender import message_manager # <-- Import the global manager
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from src.plugins.chat.emoji_manager import emoji_manager
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from src.plugins.utils.json_utils import extract_tool_call_arguments, safe_json_dumps, process_llm_tool_response # 导入新的JSON工具
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# --- End import ---
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@@ -245,9 +246,6 @@ class HeartFChatting:
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action = planner_result.get("action", "error")
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reasoning = planner_result.get("reasoning", "Planner did not provide reasoning.")
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emoji_query = planner_result.get("emoji_query", "")
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# current_mind = planner_result.get("current_mind", "[Mind unavailable]")
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# send_emoji_from_tools = planner_result.get("send_emoji_from_tools", "") # Emoji from tools
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observed_messages = planner_result.get("observed_messages", [])
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llm_error = planner_result.get("llm_error", False)
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if llm_error:
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@@ -259,7 +257,7 @@ class HeartFChatting:
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elif action == "text_reply":
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logger.debug(f"{log_prefix} HeartFChatting: 麦麦决定回复文本. 理由: {reasoning}")
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action_taken_this_cycle = True
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anchor_message = await self._get_anchor_message(observed_messages)
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anchor_message = await self._get_anchor_message()
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if not anchor_message:
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logger.error(f"{log_prefix} 循环: 无法获取锚点消息用于回复. 跳过周期.")
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else:
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@@ -304,7 +302,7 @@ class HeartFChatting:
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f"{log_prefix} HeartFChatting: 麦麦决定回复表情 ('{emoji_query}'). 理由: {reasoning}"
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)
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action_taken_this_cycle = True
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anchor = await self._get_anchor_message(observed_messages)
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anchor = await self._get_anchor_message()
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if anchor:
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try:
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# --- Handle Emoji (Moved) --- #
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@@ -329,11 +327,6 @@ class HeartFChatting:
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with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer
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wait_start_time = time.monotonic()
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while True:
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# Removed timer check within wait loop
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# async with self._timer_lock:
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# if self._loop_timer <= 0:
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# logger.info(f"{log_prefix} HeartFChatting: 等待新消息时计时器耗尽。")
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# break # 计时器耗尽，退出等待
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# 检查是否有新消息
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has_new = await observation.has_new_messages_since(planner_start_db_time)
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@@ -395,14 +388,6 @@ class HeartFChatting:
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self._processing_lock.release()
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# logger.trace(f"{log_prefix} 循环释放了处理锁.") # Reduce noise
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# --- Timer Decrement Logging Removed ---
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# async with self._timer_lock:
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# self._loop_timer -= cycle_duration
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# # Log timer decrement less aggressively
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# if cycle_duration > 0.1 or not action_taken_this_cycle:
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# logger.debug(
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# f"{log_prefix} HeartFChatting: 周期耗时 {cycle_duration:.2f}s. 剩余时间: {self._loop_timer:.1f}s."
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# )
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if cycle_duration > 0.1:
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logger.debug(f"{log_prefix} HeartFChatting: 周期耗时 {cycle_duration:.2f}s.")
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@@ -437,77 +422,34 @@ class HeartFChatting:
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"""
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log_prefix = self._get_log_prefix()
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observed_messages: List[dict] = []
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tool_result_info = {}
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get_mid_memory_id = []
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# send_emoji_from_tools = "" # Emoji suggested by tools
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current_mind: Optional[str] = None
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llm_error = False # Flag for LLM failure
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# --- Ensure SubHeartflow is available ---
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if not self.sub_hf:
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# Attempt to re-fetch if missing (might happen if initialization order changes)
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self.sub_hf = heartflow.get_subheartflow(self.stream_id)
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if not self.sub_hf:
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logger.error(f"{log_prefix}[Planner] SubHeartflow is not available. Cannot proceed.")
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return {
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"action": "error",
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"reasoning": "SubHeartflow unavailable",
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"llm_error": True,
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"observed_messages": [],
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}
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current_mind: Optional[str] = None
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llm_error = False
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try:
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# Access observation via self.sub_hf
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observation = self.sub_hf._get_primary_observation()
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await observation.observe()
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observed_messages = observation.talking_message
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observed_messages_str = observation.talking_message_str
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except Exception as e:
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logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}")
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# Handle error gracefully, maybe return an error state
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observed_messages_str = "[Error getting observation]"
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# Consider returning error here if observation is critical
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# --- 结束获取观察信息 --- #
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# --- (Moved from _replier_work) 1. 思考前使用工具 --- #
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try:
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# Access tool_user directly
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tool_result = await self.tool_user.use_tool(
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message_txt=observed_messages_str,
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chat_stream=self.chat_stream,
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observation=self.sub_hf._get_primary_observation(),
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)
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if tool_result.get("used_tools", False):
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tool_result_info = tool_result.get("structured_info", {})
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logger.debug(f"{log_prefix}[Planner] 规划前工具结果: {tool_result_info}")
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get_mid_memory_id = [
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mem["content"] for mem in tool_result_info.get("mid_chat_mem", []) if "content" in mem
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]
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except Exception as e_tool:
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logger.error(f"{log_prefix}[Planner] 规划前工具使用失败: {e_tool}")
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# --- 结束工具使用 --- #
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# --- (Moved from _replier_work) 2. SubHeartflow 思考 --- #
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try:
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current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply(
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extra_info=tool_result_info,
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obs_id=get_mid_memory_id,
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)
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# logger.debug(f"{log_prefix}[Planner] SubHF Mind: {current_mind}")
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current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply()
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except Exception as e_subhf:
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logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}")
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current_mind = "[思考时出错]"
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# --- 结束 SubHeartflow 思考 --- #
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# --- 使用 LLM 进行决策 --- #
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action = "no_reply" # Default action
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emoji_query = "" # Default emoji query (used if action is emoji_reply or text_reply with emoji)
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reasoning = "默认决策或获取决策失败"
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action = "no_reply" # 默认动作
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emoji_query = "" # 默认表情查询
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reasoning = "默认决策或获取决策失败"
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llm_error = False # LLM错误标志
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try:
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prompt = await self._build_planner_prompt(observed_messages_str, current_mind)
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prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_hf.structured_info)
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payload = {
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"model": self.planner_llm.model_name,
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"messages": [{"role": "user", "content": prompt}],
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@@ -515,83 +457,70 @@ class HeartFChatting:
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"tool_choice": {"type": "function", "function": {"name": "decide_reply_action"}},
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}
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response = await self.planner_llm._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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)
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# 执行LLM请求
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try:
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response = await self.planner_llm._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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)
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except Exception as req_e:
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logger.error(f"{log_prefix}[Planner] LLM请求执行失败: {req_e}")
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return {
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"action": "error",
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"reasoning": f"LLM请求执行失败: {req_e}",
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"emoji_query": "",
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"current_mind": current_mind,
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"observed_messages": observed_messages,
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"llm_error": True,
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}
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if len(response) == 3:
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_, _, tool_calls = response
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if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
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tool_call = tool_calls[0]
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if (
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tool_call.get("type") == "function"
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and tool_call.get("function", {}).get("name") == "decide_reply_action"
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):
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try:
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arguments = json.loads(tool_call["function"]["arguments"])
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action = arguments.get("action", "no_reply")
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reasoning = arguments.get("reasoning", "未提供理由")
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# Planner explicitly provides emoji query if action is emoji_reply or text_reply wants emoji
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emoji_query = arguments.get("emoji_query", "")
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logger.debug(
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f"{log_prefix}[Planner] LLM Prompt: {prompt}\n决策: {action}, 理由: {reasoning}, EmojiQuery: '{emoji_query}'"
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)
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except json.JSONDecodeError as json_e:
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logger.error(
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f"{log_prefix}[Planner] 解析工具参数失败: {json_e}. Args: {tool_call['function'].get('arguments')}"
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)
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action = "error"
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reasoning = "工具参数解析失败"
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llm_error = True
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except Exception as parse_e:
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logger.error(f"{log_prefix}[Planner] 处理工具参数时出错: {parse_e}")
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action = "error"
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reasoning = "处理工具参数时出错"
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llm_error = True
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else:
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logger.warning(
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f"{log_prefix}[Planner] LLM 未按预期调用 'decide_reply_action' 工具。Tool calls: {tool_calls}"
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)
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action = "error"
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reasoning = "LLM未调用预期工具"
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llm_error = True
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else:
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logger.warning(f"{log_prefix}[Planner] LLM 响应中未包含有效的工具调用。Tool calls: {tool_calls}")
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action = "error"
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reasoning = "LLM响应无工具调用"
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llm_error = True
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# 使用辅助函数处理工具调用响应
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success, arguments, error_msg = process_llm_tool_response(
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response,
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expected_tool_name="decide_reply_action",
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log_prefix=f"{log_prefix}[Planner] "
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)
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if success:
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# 提取决策参数
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action = arguments.get("action", "no_reply")
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reasoning = arguments.get("reasoning", "未提供理由")
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emoji_query = arguments.get("emoji_query", "")
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# 记录决策结果
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logger.debug(
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f"{log_prefix}[Planner] 决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
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)
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else:
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logger.warning(f"{log_prefix}[Planner] LLM 未返回预期的工具调用响应。Response parts: {len(response)}")
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# 处理工具调用失败
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logger.warning(f"{log_prefix}[Planner] {error_msg}")
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action = "error"
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reasoning = "LLM响应格式错误"
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reasoning = error_msg
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llm_error = True
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except Exception as llm_e:
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logger.error(f"{log_prefix}[Planner] Planner LLM 调用失败: {llm_e}")
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# logger.error(traceback.format_exc()) # Maybe too verbose for loop?
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logger.error(f"{log_prefix}[Planner] Planner LLM处理过程中出错: {llm_e}")
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logger.error(traceback.format_exc()) # 记录完整堆栈以便调试
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action = "error"
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reasoning = f"LLM 调用失败: {llm_e}"
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reasoning = f"LLM处理失败: {llm_e}"
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llm_error = True
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# --- 结束 LLM 决策 --- #
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return {
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"action": action,
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"reasoning": reasoning,
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"emoji_query": emoji_query, # Explicit query from Planner/LLM
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"emoji_query": emoji_query,
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"current_mind": current_mind,
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# "send_emoji_from_tools": send_emoji_from_tools, # Emoji suggested by tools (used as fallback)
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"observed_messages": observed_messages,
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"llm_error": llm_error,
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}
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async def _get_anchor_message(self, observed_messages: List[dict]) -> Optional[MessageRecv]:
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async def _get_anchor_message(self) -> Optional[MessageRecv]:
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"""
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重构观察到的最后一条消息作为回复的锚点，
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如果重构失败或观察为空，则创建一个占位符。
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"""
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try:
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# --- Create Placeholder --- #
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placeholder_id = f"mid_pf_{int(time.time() * 1000)}"
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placeholder_user = UserInfo(
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user_id="system_trigger", user_nickname="System Trigger", platform=self.chat_stream.platform
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@@ -652,37 +581,41 @@ class HeartFChatting:
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raise RuntimeError("发送回复失败，_send_response_messages返回None")
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async def shutdown(self):
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"""
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Gracefully shuts down the HeartFChatting instance by cancelling the active loop task.
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"""
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"""优雅关闭HeartFChatting实例，取消活动循环任务"""
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log_prefix = self._get_log_prefix()
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logger.info(f"{log_prefix} Shutting down HeartFChatting...")
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logger.info(f"{log_prefix} 正在关闭HeartFChatting...")
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# 取消循环任务
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if self._loop_task and not self._loop_task.done():
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logger.info(f"{log_prefix} Cancelling active PF loop task.")
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logger.info(f"{log_prefix} 正在取消HeartFChatting循环任务")
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self._loop_task.cancel()
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try:
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await asyncio.wait_for(self._loop_task, timeout=1.0) # Shorter timeout?
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except asyncio.CancelledError:
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logger.info(f"{log_prefix} PF loop task cancelled successfully.")
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except asyncio.TimeoutError:
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logger.warning(f"{log_prefix} Timeout waiting for PF loop task cancellation.")
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await asyncio.wait_for(self._loop_task, timeout=1.0)
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logger.info(f"{log_prefix} HeartFChatting循环任务已取消")
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except (asyncio.CancelledError, asyncio.TimeoutError):
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pass
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except Exception as e:
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logger.error(f"{log_prefix} Error during loop task cancellation: {e}")
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logger.error(f"{log_prefix} 取消循环任务出错: {e}")
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else:
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logger.info(f"{log_prefix} No active PF loop task found to cancel.")
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logger.info(f"{log_prefix} 没有活动的HeartFChatting循环任务")
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# 清理状态
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self._loop_active = False
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self._loop_task = None
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if self._processing_lock.locked():
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logger.warning(f"{log_prefix} Releasing processing lock during shutdown.")
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self._processing_lock.release()
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logger.info(f"{log_prefix} HeartFChatting shutdown complete.")
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logger.warning(f"{log_prefix} 已释放处理锁")
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logger.info(f"{log_prefix} HeartFChatting关闭完成")
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async def _build_planner_prompt(self, observed_messages_str: str, current_mind: Optional[str]) -> str:
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async def _build_planner_prompt(self, observed_messages_str: str, current_mind: Optional[str], structured_info: Dict[str, Any]) -> str:
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"""构建 Planner LLM 的提示词"""
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prompt = f"你的名字是 {global_config.BOT_NICKNAME}。是一个女大二学生，正在QQ聊天，正在决定是否以及如何回应当前的聊天。\n"
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if structured_info:
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prompt += f"以下是一些额外的信息：\n{structured_info}\n"
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if observed_messages_str:
|
||||
prompt += "观察到的最新聊天内容如下 (最近的消息在最后)：\n---\n"
|
||||
prompt += observed_messages_str
|
||||
@@ -726,6 +659,7 @@ class HeartFChatting:
|
||||
response_set: Optional[List[str]] = None
|
||||
try:
|
||||
response_set = await self.gpt_instance.generate_response(
|
||||
structured_info=self.sub_hf.structured_info,
|
||||
current_mind_info=self.sub_hf.current_mind,
|
||||
reason=reason,
|
||||
message=anchor_message, # Pass anchor_message positionally (matches 'message' parameter)
|
||||
|
||||
@@ -39,6 +39,7 @@ class HeartFCGenerator:
|
||||
|
||||
async def generate_response(
|
||||
self,
|
||||
structured_info: str,
|
||||
current_mind_info: str,
|
||||
reason: str,
|
||||
message: MessageRecv,
|
||||
@@ -56,7 +57,7 @@ class HeartFCGenerator:
|
||||
current_model = self.model_normal
|
||||
current_model.temperature = global_config.llm_normal["temp"] * arousal_multiplier # 激活度越高，温度越高
|
||||
model_response = await self._generate_response_with_model(
|
||||
current_mind_info, reason, message, current_model, thinking_id
|
||||
structured_info, current_mind_info, reason, message, current_model, thinking_id
|
||||
)
|
||||
|
||||
if model_response:
|
||||
@@ -71,7 +72,7 @@ class HeartFCGenerator:
|
||||
return None
|
||||
|
||||
async def _generate_response_with_model(
|
||||
self, current_mind_info: str, reason: str, message: MessageRecv, model: LLMRequest, thinking_id: str
|
||||
self, structured_info: str, current_mind_info: str, reason: str, message: MessageRecv, model: LLMRequest, thinking_id: str
|
||||
) -> str:
|
||||
sender_name = ""
|
||||
|
||||
@@ -84,6 +85,7 @@ class HeartFCGenerator:
|
||||
build_mode="focus",
|
||||
reason=reason,
|
||||
current_mind_info=current_mind_info,
|
||||
structured_info=structured_info,
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_name=sender_name,
|
||||
chat_stream=message.chat_stream,
|
||||
@@ -103,106 +105,6 @@ class HeartFCGenerator:
|
||||
|
||||
return content
|
||||
|
||||
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
|
||||
"""提取情感标签，结合立场和情绪"""
|
||||
try:
|
||||
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||
prompt = f"""
|
||||
请严格根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者对被回复者观点的直接立场：
|
||||
- "支持"：明确同意或强化被回复者观点
|
||||
- "反对"：明确反驳或否定被回复者观点
|
||||
- "中立"：不表达明确立场或无关回应
|
||||
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||
4. 考虑回复者的人格设定为{global_config.personality_core}
|
||||
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
输出要求：
|
||||
- 只需输出"立场-情绪"结果，不要解释
|
||||
- 严格基于文字直接表达的对立关系判断
|
||||
"""
|
||||
|
||||
# 调用模型生成结果
|
||||
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||
result = result.strip()
|
||||
|
||||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["支持", "反对", "中立"]
|
||||
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||
if stance in valid_stances and emotion in valid_emotions:
|
||||
return stance, emotion # 返回有效的立场-情绪组合
|
||||
else:
|
||||
logger.debug(f"无效立场-情感组合:{result}")
|
||||
return "中立", "平静" # 默认返回中立-平静
|
||||
else:
|
||||
logger.debug(f"立场-情感格式错误:{result}")
|
||||
return "中立", "平静" # 格式错误时返回默认值
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取情感标签时出错: {e}")
|
||||
return "中立", "平静" # 出错时返回默认值
|
||||
|
||||
async def _get_emotion_tags_with_reason(self, content: str, processed_plain_text: str, reason: str):
|
||||
"""提取情感标签，结合立场和情绪"""
|
||||
try:
|
||||
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||
prompt = f"""
|
||||
请严格根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者对被回复者观点的直接立场：
|
||||
- "支持"：明确同意或强化被回复者观点
|
||||
- "反对"：明确反驳或否定被回复者观点
|
||||
- "中立"：不表达明确立场或无关回应
|
||||
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||
4. 考虑回复者的人格设定为{global_config.personality_core}
|
||||
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
原因：「{reason}」
|
||||
|
||||
输出要求：
|
||||
- 只需输出"立场-情绪"结果，不要解释
|
||||
- 严格基于文字直接表达的对立关系判断
|
||||
"""
|
||||
|
||||
# 调用模型生成结果
|
||||
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||
result = result.strip()
|
||||
|
||||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["支持", "反对", "中立"]
|
||||
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||
if stance in valid_stances and emotion in valid_emotions:
|
||||
return stance, emotion # 返回有效的立场-情绪组合
|
||||
else:
|
||||
logger.debug(f"无效立场-情感组合:{result}")
|
||||
return "中立", "平静" # 默认返回中立-平静
|
||||
else:
|
||||
logger.debug(f"立场-情感格式错误:{result}")
|
||||
return "中立", "平静" # 格式错误时返回默认值
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取情感标签时出错: {e}")
|
||||
return "中立", "平静" # 出错时返回默认值
|
||||
|
||||
async def _process_response(self, content: str) -> List[str]:
|
||||
"""处理响应内容，返回处理后的内容和情感标签"""
|
||||
if not content:
|
||||
|
||||
@@ -21,6 +21,8 @@ logger = get_module_logger("prompt")
|
||||
def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
你有以下信息可供参考：
|
||||
{structured_info}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
|
||||
@@ -79,17 +81,17 @@ class PromptBuilder:
|
||||
self.activate_messages = ""
|
||||
|
||||
async def build_prompt(
|
||||
self, build_mode, reason, current_mind_info, message_txt: str, sender_name: str = "某人", chat_stream=None
|
||||
self, build_mode, reason, current_mind_info, structured_info, message_txt: str, sender_name: str = "某人", chat_stream=None
|
||||
) -> Optional[tuple[str, str]]:
|
||||
if build_mode == "normal":
|
||||
return await self._build_prompt_normal(chat_stream, message_txt, sender_name)
|
||||
|
||||
elif build_mode == "focus":
|
||||
return await self._build_prompt_focus(reason, current_mind_info, chat_stream, message_txt, sender_name)
|
||||
return await self._build_prompt_focus(reason, current_mind_info, structured_info, chat_stream, message_txt, sender_name)
|
||||
return None
|
||||
|
||||
async def _build_prompt_focus(
|
||||
self, reason, current_mind_info, chat_stream, message_txt: str, sender_name: str = "某人"
|
||||
self, reason, current_mind_info, structured_info, chat_stream, message_txt: str, sender_name: str = "某人"
|
||||
) -> tuple[str, str]:
|
||||
individuality = Individuality.get_instance()
|
||||
prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
|
||||
@@ -148,6 +150,7 @@ class PromptBuilder:
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"heart_flow_prompt",
|
||||
structured_info=structured_info,
|
||||
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
|
||||
if chat_in_group
|
||||
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
|
||||
|
||||
@@ -83,6 +83,7 @@ class NormalChatGenerator:
|
||||
build_mode="normal",
|
||||
reason="",
|
||||
current_mind_info="",
|
||||
structured_info="",
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_name=sender_name,
|
||||
chat_stream=message.chat_stream,
|
||||
|
||||
@@ -710,6 +710,8 @@ class LLMRequest:
|
||||
usage = None # 初始化usage变量，避免未定义错误
|
||||
reasoning_content = ""
|
||||
content = ""
|
||||
tool_calls = None # 初始化工具调用变量
|
||||
|
||||
async for line_bytes in response.content:
|
||||
try:
|
||||
line = line_bytes.decode("utf-8").strip()
|
||||
@@ -731,11 +733,20 @@ class LLMRequest:
|
||||
if delta_content is None:
|
||||
delta_content = ""
|
||||
accumulated_content += delta_content
|
||||
|
||||
# 提取工具调用信息
|
||||
if "tool_calls" in delta:
|
||||
if tool_calls is None:
|
||||
tool_calls = delta["tool_calls"]
|
||||
else:
|
||||
# 合并工具调用信息
|
||||
tool_calls.extend(delta["tool_calls"])
|
||||
|
||||
# 检测流式输出文本是否结束
|
||||
finish_reason = chunk["choices"][0].get("finish_reason")
|
||||
if delta.get("reasoning_content", None):
|
||||
reasoning_content += delta["reasoning_content"]
|
||||
if finish_reason == "stop":
|
||||
if finish_reason == "stop" or finish_reason == "tool_calls":
|
||||
chunk_usage = chunk.get("usage", None)
|
||||
if chunk_usage:
|
||||
usage = chunk_usage
|
||||
@@ -763,14 +774,21 @@ class LLMRequest:
|
||||
if think_match:
|
||||
reasoning_content = think_match.group(1).strip()
|
||||
content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
|
||||
|
||||
# 构建消息对象
|
||||
message = {
|
||||
"content": content,
|
||||
"reasoning_content": reasoning_content,
|
||||
}
|
||||
|
||||
# 如果有工具调用，添加到消息中
|
||||
if tool_calls:
|
||||
message["tool_calls"] = tool_calls
|
||||
|
||||
result = {
|
||||
"choices": [
|
||||
{
|
||||
"message": {
|
||||
"content": content,
|
||||
"reasoning_content": reasoning_content,
|
||||
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
|
||||
}
|
||||
"message": message
|
||||
}
|
||||
],
|
||||
"usage": usage,
|
||||
@@ -1046,6 +1064,7 @@ class LLMRequest:
|
||||
|
||||
# 只有当tool_calls存在且不为空时才返回
|
||||
if tool_calls:
|
||||
logger.debug(f"检测到工具调用: {tool_calls}")
|
||||
return content, reasoning_content, tool_calls
|
||||
else:
|
||||
return content, reasoning_content
|
||||
@@ -1109,7 +1128,30 @@ class LLMRequest:
|
||||
|
||||
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
|
||||
# 原样返回响应，不做处理
|
||||
|
||||
return response
|
||||
|
||||
async def generate_response_tool_async(self, prompt: str, tools: list, **kwargs) -> Union[str, Tuple]:
|
||||
"""异步方式根据输入的提示生成模型的响应"""
|
||||
# 构建请求体，不硬编码max_tokens
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
**self.params,
|
||||
**kwargs,
|
||||
"tools": tools
|
||||
}
|
||||
|
||||
logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具")
|
||||
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
|
||||
# 检查响应是否包含工具调用
|
||||
if isinstance(response, tuple) and len(response) == 3:
|
||||
content, reasoning_content, tool_calls = response
|
||||
logger.debug(f"收到工具调用响应，包含 {len(tool_calls) if tool_calls else 0} 个工具调用")
|
||||
return content, reasoning_content, tool_calls
|
||||
else:
|
||||
logger.debug(f"收到普通响应，无工具调用")
|
||||
return response
|
||||
|
||||
async def get_embedding(self, text: str) -> Union[list, None]:
|
||||
"""异步方法：获取文本的embedding向量
|
||||
|
||||
@@ -303,7 +303,7 @@ async def build_readable_messages(
|
||||
)
|
||||
|
||||
readable_read_mark = translate_timestamp_to_human_readable(read_mark, mode=timestamp_mode)
|
||||
read_mark_line = f"\n--- 以上消息已读 (标记时间: {readable_read_mark}) ---\n"
|
||||
read_mark_line = f"\n\n--- 以上消息已读 (标记时间: {readable_read_mark}) ---\n--- 请关注你上次思考之后以下的新消息---\n"
|
||||
|
||||
# 组合结果，确保空部分不引入多余的标记或换行
|
||||
if formatted_before and formatted_after:
|
||||
|
||||
297
src/plugins/utils/json_utils.py
Normal file
297
src/plugins/utils/json_utils.py
Normal file
@@ -0,0 +1,297 @@
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Dict, Optional, TypeVar, Generic, List, Union, Callable, Tuple
|
||||
|
||||
# 定义类型变量用于泛型类型提示
|
||||
T = TypeVar('T')
|
||||
|
||||
# 获取logger
|
||||
logger = logging.getLogger("json_utils")
|
||||
|
||||
def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
|
||||
"""
|
||||
安全地解析JSON字符串，出错时返回默认值
|
||||
|
||||
参数:
|
||||
json_str: 要解析的JSON字符串
|
||||
default_value: 解析失败时返回的默认值
|
||||
|
||||
返回:
|
||||
解析后的Python对象，或在解析失败时返回default_value
|
||||
"""
|
||||
if not json_str:
|
||||
return default_value
|
||||
|
||||
try:
|
||||
return json.loads(json_str)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"JSON解析失败: {e}, JSON字符串: {json_str[:100]}...")
|
||||
return default_value
|
||||
except Exception as e:
|
||||
logger.error(f"JSON解析过程中发生意外错误: {e}")
|
||||
return default_value
|
||||
|
||||
def extract_tool_call_arguments(tool_call: Dict[str, Any],
|
||||
default_value: Dict[str, Any] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
从LLM工具调用对象中提取参数
|
||||
|
||||
参数:
|
||||
tool_call: 工具调用对象字典
|
||||
default_value: 解析失败时返回的默认值
|
||||
|
||||
返回:
|
||||
解析后的参数字典，或在解析失败时返回default_value
|
||||
"""
|
||||
default_result = default_value or {}
|
||||
|
||||
if not tool_call or not isinstance(tool_call, dict):
|
||||
logger.error(f"无效的工具调用对象: {tool_call}")
|
||||
return default_result
|
||||
|
||||
try:
|
||||
# 提取function参数
|
||||
function_data = tool_call.get("function", {})
|
||||
if not function_data or not isinstance(function_data, dict):
|
||||
logger.error(f"工具调用缺少function字段或格式不正确: {tool_call}")
|
||||
return default_result
|
||||
|
||||
# 提取arguments
|
||||
arguments_str = function_data.get("arguments", "{}")
|
||||
if not arguments_str:
|
||||
return default_result
|
||||
|
||||
# 解析JSON
|
||||
return safe_json_loads(arguments_str, default_result)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取工具调用参数时出错: {e}")
|
||||
return default_result
|
||||
|
||||
def get_json_value(json_obj: Dict[str, Any], key_path: str,
|
||||
default_value: T = None,
|
||||
transform_func: Callable[[Any], T] = None) -> Union[Any, T]:
|
||||
"""
|
||||
从JSON对象中按照路径提取值，支持点表示法路径，如"data.items.0.name"
|
||||
|
||||
参数:
|
||||
json_obj: JSON对象(已解析的字典)
|
||||
key_path: 键路径，使用点表示法，如"data.items.0.name"
|
||||
default_value: 获取失败时返回的默认值
|
||||
transform_func: 可选的转换函数，用于对获取的值进行转换
|
||||
|
||||
返回:
|
||||
路径指向的值，或在获取失败时返回default_value
|
||||
"""
|
||||
if not json_obj or not key_path:
|
||||
return default_value
|
||||
|
||||
try:
|
||||
# 分割路径
|
||||
keys = key_path.split(".")
|
||||
current = json_obj
|
||||
|
||||
# 遍历路径
|
||||
for key in keys:
|
||||
# 处理数组索引
|
||||
if key.isdigit() and isinstance(current, list):
|
||||
index = int(key)
|
||||
if 0 <= index < len(current):
|
||||
current = current[index]
|
||||
else:
|
||||
return default_value
|
||||
# 处理字典键
|
||||
elif isinstance(current, dict):
|
||||
if key in current:
|
||||
current = current[key]
|
||||
else:
|
||||
return default_value
|
||||
else:
|
||||
return default_value
|
||||
|
||||
# 应用转换函数(如果提供)
|
||||
if transform_func and current is not None:
|
||||
return transform_func(current)
|
||||
return current
|
||||
except Exception as e:
|
||||
logger.error(f"从JSON获取值时出错: {e}, 路径: {key_path}")
|
||||
return default_value
|
||||
|
||||
def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False,
|
||||
pretty: bool = False) -> str:
|
||||
"""
|
||||
安全地将Python对象序列化为JSON字符串
|
||||
|
||||
参数:
|
||||
obj: 要序列化的Python对象
|
||||
default_value: 序列化失败时返回的默认值
|
||||
ensure_ascii: 是否确保ASCII编码(默认False，允许中文等非ASCII字符)
|
||||
pretty: 是否美化输出JSON
|
||||
|
||||
返回:
|
||||
序列化后的JSON字符串，或在序列化失败时返回default_value
|
||||
"""
|
||||
try:
|
||||
indent = 2 if pretty else None
|
||||
return json.dumps(obj, ensure_ascii=ensure_ascii, indent=indent)
|
||||
except TypeError as e:
|
||||
logger.error(f"JSON序列化失败(类型错误): {e}")
|
||||
return default_value
|
||||
except Exception as e:
|
||||
logger.error(f"JSON序列化过程中发生意外错误: {e}")
|
||||
return default_value
|
||||
|
||||
def merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
合并多个JSON对象(字典)
|
||||
|
||||
参数:
|
||||
*objects: 要合并的JSON对象(字典)
|
||||
|
||||
返回:
|
||||
合并后的字典，后面的对象会覆盖前面对象的相同键
|
||||
"""
|
||||
result = {}
|
||||
for obj in objects:
|
||||
if obj and isinstance(obj, dict):
|
||||
result.update(obj)
|
||||
return result
|
||||
|
||||
def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, List[Any], str]:
|
||||
"""
|
||||
标准化LLM响应格式，将各种格式（如元组）转换为统一的列表格式
|
||||
|
||||
参数:
|
||||
response: 原始LLM响应
|
||||
log_prefix: 日志前缀
|
||||
|
||||
返回:
|
||||
元组 (成功标志, 标准化后的响应列表, 错误消息)
|
||||
"""
|
||||
# 检查是否为None
|
||||
if response is None:
|
||||
return False, [], "LLM响应为None"
|
||||
|
||||
# 记录原始类型
|
||||
logger.debug(f"{log_prefix}LLM响应原始类型: {type(response).__name__}")
|
||||
|
||||
# 将元组转换为列表
|
||||
if isinstance(response, tuple):
|
||||
logger.debug(f"{log_prefix}将元组响应转换为列表")
|
||||
response = list(response)
|
||||
|
||||
# 确保是列表类型
|
||||
if not isinstance(response, list):
|
||||
return False, [], f"无法处理的LLM响应类型: {type(response).__name__}"
|
||||
|
||||
# 处理工具调用部分（如果存在）
|
||||
if len(response) == 3:
|
||||
content, reasoning, tool_calls = response
|
||||
|
||||
# 将工具调用部分转换为列表（如果是元组）
|
||||
if isinstance(tool_calls, tuple):
|
||||
logger.debug(f"{log_prefix}将工具调用元组转换为列表")
|
||||
tool_calls = list(tool_calls)
|
||||
response[2] = tool_calls
|
||||
|
||||
return True, response, ""
|
||||
|
||||
def process_llm_tool_calls(response: List[Any], log_prefix: str = "") -> Tuple[bool, List[Dict[str, Any]], str]:
|
||||
"""
|
||||
处理并提取LLM响应中的工具调用列表
|
||||
|
||||
参数:
|
||||
response: 标准化后的LLM响应列表
|
||||
log_prefix: 日志前缀
|
||||
|
||||
返回:
|
||||
元组 (成功标志, 工具调用列表, 错误消息)
|
||||
"""
|
||||
# 确保响应格式正确
|
||||
if len(response) != 3:
|
||||
return False, [], f"LLM响应元素数量不正确: 预期3个元素，实际{len(response)}个"
|
||||
|
||||
# 提取工具调用部分
|
||||
tool_calls = response[2]
|
||||
|
||||
# 检查工具调用是否有效
|
||||
if tool_calls is None:
|
||||
return False, [], "工具调用部分为None"
|
||||
|
||||
if not isinstance(tool_calls, list):
|
||||
return False, [], f"工具调用部分不是列表: {type(tool_calls).__name__}"
|
||||
|
||||
if len(tool_calls) == 0:
|
||||
return False, [], "工具调用列表为空"
|
||||
|
||||
# 检查工具调用是否格式正确
|
||||
valid_tool_calls = []
|
||||
for i, tool_call in enumerate(tool_calls):
|
||||
if not isinstance(tool_call, dict):
|
||||
logger.warning(f"{log_prefix}工具调用[{i}]不是字典: {type(tool_call).__name__}")
|
||||
continue
|
||||
|
||||
if tool_call.get("type") != "function":
|
||||
logger.warning(f"{log_prefix}工具调用[{i}]不是函数类型: {tool_call.get('type', '未知')}")
|
||||
continue
|
||||
|
||||
if "function" not in tool_call or not isinstance(tool_call["function"], dict):
|
||||
logger.warning(f"{log_prefix}工具调用[{i}]缺少function字段或格式不正确")
|
||||
continue
|
||||
|
||||
valid_tool_calls.append(tool_call)
|
||||
|
||||
# 检查是否有有效的工具调用
|
||||
if not valid_tool_calls:
|
||||
return False, [], "没有找到有效的工具调用"
|
||||
|
||||
return True, valid_tool_calls, ""
|
||||
|
||||
def process_llm_tool_response(
|
||||
response: Any,
|
||||
expected_tool_name: str = None,
|
||||
log_prefix: str = ""
|
||||
) -> Tuple[bool, Dict[str, Any], str]:
|
||||
"""
|
||||
处理LLM返回的工具调用响应，进行常见错误检查并提取参数
|
||||
|
||||
参数:
|
||||
response: LLM的响应，预期是[content, reasoning, tool_calls]格式的列表或元组
|
||||
expected_tool_name: 预期的工具名称，如不指定则不检查
|
||||
log_prefix: 日志前缀，用于标识日志来源
|
||||
|
||||
返回:
|
||||
三元组(成功标志, 参数字典, 错误描述)
|
||||
- 如果成功解析，返回(True, 参数字典, "")
|
||||
- 如果解析失败，返回(False, {}, 错误描述)
|
||||
"""
|
||||
# 使用新的标准化函数
|
||||
success, normalized_response, error_msg = normalize_llm_response(response, log_prefix)
|
||||
if not success:
|
||||
return False, {}, error_msg
|
||||
|
||||
# 使用新的工具调用处理函数
|
||||
success, valid_tool_calls, error_msg = process_llm_tool_calls(normalized_response, log_prefix)
|
||||
if not success:
|
||||
return False, {}, error_msg
|
||||
|
||||
# 检查是否有工具调用
|
||||
if not valid_tool_calls:
|
||||
return False, {}, "没有有效的工具调用"
|
||||
|
||||
# 获取第一个工具调用
|
||||
tool_call = valid_tool_calls[0]
|
||||
|
||||
# 检查工具名称(如果提供了预期名称)
|
||||
if expected_tool_name:
|
||||
actual_name = tool_call.get("function", {}).get("name")
|
||||
if actual_name != expected_tool_name:
|
||||
return False, {}, f"工具名称不匹配: 预期'{expected_tool_name}'，实际'{actual_name}'"
|
||||
|
||||
# 提取并解析参数
|
||||
try:
|
||||
arguments = extract_tool_call_arguments(tool_call, {})
|
||||
return True, arguments, ""
|
||||
except Exception as e:
|
||||
logger.error(f"{log_prefix}解析工具参数时出错: {e}")
|
||||
return False, {}, f"解析参数失败: {str(e)}"
|
||||
Reference in New Issue
Block a user