mirror of https://github.com/Mai-with-u/MaiBot.git
🤖 自动格式化代码 [skip ci]
github-actions[bot]
parent
5a7c54acef
commit
3304dc6b29
|
|
@ -4,6 +4,7 @@ from src.plugins.memory_system.Hippocampus import HippocampusManager
|
|||
from src.plugins.knowledge.knowledge_lib import qa_manager
|
||||
from src.common.database import db
|
||||
from src.plugins.chat.utils import get_embedding
|
||||
|
||||
# import jieba # 如果需要旧版知识库的回退，可能需要
|
||||
# import re # 如果需要旧版知识库的回退，可能需要
|
||||
from src.common.logger_manager import get_logger
|
||||
|
|
@ -124,7 +125,7 @@ class ActionPlanner:
|
|||
self.name = global_config.BOT_NICKNAME
|
||||
self.private_name = private_name
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
|
||||
|
||||
async def _get_memory_info(self, text: str) -> str:
|
||||
"""根据文本自动检索相关记忆"""
|
||||
memory_prompt = ""
|
||||
|
|
@ -132,18 +133,20 @@ class ActionPlanner:
|
|||
try:
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=text,
|
||||
max_memory_num=2, # 最多获取 2 条记忆
|
||||
max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
|
||||
max_depth=3, # 搜索深度
|
||||
fast_retrieval=False # 是否快速检索
|
||||
max_memory_num=2, # 最多获取 2 条记忆
|
||||
max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
|
||||
max_depth=3, # 搜索深度
|
||||
fast_retrieval=False, # 是否快速检索
|
||||
)
|
||||
if related_memory:
|
||||
for memory in related_memory:
|
||||
# memory[0] 是记忆ID, memory[1] 是记忆内容
|
||||
related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
|
||||
related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
|
||||
if related_memory_info:
|
||||
memory_prompt = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，供参考)\n"
|
||||
logger.debug(f"[私聊]决策层[{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}...")
|
||||
logger.debug(
|
||||
f"[私聊]决策层[{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}..."
|
||||
)
|
||||
else:
|
||||
logger.debug(f"[私聊]决策层[{self.private_name}]自动检索记忆返回为空。")
|
||||
else:
|
||||
|
|
@ -179,9 +182,11 @@ class ActionPlanner:
|
|||
return ""
|
||||
|
||||
# 调用我们之前添加的 get_info_from_db 函数
|
||||
results = get_info_from_db(embedding, limit=5, threshold=threshold, return_raw=True) # 最多查 5 条
|
||||
results = get_info_from_db(embedding, limit=5, threshold=threshold, return_raw=True) # 最多查 5 条
|
||||
|
||||
logger.info(f"[私聊][{self.private_name}]旧版知识库查询完成，耗时: {time.time() - start_time:.3f}秒，获取{len(results)}条结果")
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}]旧版知识库查询完成，耗时: {time.time() - start_time:.3f}秒，获取{len(results)}条结果"
|
||||
)
|
||||
|
||||
# 去重和格式化
|
||||
unique_contents = set()
|
||||
|
|
@ -231,7 +236,9 @@ class ActionPlanner:
|
|||
else:
|
||||
logger.debug(f"[私聊][{self.private_name}]LPMM 知识库未返回有效知识，尝试旧版数据库检索。")
|
||||
except Exception as e:
|
||||
logger.error(f"[私聊][{self.private_name}]调用 LPMM 知识库 (qa_manager.get_knowledge) 时发生异常: {str(e)}，尝试旧版数据库检索。")
|
||||
logger.error(
|
||||
f"[私聊][{self.private_name}]调用 LPMM 知识库 (qa_manager.get_knowledge) 时发生异常: {str(e)}，尝试旧版数据库检索。"
|
||||
)
|
||||
|
||||
# 2. 如果 LPMM 失败或无结果，尝试旧版数据库
|
||||
try:
|
||||
|
|
@ -246,11 +253,15 @@ class ActionPlanner:
|
|||
logger.debug(f"[私聊][{self.private_name}]旧版数据库也未检索到有效知识。")
|
||||
|
||||
except Exception as e2:
|
||||
logger.error(f"[私聊][{self.private_name}]调用旧版知识库检索 (_get_prompt_info_old) 时也发生异常: {str(e2)}")
|
||||
logger.error(
|
||||
f"[私聊][{self.private_name}]调用旧版知识库检索 (_get_prompt_info_old) 时也发生异常: {str(e2)}"
|
||||
)
|
||||
|
||||
# 如果两种方法都失败或无结果
|
||||
logger.info(f"[私聊][{self.private_name}]自动知识检索总耗时: {time.time() - start_time:.3f}秒，未找到任何相关知识。")
|
||||
return "" # 返回空字符串
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}]自动知识检索总耗时: {time.time() - start_time:.3f}秒，未找到任何相关知识。"
|
||||
)
|
||||
return "" # 返回空字符串
|
||||
|
||||
# 修改 plan 方法签名，增加 last_successful_reply_action 参数
|
||||
async def plan(
|
||||
|
|
@ -362,36 +373,36 @@ class ActionPlanner:
|
|||
# --- 知识信息字符串构建开始 ---
|
||||
# knowledge_info_str = "【已获取的相关知识和记忆】\n"
|
||||
# try:
|
||||
# 检查 conversation_info 是否有 knowledge_list 并且不为空
|
||||
# if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
|
||||
# 最多只显示最近的 5 条知识，防止 Prompt 过长
|
||||
# recent_knowledge = conversation_info.knowledge_list[-5:]
|
||||
# for i, knowledge_item in enumerate(recent_knowledge):
|
||||
# if isinstance(knowledge_item, dict):
|
||||
# query = knowledge_item.get("query", "未知查询")
|
||||
# knowledge = knowledge_item.get("knowledge", "无知识内容")
|
||||
# source = knowledge_item.get("source", "未知来源")
|
||||
# 只取知识内容的前 2000 个字，避免太长
|
||||
# knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
|
||||
# knowledge_info_str += (
|
||||
# f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n"
|
||||
# )
|
||||
# else:
|
||||
# 处理列表里不是字典的异常情况
|
||||
# knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
|
||||
# 检查 conversation_info 是否有 knowledge_list 并且不为空
|
||||
# if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
|
||||
# 最多只显示最近的 5 条知识，防止 Prompt 过长
|
||||
# recent_knowledge = conversation_info.knowledge_list[-5:]
|
||||
# for i, knowledge_item in enumerate(recent_knowledge):
|
||||
# if isinstance(knowledge_item, dict):
|
||||
# query = knowledge_item.get("query", "未知查询")
|
||||
# knowledge = knowledge_item.get("knowledge", "无知识内容")
|
||||
# source = knowledge_item.get("source", "未知来源")
|
||||
# 只取知识内容的前 2000 个字，避免太长
|
||||
# knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
|
||||
# knowledge_info_str += (
|
||||
# f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n"
|
||||
# )
|
||||
# else:
|
||||
# 处理列表里不是字典的异常情况
|
||||
# knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
|
||||
|
||||
# if not recent_knowledge: # 如果 knowledge_list 存在但为空
|
||||
# knowledge_info_str += "- 暂无相关知识和记忆。\n"
|
||||
# if not recent_knowledge: # 如果 knowledge_list 存在但为空
|
||||
# knowledge_info_str += "- 暂无相关知识和记忆。\n"
|
||||
|
||||
# else:
|
||||
# 如果 conversation_info 没有 knowledge_list 属性，或者列表为空
|
||||
# knowledge_info_str += "- 暂无相关知识记忆。\n"
|
||||
# else:
|
||||
# 如果 conversation_info 没有 knowledge_list 属性，或者列表为空
|
||||
# knowledge_info_str += "- 暂无相关知识记忆。\n"
|
||||
# except AttributeError:
|
||||
# logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
|
||||
# knowledge_info_str += "- 获取知识列表时出错。\n"
|
||||
# logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
|
||||
# knowledge_info_str += "- 获取知识列表时出错。\n"
|
||||
# except Exception as e:
|
||||
# logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
|
||||
# knowledge_info_str += "- 处理知识列表时出错。\n"
|
||||
# logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
|
||||
# knowledge_info_str += "- 处理知识列表时出错。\n"
|
||||
# --- 知识信息字符串构建结束 ---
|
||||
|
||||
# 获取聊天历史记录 (chat_history_text)
|
||||
|
|
@ -503,16 +514,20 @@ class ActionPlanner:
|
|||
|
||||
retrieved_memory_str_planner = ""
|
||||
retrieved_knowledge_str_planner = ""
|
||||
retrieval_context = chat_history_text # 使用聊天记录作为检索上下文
|
||||
retrieval_context = chat_history_text # 使用聊天记录作为检索上下文
|
||||
if retrieval_context and retrieval_context != "还没有聊天记录。" and retrieval_context != "[构建聊天记录出错]":
|
||||
try:
|
||||
logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 开始自动检索记忆...")
|
||||
retrieved_memory_str_planner = await self._get_memory_info(text=retrieval_context)
|
||||
logger.info(f"[私聊][{self.private_name}] (ActionPlanner) 自动检索记忆 {'完成' if retrieved_memory_str_planner else '无结果'}。")
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}] (ActionPlanner) 自动检索记忆 {'完成' if retrieved_memory_str_planner else '无结果'}。"
|
||||
)
|
||||
|
||||
logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 开始自动知识检索...")
|
||||
retrieved_knowledge_str_planner = await self._get_prompt_info(message=retrieval_context)
|
||||
logger.info(f"[私聊][{self.private_name}] (ActionPlanner) 自动检索知识 {'完成' if retrieved_knowledge_str_planner else '无结果'}。")
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}] (ActionPlanner) 自动检索知识 {'完成' if retrieved_knowledge_str_planner else '无结果'}。"
|
||||
)
|
||||
except Exception as retrieval_err:
|
||||
logger.error(f"[私聊][{self.private_name}] (ActionPlanner) 自动检索时出错: {retrieval_err}")
|
||||
retrieved_memory_str_planner = "检索记忆时出错。\n"
|
||||
|
|
@ -541,7 +556,9 @@ class ActionPlanner:
|
|||
chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。",
|
||||
# knowledge_info_str=knowledge_info_str,
|
||||
retrieved_memory_str=retrieved_memory_str_planner if retrieved_memory_str_planner else "无相关记忆。",
|
||||
retrieved_knowledge_str=retrieved_knowledge_str_planner if retrieved_knowledge_str_planner else "无相关知识。"
|
||||
retrieved_knowledge_str=retrieved_knowledge_str_planner
|
||||
if retrieved_knowledge_str_planner
|
||||
else "无相关知识。",
|
||||
)
|
||||
|
||||
logger.debug(f"[私聊][{self.private_name}]发送到LLM的最终提示词:\n------\n{prompt}\n------")
|
||||
|
|
@ -644,7 +661,8 @@ class ActionPlanner:
|
|||
# 外层异常处理保持不变
|
||||
logger.error(f"[私聊][{self.private_name}]规划行动时调用 LLM 或处理结果出错: {str(e)}")
|
||||
return "wait", f"行动规划处理中发生错误，暂时等待: {str(e)}"
|
||||
|
||||
|
||||
|
||||
def get_info_from_db(
|
||||
query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
|
||||
) -> Union[str, list]:
|
||||
|
|
@ -696,7 +714,13 @@ def get_info_from_db(
|
|||
}
|
||||
},
|
||||
# 防止除以零错误，添加一个小的 epsilon
|
||||
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$max": [{"$multiply": ["$magnitude1", "$magnitude2"]}, 1e-9]}]}}},
|
||||
{
|
||||
"$addFields": {
|
||||
"similarity": {
|
||||
"$divide": ["$dotProduct", {"$max": [{"$multiply": ["$magnitude1", "$magnitude2"]}, 1e-9]}]
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"$match": {
|
||||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||||
|
|
@ -723,4 +747,3 @@ def get_info_from_db(
|
|||
else:
|
||||
# 返回所有找到的内容，用换行分隔
|
||||
return "\n".join(str(result["content"]) for result in results)
|
||||
|
||||
|
|
|
|||
|
|
@ -506,30 +506,30 @@ class Conversation:
|
|||
conversation_info.done_action.append(wait_action_record)
|
||||
|
||||
# elif action == "fetch_knowledge":
|
||||
# self.state = ConversationState.FETCHING
|
||||
# knowledge_query = reason
|
||||
# try:
|
||||
# 检查 knowledge_fetcher 是否存在
|
||||
# if not hasattr(self, "knowledge_fetcher"):
|
||||
# logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。")
|
||||
# raise AttributeError("KnowledgeFetcher not initialized")
|
||||
# self.state = ConversationState.FETCHING
|
||||
# knowledge_query = reason
|
||||
# try:
|
||||
# 检查 knowledge_fetcher 是否存在
|
||||
# if not hasattr(self, "knowledge_fetcher"):
|
||||
# logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。")
|
||||
# raise AttributeError("KnowledgeFetcher not initialized")
|
||||
|
||||
# knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
|
||||
# logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
|
||||
# if knowledge:
|
||||
# 确保 knowledge_list 存在
|
||||
# if not hasattr(conversation_info, "knowledge_list"):
|
||||
# conversation_info.knowledge_list = []
|
||||
# conversation_info.knowledge_list.append(
|
||||
# {"query": knowledge_query, "knowledge": knowledge, "source": source}
|
||||
# )
|
||||
# action_successful = True
|
||||
# except Exception as fetch_err:
|
||||
# logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
|
||||
# conversation_info.done_action[action_index].update(
|
||||
# {"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
|
||||
# )
|
||||
# self.conversation_info.last_successful_reply_action = None # 重置状态
|
||||
# knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
|
||||
# logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
|
||||
# if knowledge:
|
||||
# 确保 knowledge_list 存在
|
||||
# if not hasattr(conversation_info, "knowledge_list"):
|
||||
# conversation_info.knowledge_list = []
|
||||
# conversation_info.knowledge_list.append(
|
||||
# {"query": knowledge_query, "knowledge": knowledge, "source": source}
|
||||
# )
|
||||
# action_successful = True
|
||||
# except Exception as fetch_err:
|
||||
# logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
|
||||
# conversation_info.done_action[action_index].update(
|
||||
# {"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
|
||||
# )
|
||||
# self.conversation_info.last_successful_reply_action = None # 重置状态
|
||||
|
||||
elif action == "rethink_goal":
|
||||
self.state = ConversationState.RETHINKING
|
||||
|
|
|
|||
|
|
@ -1,15 +1,19 @@
|
|||
# 用于访问记忆系统
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
|
||||
# 用于访问新的知识库 (LPMM)
|
||||
from src.plugins.knowledge.knowledge_lib import qa_manager
|
||||
|
||||
# 用于访问数据库 (旧知识库需要)
|
||||
from src.common.database import db
|
||||
|
||||
# 用于获取文本的嵌入向量 (旧知识库需要)
|
||||
from src.plugins.chat.utils import get_embedding
|
||||
|
||||
# 可能用于旧知识库提取主题 (如果需要回退到旧方法)
|
||||
# import jieba # 如果报错说找不到 jieba，可能需要安装: pip install jieba
|
||||
# import re # 正则表达式库，通常 Python 自带
|
||||
from typing import Tuple, List, Dict, Any,Union
|
||||
from typing import Tuple, List, Dict, Any, Union
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
|
|
@ -116,6 +120,7 @@ class ReplyGenerator:
|
|||
self.private_name = private_name
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
self.reply_checker = ReplyChecker(stream_id, private_name)
|
||||
|
||||
async def _get_memory_info(self, text: str) -> str:
|
||||
"""根据文本自动检索相关记忆"""
|
||||
memory_prompt = ""
|
||||
|
|
@ -123,15 +128,15 @@ class ReplyGenerator:
|
|||
try:
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=text,
|
||||
max_memory_num=2, # 最多获取 2 条记忆
|
||||
max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
|
||||
max_depth=3, # 搜索深度
|
||||
fast_retrieval=False # 是否快速检索
|
||||
max_memory_num=2, # 最多获取 2 条记忆
|
||||
max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
|
||||
max_depth=3, # 搜索深度
|
||||
fast_retrieval=False, # 是否快速检索
|
||||
)
|
||||
if related_memory:
|
||||
for memory in related_memory:
|
||||
# memory[0] 是记忆ID, memory[1] 是记忆内容
|
||||
related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
|
||||
related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
|
||||
if related_memory_info:
|
||||
memory_prompt = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，不一定是目前聊天里的人说的，回忆中别人说的事情也不一定是准确的，请记住)\n"
|
||||
logger.debug(f"[私聊][{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}...")
|
||||
|
|
@ -170,9 +175,11 @@ class ReplyGenerator:
|
|||
return ""
|
||||
|
||||
# 调用我们之前添加的 get_info_from_db 函数
|
||||
results = get_info_from_db(embedding, limit=5, threshold=threshold, return_raw=True) # 最多查 5 条
|
||||
results = get_info_from_db(embedding, limit=5, threshold=threshold, return_raw=True) # 最多查 5 条
|
||||
|
||||
logger.info(f"[私聊][{self.private_name}]旧版知识库查询完成，耗时: {time.time() - start_time:.3f}秒，获取{len(results)}条结果")
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}]旧版知识库查询完成，耗时: {time.time() - start_time:.3f}秒，获取{len(results)}条结果"
|
||||
)
|
||||
|
||||
# 去重和格式化
|
||||
unique_contents = set()
|
||||
|
|
@ -222,7 +229,9 @@ class ReplyGenerator:
|
|||
else:
|
||||
logger.debug(f"[私聊][{self.private_name}]LPMM 知识库未返回有效知识，尝试旧版数据库检索。")
|
||||
except Exception as e:
|
||||
logger.error(f"[私聊][{self.private_name}]调用 LPMM 知识库 (qa_manager.get_knowledge) 时发生异常: {str(e)}，尝试旧版数据库检索。")
|
||||
logger.error(
|
||||
f"[私聊][{self.private_name}]调用 LPMM 知识库 (qa_manager.get_knowledge) 时发生异常: {str(e)}，尝试旧版数据库检索。"
|
||||
)
|
||||
|
||||
# 2. 如果 LPMM 失败或无结果，尝试旧版数据库
|
||||
try:
|
||||
|
|
@ -237,11 +246,15 @@ class ReplyGenerator:
|
|||
logger.debug(f"[私聊][{self.private_name}]旧版数据库也未检索到有效知识。")
|
||||
|
||||
except Exception as e2:
|
||||
logger.error(f"[私聊][{self.private_name}]调用旧版知识库检索 (_get_prompt_info_old) 时也发生异常: {str(e2)}")
|
||||
logger.error(
|
||||
f"[私聊][{self.private_name}]调用旧版知识库检索 (_get_prompt_info_old) 时也发生异常: {str(e2)}"
|
||||
)
|
||||
# 如果两种方法都失败或无结果
|
||||
logger.info(f"[私聊][{self.private_name}]自动知识检索总耗时: {time.time() - start_time:.3f}秒，未找到任何相关知识。")
|
||||
return "" # 返回空字符串
|
||||
|
||||
logger.info(
|
||||
f"[私聊][{self.private_name}]自动知识检索总耗时: {time.time() - start_time:.3f}秒，未找到任何相关知识。"
|
||||
)
|
||||
return "" # 返回空字符串
|
||||
|
||||
# 修改 generate 方法签名，增加 action_type 参数
|
||||
async def generate(
|
||||
self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
|
||||
|
|
@ -284,34 +297,34 @@ class ReplyGenerator:
|
|||
# --- 新增：构建知识信息字符串 ---
|
||||
# knowledge_info_str = "【供参考的相关知识和记忆】\n" # 稍微改下标题，表明是供参考
|
||||
# try:
|
||||
# 检查 conversation_info 是否有 knowledge_list 并且不为空
|
||||
# if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
|
||||
# 最多只显示最近的 5 条知识
|
||||
# recent_knowledge = conversation_info.knowledge_list[-5:]
|
||||
# for i, knowledge_item in enumerate(recent_knowledge):
|
||||
# if isinstance(knowledge_item, dict):
|
||||
# query = knowledge_item.get("query", "未知查询")
|
||||
# knowledge = knowledge_item.get("knowledge", "无知识内容")
|
||||
# source = knowledge_item.get("source", "未知来源")
|
||||
# 只取知识内容的前 2000 个字
|
||||
# knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
|
||||
# knowledge_info_str += (
|
||||
# f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n" # 格式微调，更简洁
|
||||
# )
|
||||
# else:
|
||||
# knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
|
||||
# 检查 conversation_info 是否有 knowledge_list 并且不为空
|
||||
# if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
|
||||
# 最多只显示最近的 5 条知识
|
||||
# recent_knowledge = conversation_info.knowledge_list[-5:]
|
||||
# for i, knowledge_item in enumerate(recent_knowledge):
|
||||
# if isinstance(knowledge_item, dict):
|
||||
# query = knowledge_item.get("query", "未知查询")
|
||||
# knowledge = knowledge_item.get("knowledge", "无知识内容")
|
||||
# source = knowledge_item.get("source", "未知来源")
|
||||
# 只取知识内容的前 2000 个字
|
||||
# knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
|
||||
# knowledge_info_str += (
|
||||
# f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n" # 格式微调，更简洁
|
||||
# )
|
||||
# else:
|
||||
# knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
|
||||
|
||||
# if not recent_knowledge:
|
||||
# knowledge_info_str += "- 暂无。\n" # 更简洁的提示
|
||||
# if not recent_knowledge:
|
||||
# knowledge_info_str += "- 暂无。\n" # 更简洁的提示
|
||||
|
||||
# else:
|
||||
# knowledge_info_str += "- 暂无。\n"
|
||||
# else:
|
||||
# knowledge_info_str += "- 暂无。\n"
|
||||
# except AttributeError:
|
||||
# logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
|
||||
# knowledge_info_str += "- 获取知识列表时出错。\n"
|
||||
# logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
|
||||
# knowledge_info_str += "- 获取知识列表时出错。\n"
|
||||
# except Exception as e:
|
||||
# logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
|
||||
# knowledge_info_str += "- 处理知识列表时出错。\n"
|
||||
# logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
|
||||
# knowledge_info_str += "- 处理知识列表时出错。\n"
|
||||
|
||||
# 获取聊天历史记录 (chat_history_text)
|
||||
chat_history_text = observation_info.chat_history_str
|
||||
|
|
@ -378,8 +391,10 @@ class ReplyGenerator:
|
|||
goals_str=goals_str,
|
||||
chat_history_text=chat_history_text,
|
||||
# knowledge_info_str=knowledge_info_str,
|
||||
retrieved_memory_str=retrieved_memory_str if retrieved_memory_str else "无相关记忆。", # 如果为空则提示无
|
||||
retrieved_knowledge_str=retrieved_knowledge_str if retrieved_knowledge_str else "无相关知识。" # 如果为空则提示无
|
||||
retrieved_memory_str=retrieved_memory_str if retrieved_memory_str else "无相关记忆。", # 如果为空则提示无
|
||||
retrieved_knowledge_str=retrieved_knowledge_str
|
||||
if retrieved_knowledge_str
|
||||
else "无相关知识。", # 如果为空则提示无
|
||||
)
|
||||
|
||||
# --- 调用 LLM 生成 ---
|
||||
|
|
@ -403,6 +418,7 @@ class ReplyGenerator:
|
|||
"""
|
||||
return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count)
|
||||
|
||||
|
||||
def get_info_from_db(
|
||||
query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
|
||||
) -> Union[str, list]:
|
||||
|
|
@ -454,7 +470,13 @@ def get_info_from_db(
|
|||
}
|
||||
},
|
||||
# 防止除以零错误，添加一个小的 epsilon
|
||||
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$max": [{"$multiply": ["$magnitude1", "$magnitude2"]}, 1e-9]}]}}},
|
||||
{
|
||||
"$addFields": {
|
||||
"similarity": {
|
||||
"$divide": ["$dotProduct", {"$max": [{"$multiply": ["$magnitude1", "$magnitude2"]}, 1e-9]}]
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"$match": {
|
||||
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
|
||||
|
|
|
|||
Loading…
Reference in New Issue