87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
from typing import List, Tuple
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from src.common.logger import get_module_logger
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from src.chat.memory_system.Hippocampus import HippocampusManager
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from src.chat.models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.chat.message_receive.message import Message
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from src.chat.knowledge.knowledge_lib import qa_manager
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from src.chat.utils.chat_message_builder import build_readable_messages
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logger = get_module_logger("knowledge_fetcher")
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class KnowledgeFetcher:
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"""知识调取器"""
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def __init__(self, private_name: str):
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# TODO: API-Adapter修改标记
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self.llm = LLMRequest(
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model=global_config.model.normal,
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temperature=global_config.model.normal["temp"],
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max_tokens=1000,
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request_type="knowledge_fetch",
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)
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self.private_name = private_name
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def _lpmm_get_knowledge(self, query: str) -> str:
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"""获取相关知识
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Args:
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query: 查询内容
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Returns:
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str: 构造好的,带相关度的知识
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"""
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logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
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try:
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knowledge_info = qa_manager.get_knowledge(query)
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logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}")
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return knowledge_info
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}")
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return "未找到匹配的知识"
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async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
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"""获取相关知识
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Args:
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query: 查询内容
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chat_history: 聊天历史
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Returns:
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Tuple[str, str]: (获取的知识, 知识来源)
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"""
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# 构建查询上下文
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chat_history_text = await build_readable_messages(
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chat_history,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="relative",
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read_mark=0.0,
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)
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# 从记忆中获取相关知识
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related_memory = await HippocampusManager.get_instance().get_memory_from_text(
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text=f"{query}\n{chat_history_text}",
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max_memory_num=3,
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max_memory_length=2,
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max_depth=3,
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fast_retrieval=False,
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)
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knowledge_text = ""
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sources_text = "无记忆匹配" # 默认值
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if related_memory:
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sources = []
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for memory in related_memory:
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knowledge_text += memory[1] + "\n"
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sources.append(f"记忆片段{memory[0]}")
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knowledge_text = knowledge_text.strip()
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sources_text = "，".join(sources)
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knowledge_text += "\n现在有以下**知识**可供参考：\n "
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knowledge_text += self._lpmm_get_knowledge(query)
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knowledge_text += "\n请记住这些**知识**，并根据**知识**回答问题。\n"
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return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配"
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