From fd052cd43b376039d853dfd209a2e34f5366fcb7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=A2=A8=E6=A2=93=E6=9F=92?= <1787882683@qq.com>
Date: Fri, 25 Apr 2025 18:32:11 +0800
Subject: [PATCH] =?UTF-8?q?feat(KnowledgeFetcher):=20=E6=B7=BB=E5=8A=A0LPM?=
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为KnowledgeFetcher类新增_lpmm_get_knowledge方法，用于从LPMM知识库中获取相关知识。同时，在fetch方法中整合了LPMM知识库查询结果，以提供更全面的知识参考。
---
 src/plugins/PFC/pfc_KnowledgeFetcher.py | 29 ++++++++++++++++++++++---
 1 file changed, 26 insertions(+), 3 deletions(-)

diff --git a/src/plugins/PFC/pfc_KnowledgeFetcher.py b/src/plugins/PFC/pfc_KnowledgeFetcher.py
index 1a0d495c3..63f71aad8 100644
--- a/src/plugins/PFC/pfc_KnowledgeFetcher.py
+++ b/src/plugins/PFC/pfc_KnowledgeFetcher.py
@@ -4,6 +4,7 @@ from src.plugins.memory_system.Hippocampus import HippocampusManager
 from ..models.utils_model import LLMRequest
 from ...config.config import global_config
 from ..chat.message import Message
+from ..knowledge.knowledge_lib import qa_manager
 
 logger = get_module_logger("knowledge_fetcher")
 
@@ -18,6 +19,25 @@ class KnowledgeFetcher:
             max_tokens=1000,
             request_type="knowledge_fetch",
         )
+    
+    def _lpmm_get_knowledge(self, query: str) -> str:
+        """获取相关知识
+
+        Args:
+            query: 查询内容
+
+        Returns:
+            str: 构造好的,带相关度的知识
+        """
+        
+        logger.debug("正在从LPMM知识库中获取知识")
+        try:
+            knowledge_info = qa_manager.get_knowledge(query)
+            logger.debug(f"LPMM知识库查询结果: {knowledge_info:150}")
+            return knowledge_info
+        except Exception as e:
+            logger.error(f"LPMM知识库搜索工具执行失败: {str(e)}")
+            return "未找到匹配的知识"
 
     async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
         """获取相关知识
@@ -43,13 +63,16 @@ class KnowledgeFetcher:
             max_depth=3,
             fast_retrieval=False,
         )
-
+        knowledge = ""
         if related_memory:
-            knowledge = ""
+            
             sources = []
             for memory in related_memory:
                 knowledge += memory[1] + "\n"
                 sources.append(f"记忆片段{memory[0]}")
-            return knowledge.strip(), "，".join(sources)
+            knowledge = knowledge.strip(), "，".join(sources)
+        
+        knowledge +="现在有以下**知识**可供参考：\n 请记住这些**知识**，并根据**知识**回答问题。\n"
+        knowledge += self._lpmm_get_knowledge(query)
 
         return "未找到相关知识", "无记忆匹配"