BruceSun
/
MaiBot
mirror of https://github.com/Mai-with-u/MaiBot.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update lpmm_get_knowledge.py

pull/998/head
2829798842 2025-05-28 21:33:30 +08:00 committed by GitHub
parent fd35f17ade
commit 91cbe9f0fa
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 10 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
src/tools/tool_can_use/lpmm_get_knowledge.py
View File

@ -43,17 +43,17 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
knowledge_info = qa_manager.get_knowledge(query) knowledge_info = qa_manager.get_knowledge(query)
logger.debug(f"知识库查询结果: {knowledge_info}") logger.debug(f"知识库查询结果: {knowledge_info}")
if knowledge_info: if knowledge_info:
content = f"You know this knowledge: {knowledge_info}" content = f"你知道这些知识: {knowledge_info}"
else: else:
content = f"You don't know much about {query}" content = f"你不太了解有关{query}的知识"
return {"type": "lpmm_knowledge", "id": query, "content": content} return {"type": "lpmm_knowledge", "id": query, "content": content}
# If embedding retrieval fails # 如果获取嵌入失败
return {"type": "info", "id": query, "content": f"Unable to get embedding vector for '{query}', your lpmm knowledge base failed"} return {"type": "info", "id": query, "content": f"无法获取关于'{query}'的嵌入向量，你lpmm知识库炸了"}
except Exception as e: except Exception as e:
logger.error(f"知识库搜索工具执行失败: {str(e)}") logger.error(f"知识库搜索工具执行失败: {str(e)}")
# 在其他异常情况下，确保 id 仍然是 query (如果它被定义了) # 在其他异常情况下，确保 id 仍然是 query (如果它被定义了)
query_id = query if "query" in locals() else "unknown_query" query_id = query if "query" in locals() else "unknown_query"
return {"type": "info", "id": query_id, "content": f"lpmm knowledge base search failed: {str(e)}"} return {"type": "info", "id": query_id, "content": f"lpmm知识库搜索失败，炸了: {str(e)}"}
# def get_info_from_db( # def get_info_from_db(
# self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False # self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
@ -139,18 +139,18 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
def _format_results(self, results: list) -> str: def _format_results(self, results: list) -> str:
"""格式化结果""" """格式化结果"""
if not results: if not results:
return "No relevant knowledge found." return "未找到相关知识。"
formatted_string = "I found some relevant knowledge:\n" formatted_string = "我找到了一些相关知识：\n"
for i, result in enumerate(results): for i, result in enumerate(results):
# chunk_id = result.get("chunk_id") # chunk_id = result.get("chunk_id")
text = result.get("text", "") text = result.get("text", "")
source = result.get("source", "Unknown source") source = result.get("source", "未知来源")
source_type = result.get("source_type", "Unknown type") source_type = result.get("source_type", "未知类型")
similarity = result.get("similarity", 0.0) similarity = result.get("similarity", 0.0)
formatted_string += ( formatted_string += (
f"{i + 1}. (Similarity: {similarity:.2f}) Type: {source_type}, Source: {source} \nContent fragment: {text}\n\n" f"{i + 1}. (相似度: {similarity:.2f}) 类型: {source_type}, 来源: {source} \n内容片段: {text}\n\n"
) )
# 暂时去掉chunk_id # 暂时去掉chunk_id
# formatted_string += f"{i + 1}. (相似度: {similarity:.2f}) 类型: {source_type}, 来源: {source}, Chunk ID: {chunk_id} \n内容片段: {text}\n\n" # formatted_string += f"{i + 1}. (相似度: {similarity:.2f}) 类型: {source_type}, 来源: {source}, Chunk ID: {chunk_id} \n内容片段: {text}\n\n"