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fix typing

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This commit is contained in:
UnCLAS-Prommer
2025-08-03 13:08:28 +08:00
parent a5631fd23a
commit 44f53213af
2 changed files with 49 additions and 49 deletions
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5
plugins/hello_world_plugin/plugin.py
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@@ -11,6 +11,7 @@ from src.plugin_system import (
BaseEventHandler, BaseEventHandler,
EventType, EventType,
MaiMessages, MaiMessages,
ToolParamType
) )
@@ -20,8 +21,8 @@ class CompareNumbersTool(BaseTool):
name = "compare_numbers" name = "compare_numbers"
description = "使用工具 比较两个数的大小，返回较大的数" description = "使用工具 比较两个数的大小，返回较大的数"
parameters = [ parameters = [
("num1", "number", "第一个数字", True), ("num1", ToolParamType.FLOAT, "第一个数字", True, None),
("num2", "number", "第二个数字", True), ("num2", ToolParamType.FLOAT, "第二个数字", True, None),
] ]
async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]: async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:

93
src/chat/knowledge/qa_manager.py
View File

@@ -28,7 +28,7 @@ class QAManager:
self.kg_manager = kg_manager self.kg_manager = kg_manager
self.qa_model = LLMRequest(model_set=model_config.model_task_config.lpmm_qa, request_type="lpmm.qa") self.qa_model = LLMRequest(model_set=model_config.model_task_config.lpmm_qa, request_type="lpmm.qa")
async def process_query(self, question: str) -> Tuple[List[Tuple[str, float, float]], Optional[Dict[str, float]]]: async def process_query(self, question: str) -> Optional[Tuple[List[Tuple[str, float, float]], Optional[Dict[str, float]]]]:
"""处理查询""" """处理查询"""
# 生成问题的Embedding # 生成问题的Embedding
@@ -46,61 +46,60 @@ class QAManager:
question_embedding, question_embedding,
global_config.lpmm_knowledge.qa_relation_search_top_k, global_config.lpmm_knowledge.qa_relation_search_top_k,
) )
if relation_search_res is not None: if relation_search_res is None:
# 过滤阈值 return None
# 考虑动态阈值：当存在显著数值差异的结果时，保留显著结果；否则，保留所有结果 # 过滤阈值
relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0) # 考虑动态阈值：当存在显著数值差异的结果时，保留显著结果；否则，保留所有结果
if not relation_search_res or relation_search_res[0][1] < global_config.lpmm_knowledge.qa_relation_threshold: relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0)
# 未找到相关关系 if not relation_search_res or relation_search_res[0][1] < global_config.lpmm_knowledge.qa_relation_threshold:
logger.debug("未找到相关关系，跳过关系检索") # 未找到相关关系
relation_search_res = [] logger.debug("未找到相关关系，跳过关系检索")
relation_search_res = []
part_end_time = time.perf_counter() part_end_time = time.perf_counter()
logger.debug(f"关系检索用时：{part_end_time - part_start_time:.5f}s") logger.debug(f"关系检索用时：{part_end_time - part_start_time:.5f}s")
for res in relation_search_res: for res in relation_search_res:
rel_str = self.embed_manager.relation_embedding_store.store.get(res[0]).str rel_str = self.embed_manager.relation_embedding_store.store.get(res[0]).str
print(f"找到相关关系，相似度：{(res[1] * 100):.2f}% - {rel_str}") print(f"找到相关关系，相似度：{(res[1] * 100):.2f}% - {rel_str}")
# TODO: 使用LLM过滤三元组结果 # TODO: 使用LLM过滤三元组结果
# logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s") # logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s")
# part_start_time = time.time() # part_start_time = time.time()
# 根据问题Embedding查询Paragraph Embedding库 # 根据问题Embedding查询Paragraph Embedding库
part_start_time = time.perf_counter()
paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
question_embedding,
global_config.lpmm_knowledge.qa_paragraph_search_top_k,
)
part_end_time = time.perf_counter()
logger.debug(f"文段检索用时：{part_end_time - part_start_time:.5f}s")
if len(relation_search_res) != 0:
logger.info("找到相关关系，将使用RAG进行检索")
# 使用KG检索
part_start_time = time.perf_counter() part_start_time = time.perf_counter()
paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k( result, ppr_node_weights = self.kg_manager.kg_search(
question_embedding, relation_search_res, paragraph_search_res, self.embed_manager
global_config.lpmm_knowledge.qa_paragraph_search_top_k,
) )
part_end_time = time.perf_counter() part_end_time = time.perf_counter()
logger.debug(f"文段检索用时：{part_end_time - part_start_time:.5f}s") logger.info(f"RAG检索用时：{part_end_time - part_start_time:.5f}s")
if len(relation_search_res) != 0:
logger.info("找到相关关系，将使用RAG进行检索")
# 使用KG检索
part_start_time = time.perf_counter()
result, ppr_node_weights = self.kg_manager.kg_search(
relation_search_res, paragraph_search_res, self.embed_manager
)
part_end_time = time.perf_counter()
logger.info(f"RAG检索用时：{part_end_time - part_start_time:.5f}s")
else:
logger.info("未找到相关关系，将使用文段检索结果")
result = paragraph_search_res
ppr_node_weights = None
# 过滤阈值
result = dyn_select_top_k(result, 0.5, 1.0)
for res in result:
raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
return result, ppr_node_weights
else: else:
return None logger.info("未找到相关关系，将使用文段检索结果")
result = paragraph_search_res
ppr_node_weights = None
async def get_knowledge(self, question: str) -> str: # 过滤阈值
result = dyn_select_top_k(result, 0.5, 1.0)
for res in result:
raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
return result, ppr_node_weights
async def get_knowledge(self, question: str) -> Optional[str]:
"""获取知识""" """获取知识"""
# 处理查询 # 处理查询
processed_result = await self.process_query(question) processed_result = await self.process_query(question)