🤖 自动格式化代码 [skip ci]

import_openie.py

@@ -20,6 +20,7 @@ import sys
 
 logger = get_module_logger("LPMM知识库-OpenIE导入")
 
+
 def hash_deduplicate(
     raw_paragraphs: Dict[str, str],
     triple_list_data: Dict[str, List[List[str]]],
@@ -43,14 +44,10 @@ def hash_deduplicate(
     # 保存去重后的三元组
     new_triple_list_data = dict()
 
-    for _, (raw_paragraph, triple_list) in enumerate(
+    for _, (raw_paragraph, triple_list) in enumerate(zip(raw_paragraphs.values(), triple_list_data.values())):
-        zip(raw_paragraphs.values(), triple_list_data.values())
-    ):
         # 段落hash
         paragraph_hash = get_sha256(raw_paragraph)
-        if ((PG_NAMESPACE + "-" + paragraph_hash) in stored_pg_hashes) and (
+        if ((PG_NAMESPACE + "-" + paragraph_hash) in stored_pg_hashes) and (paragraph_hash in stored_paragraph_hashes):
-            paragraph_hash in stored_paragraph_hashes
-        ):
             continue
         new_raw_paragraphs[paragraph_hash] = raw_paragraph
         new_triple_list_data[paragraph_hash] = triple_list
@@ -58,9 +55,7 @@ def hash_deduplicate(
     return new_raw_paragraphs, new_triple_list_data
 
 
-def handle_import_openie(
+def handle_import_openie(openie_data: OpenIE, embed_manager: EmbeddingManager, kg_manager: KGManager) -> bool:
-    openie_data: OpenIE, embed_manager: EmbeddingManager, kg_manager: KGManager
-) -> bool:
     # 从OpenIE数据中提取段落原文与三元组列表
     # 索引的段落原文
     raw_paragraphs = openie_data.extract_raw_paragraph_dict()
@@ -68,9 +63,7 @@ def handle_import_openie(
     entity_list_data = openie_data.extract_entity_dict()
     # 索引的三元组列表
     triple_list_data = openie_data.extract_triple_dict()
-    if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(
+    if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(triple_list_data):
-        triple_list_data
-    ):
         logger.error("OpenIE数据存在异常")
         return False
     # 将索引换为对应段落的hash值
@@ -112,11 +105,11 @@ def main():
     print("知识导入时，会消耗大量系统资源，建议在较好配置电脑上运行")
     print("同上样例，导入时10700K几乎跑满，14900HX占用80%，峰值内存占用约3G")
     confirm = input("确认继续执行？(y/n): ").strip().lower()
-    if confirm != 'y':
+    if confirm != "y":
         logger.info("用户取消操作")
         print("操作已取消")
         sys.exit(1)
-    print("\n" + "="*40 + "\n")
+    print("\n" + "=" * 40 + "\n")
 
     logger.info("----开始导入openie数据----\n")
 
@@ -129,9 +122,7 @@ def main():
         )
 
     # 初始化Embedding库
-    embed_manager = embed_manager = EmbeddingManager(
+    embed_manager = embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
-        llm_client_list[global_config["embedding"]["provider"]]
-    )
     logger.info("正在从文件加载Embedding库")
     try:
         embed_manager.load_from_file()

info_extraction.py

@@ -91,11 +91,11 @@ def main():
     print("或者使用可以用赠金抵扣的Pro模型")
     print("请确保账户余额充足，并且在执行前确认无误。")
     confirm = input("确认继续执行？(y/n): ").strip().lower()
-    if confirm != 'y':
+    if confirm != "y":
         logger.info("用户取消操作")
         print("操作已取消")
         sys.exit(1)
-    print("\n" + "="*40 + "\n")
+    print("\n" + "=" * 40 + "\n")
 
     logger.info("--------进行信息提取--------\n")
 

raw_data_preprocessor.py

@@ -3,18 +3,17 @@ import os
 from pathlib import Path
 import sys  # 新增系统模块导入
 
+
 def check_and_create_dirs():
     """检查并创建必要的目录"""
-    required_dirs = [
+    required_dirs = ["data/lpmm_raw_data", "data/imported_lpmm_data"]
-        "data/lpmm_raw_data",
+
-        "data/imported_lpmm_data"
-    ]
-    
     for dir_path in required_dirs:
         if not os.path.exists(dir_path):
             os.makedirs(dir_path)
             print(f"已创建目录: {dir_path}")
 
+
 def process_text_file(file_path):
     """处理单个文本文件，返回段落列表"""
     with open(file_path, "r", encoding="utf-8") as f:
@@ -29,12 +28,13 @@ def process_text_file(file_path):
                 paragraph = ""
         else:
             paragraph += line + "\n"
-    
+
     if paragraph != "":
         paragraphs.append(paragraph.strip())
-    
+
     return paragraphs
 
+
 def main():
     # 新增用户确认提示
     print("=== 重要操作确认 ===")
@@ -43,42 +43,43 @@ def main():
     print("在进行知识库导入之前")
     print("请修改config/lpmm_config.toml中的配置项")
     confirm = input("确认继续执行？(y/n): ").strip().lower()
-    if confirm != 'y':
+    if confirm != "y":
         print("操作已取消")
         sys.exit(1)
-    print("\n" + "="*40 + "\n")
+    print("\n" + "=" * 40 + "\n")
 
     # 检查并创建必要的目录
     check_and_create_dirs()
-    
+
     # 检查输出文件是否存在
     if os.path.exists("data/import.json"):
         print("错误: data/import.json 已存在，请先处理或删除该文件")
         sys.exit(1)
-    
+
     if os.path.exists("data/openie.json"):
         print("错误: data/openie.json 已存在，请先处理或删除该文件")
         sys.exit(1)
-    
+
     # 获取所有原始文本文件
     raw_files = list(Path("data/lpmm_raw_data").glob("*.txt"))
     if not raw_files:
         print("警告: data/lpmm_raw_data 中没有找到任何 .txt 文件")
         sys.exit(1)
-    
+
     # 处理所有文件
     all_paragraphs = []
     for file in raw_files:
         print(f"正在处理文件: {file.name}")
         paragraphs = process_text_file(file)
         all_paragraphs.extend(paragraphs)
-    
+
     # 保存合并后的结果
     output_path = "data/import.json"
     with open(output_path, "w", encoding="utf-8") as f:
         json.dump(all_paragraphs, f, ensure_ascii=False, indent=4)
-    
+
     print(f"处理完成，结果已保存到: {output_path}")
 
+
 if __name__ == "__main__":
-    main()
+    main()

src/do_tool/tool_can_use/lpmm_get_knowledge.py

@@ -1,5 +1,6 @@
 from src.do_tool.tool_can_use.base_tool import BaseTool
 from src.plugins.chat.utils import get_embedding
+
 # from src.common.database import db
 from src.common.logger import get_module_logger
 from typing import Dict, Any

src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py

@@ -364,7 +364,7 @@ class PromptBuilder:
         #             grouped_results[topic] = []
         #         grouped_results[topic].append(result)
 
-            # 按主题组织输出
+        # 按主题组织输出
         #     for topic, results in grouped_results.items():
         #         related_info += f"【主题: {topic}】\n"
         #         for _i, result in enumerate(results, 1):

src/plugins/knowledge/knowledge_lib.py

@@ -22,9 +22,7 @@ for key in global_config["llm_providers"]:
     )
 
 # 初始化Embedding库
-embed_manager = EmbeddingManager(
+embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
-    llm_client_list[global_config["embedding"]["provider"]]
-)
 logger.info("正在从文件加载Embedding库")
 try:
     embed_manager.load_from_file()
@@ -63,4 +61,3 @@ inspire_manager = MemoryActiveManager(
     embed_manager,
     llm_client_list[global_config["embedding"]["provider"]],
 )
-

src/plugins/knowledge/lib/quick_algo/__init__.py

@@ -23,9 +23,7 @@ def _nx_graph_to_lists(
         A tuple containing the list of edges and the list of nodes.
     """
     nodes = [node for node in graph.nodes()]
-    edges = [
+    edges = [(u, v, graph.get_edge_data(u, v).get("weight", 0.0)) for u, v in graph.edges()]
-        (u, v, graph.get_edge_data(u, v).get("weight", 0.0)) for u, v in graph.edges()
-    ]
 
     return edges, nodes
 

src/plugins/knowledge/lib/quick_algo/pagerank_py.py

@@ -9,7 +9,7 @@ def pagerank_py(
     personalization: Optional[Dict[str, float]] = None,
     alpha: float = 0.85,
     max_iter: int = 100,
-    tol: float = 1e-6
+    tol: float = 1e-6,
 ) -> Dict[str, float]:
     """使用 Python、NumPy 和 SciPy 计算个性化 PageRank。
 
@@ -44,14 +44,14 @@ def pagerank_py(
         raw_values = np.maximum(raw_values, 0)
         norm_sum = np.sum(raw_values)
 
-        if norm_sum > 1e-9: # 避免除以零
+        if norm_sum > 1e-9:  # 避免除以零
             personalization_vec = raw_values / norm_sum
         else:
             # 如果所有提供的个性化值都为零或负数，则回退到均匀分布
             print("警告：个性化值总和为零或所有值均为非正数。回退到均匀个性化设置。")
             personalization_vec.fill(1.0 / num_nodes)
 
-    # --- 构建稀疏邻接矩阵 --- 
+    # --- 构建稀疏邻接矩阵 ---
     # 标准 PageRank 需要基于出度的归一化
     row_ind = []
     col_ind = []
@@ -66,9 +66,9 @@ def pagerank_py(
             col_ind.append(src_idx)
             # 暂存原始权重，如果需要加权 PageRank，可以在此使用 w
             # 对于标准 PageRank，我们只需要知道连接存在
-            data.append(1.0) # 初始数据设为 1，之后归一化
+            data.append(1.0)  # 初始数据设为 1，之后归一化
             # 标准 PageRank 的出度是边的数量，加权 PageRank 可以用 w
-            out_degree[src_idx] += 1 
+            out_degree[src_idx] += 1
 
     # 归一化权重（构建转移矩阵 M 的转置 M.T）
     # M[j, i] 是从 i 到 j 的概率
@@ -82,17 +82,16 @@ def pagerank_py(
         if out_degree[c] > 0:
             # 标准 PageRank: 1.0 / out_degree[c]
             # 如果要用原始权重 w 作为转移概率（需确保它们已归一化），则用 w / sum(w for edges from c)
-            normalized_data.append(d / out_degree[c]) 
+            normalized_data.append(d / out_degree[c])
-            new_row_ind.append(c) # M.T 的行索引是 src_idx
+            new_row_ind.append(c)  # M.T 的行索引是 src_idx
-            new_col_ind.append(r) # M.T 的列索引是 dst_idx
+            new_col_ind.append(r)  # M.T 的列索引是 dst_idx
 
     # 创建稀疏矩阵 (M.T)
     # 注意：scipy.sparse 期望 (data, (row_ind, col_ind)) 格式
     # 这里构建的是 M 的转置，方便后续计算 scores = alpha * M.T @ scores + ...
     if len(normalized_data) > 0:
         # 使用 csc_matrix 以便高效地进行列操作（矩阵向量乘法）
-        M_T = sp.csc_matrix((normalized_data, (new_row_ind, new_col_ind)), 
+        M_T = sp.csc_matrix((normalized_data, (new_row_ind, new_col_ind)), shape=(num_nodes, num_nodes))
-                            shape=(num_nodes, num_nodes))
     else:
         M_T = sp.csc_matrix((num_nodes, num_nodes))
 
@@ -109,44 +108,45 @@ def pagerank_py(
     # 还有一种做法是仅分配给个性化向量中非零的节点
 
     # --- PageRank 迭代 ---
-    scores = personalization_vec.copy() # 从个性化向量开始
+    scores = personalization_vec.copy()  # 从个性化向量开始
 
     for iteration in range(max_iter):
         prev_scores = scores.copy()
-        
+
         # 计算来自链接的贡献
         linked_scores = M_T @ scores
-        
+
         # 计算来自悬挂节点的贡献
         # 悬挂节点的总分数 * 悬挂权重向量
         dangling_sum = np.sum(scores[is_dangling])
         dangling_contribution = dangling_sum * dangling_weights
-        
+
         # 结合瞬移、链接贡献和悬挂节点贡献
         scores = alpha * (linked_scores + dangling_contribution) + (1 - alpha) * personalization_vec
-        
+
         # 检查收敛性 (L1 范数)
         diff = np.sum(np.abs(scores - prev_scores))
         if diff < tol:
             print(f"在 {iteration + 1} 次迭代后收敛。")
             break
-    else: # 循环完成但未中断
+    else:  # 循环完成但未中断
         print(f"达到最大迭代次数 ({max_iter}) 但未收敛。")
 
     # --- 格式化输出 ---
     result_dict = {index_to_node[i]: scores[i] for i in range(num_nodes)}
     return result_dict
 
+
 # --- 示例用法（类似于 pr.c 中的 main）---
 if __name__ == "__main__":
     nodes_test = ["0", "1", "2", "3", "4"]
     edges_test = [
-        ("0", "1", 0.5), # 权重在此实现中仅用于确定出度
+        ("0", "1", 0.5),  # 权重在此实现中仅用于确定出度
         ("1", "2", 0.3),
         ("2", "0", 0.2),
         ("1", "3", 0.4),
         ("3", "4", 0.6),
-        ("4", "1", 0.7)
+        ("4", "1", 0.7),
     ]
     # 添加一个悬挂节点示例
     nodes_test.append("5")
@@ -161,37 +161,33 @@ if __name__ == "__main__":
 
     print("运行优化的 Python PageRank 实现...")
     result = pagerank_py(
-        nodes_test,
+        nodes_test, edges_test, personalization_test, alpha=alpha_test, max_iter=max_iter_test, tol=tol_test
-        edges_test,
-        personalization_test,
-        alpha=alpha_test,
-        max_iter=max_iter_test,
-        tol=tol_test
     )
 
     print("\nPageRank 分数:")
     # 按节点索引排序以获得一致的输出
     sorted_nodes = sorted(result.keys(), key=lambda x: int(x))
     for node_id in sorted_nodes:
-         print(f"节点 {node_id}: {result[node_id]:.6f}")
+        print(f"节点 {node_id}: {result[node_id]:.6f}")
 
     print("\n使用默认个性化设置运行...")
     result_default_pers = pagerank_py(
         nodes_test,
         edges_test,
-        personalization=None, # 使用默认的统一性化设置
+        personalization=None,  # 使用默认的统一性化设置
         alpha=alpha_test,
         max_iter=max_iter_test,
-        tol=tol_test
+        tol=tol_test,
     )
     print("\nPageRank 分数（默认个性化）:")
     sorted_nodes_default = sorted(result_default_pers.keys(), key=lambda x: int(x))
     for node_id in sorted_nodes_default:
-         print(f"节点 {node_id}: {result_default_pers[node_id]:.6f}")
+        print(f"节点 {node_id}: {result_default_pers[node_id]:.6f}")
 
     # 与 NetworkX 对比 (如果安装了)
     try:
         import networkx as nx
+
         print("\n与 NetworkX PageRank 对比 (个性化)...")
         G = nx.DiGraph()
         G.add_nodes_from(nodes_test)
@@ -200,25 +196,29 @@ if __name__ == "__main__":
         # 为了更接近我们的实现，我们不传递权重给 add_edges_from
         edges_for_nx = [(u, v) for u, v, w in edges_test]
         G.add_edges_from(edges_for_nx)
-        
+
         # 归一化 NetworkX 的个性化向量
         nx_pers = {node: personalization_test.get(node, 0.0) for node in nodes_test}
         pers_sum = sum(nx_pers.values())
         if pers_sum > 0:
             nx_pers = {k: v / pers_sum for k, v in nx_pers.items()}
-        else: # 如果全为0，NetworkX 会报错或行为未定义，我们设为 None
+        else:  # 如果全为0，NetworkX 会报错或行为未定义，我们设为 None
             nx_pers = None
 
-        nx_result = nx.pagerank(G, alpha=alpha_test, personalization=nx_pers, max_iter=max_iter_test, tol=tol_test, weight=None) # weight=None 强制标准 PageRank
+        nx_result = nx.pagerank(
+            G, alpha=alpha_test, personalization=nx_pers, max_iter=max_iter_test, tol=tol_test, weight=None
+        )  # weight=None 强制标准 PageRank
         for node_id in sorted_nodes:
             print(f"节点 {node_id}: {nx_result.get(node_id, 0.0):.6f}")
 
         print("\n与 NetworkX PageRank 对比 (默认)...")
-        nx_result_default = nx.pagerank(G, alpha=alpha_test, personalization=None, max_iter=max_iter_test, tol=tol_test, weight=None)
+        nx_result_default = nx.pagerank(
+            G, alpha=alpha_test, personalization=None, max_iter=max_iter_test, tol=tol_test, weight=None
+        )
         for node_id in sorted_nodes_default:
             print(f"节点 {node_id}: {nx_result_default.get(node_id, 0.0):.6f}")
 
     except ImportError:
         print("\n未安装 NetworkX，跳过对比。")
     except Exception as e:
-        print(f"\n运行 NetworkX PageRank 时出错: {e}")
+        print(f"\n运行 NetworkX PageRank 时出错: {e}")

src/plugins/knowledge/src/config.py

@@ -1,6 +1,7 @@
 import os
 import toml
 from .global_logger import logger
+
 PG_NAMESPACE = "paragraph"
 ENT_NAMESPACE = "entity"
 REL_NAMESPACE = "relation"
@@ -58,6 +59,7 @@ def _load_config(config, config_file_path):
 
     logger.info(f"Configurations loaded from file: {config_file_path}")
 
+
 global_config = dict(
     {
         "llm_providers": {
@@ -119,4 +121,4 @@ file_path = os.path.abspath(__file__)
 dir_path = os.path.dirname(file_path)
 root_path = os.path.join(dir_path, os.pardir, os.pardir, os.pardir, os.pardir)
 config_path = os.path.join(root_path, "config", "lpmm_config.toml")
-_load_config(global_config, config_path)
+_load_config(global_config, config_path)

src/plugins/knowledge/src/embedding_store.py

@@ -47,9 +47,7 @@ class EmbeddingStore:
         self.idx2hash = None
 
     def _get_embedding(self, s: str) -> List[float]:
-        return self.llm_client.send_embedding_request(
+        return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s)
-            global_config["embedding"]["model"], s
-        )
 
     def batch_insert_strs(self, strs: List[str]) -> None:
         """向库中存入字符串"""
@@ -83,14 +81,10 @@ class EmbeddingStore:
         logger.info(f"{self.namespace}嵌入库保存成功")
 
         if self.faiss_index is not None and self.idx2hash is not None:
-            logger.info(
+            logger.info(f"正在保存{self.namespace}嵌入库的FaissIndex到文件{self.index_file_path}")
-                f"正在保存{self.namespace}嵌入库的FaissIndex到文件{self.index_file_path}"
-            )
             faiss.write_index(self.faiss_index, self.index_file_path)
             logger.info(f"{self.namespace}嵌入库的FaissIndex保存成功")
-            logger.info(
+            logger.info(f"正在保存{self.namespace}嵌入库的idx2hash映射到文件{self.idx2hash_file_path}")
-                f"正在保存{self.namespace}嵌入库的idx2hash映射到文件{self.idx2hash_file_path}"
-            )
             with open(self.idx2hash_file_path, "w", encoding="utf-8") as f:
                 f.write(json.dumps(self.idx2hash, ensure_ascii=False, indent=4))
             logger.info(f"{self.namespace}嵌入库的idx2hash映射保存成功")
@@ -103,24 +97,18 @@ class EmbeddingStore:
         logger.info(f"正在从文件{self.embedding_file_path}中加载{self.namespace}嵌入库")
         data_frame = pd.read_parquet(self.embedding_file_path, engine="pyarrow")
         for _, row in tqdm.tqdm(data_frame.iterrows(), total=len(data_frame)):
-            self.store[row["hash"]] = EmbeddingStoreItem(
+            self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"])
-                row["hash"], row["embedding"], row["str"]
-            )
         logger.info(f"{self.namespace}嵌入库加载成功")
 
         try:
             if os.path.exists(self.index_file_path):
-                logger.info(
+                logger.info(f"正在从文件{self.index_file_path}中加载{self.namespace}嵌入库的FaissIndex")
-                    f"正在从文件{self.index_file_path}中加载{self.namespace}嵌入库的FaissIndex"
-                )
                 self.faiss_index = faiss.read_index(self.index_file_path)
                 logger.info(f"{self.namespace}嵌入库的FaissIndex加载成功")
             else:
                 raise Exception(f"文件{self.index_file_path}不存在")
             if os.path.exists(self.idx2hash_file_path):
-                logger.info(
+                logger.info(f"正在从文件{self.idx2hash_file_path}中加载{self.namespace}嵌入库的idx2hash映射")
-                    f"正在从文件{self.idx2hash_file_path}中加载{self.namespace}嵌入库的idx2hash映射"
-                )
                 with open(self.idx2hash_file_path, "r") as f:
                     self.idx2hash = json.load(f)
                 logger.info(f"{self.namespace}嵌入库的idx2hash映射加载成功")
@@ -215,9 +203,7 @@ class EmbeddingManager:
         for triples in triple_list_data.values():
             graph_triples.extend([tuple(t) for t in triples])
         graph_triples = list(set(graph_triples))
-        self.relation_embedding_store.batch_insert_strs(
+        self.relation_embedding_store.batch_insert_strs([str(triple) for triple in graph_triples])
-            [str(triple) for triple in graph_triples]
-        )
 
     def load_from_file(self):
         """从文件加载"""

src/plugins/knowledge/src/global_logger.py

@@ -7,8 +7,6 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
 console_logging_handler = logging.StreamHandler()
-console_logging_handler.setFormatter(
+console_logging_handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(message)s"))
-    logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
-)
 console_logging_handler.setLevel(logging.DEBUG)
 logger.addHandler(console_logging_handler)

src/plugins/knowledge/src/ie_process.py

@@ -38,16 +38,12 @@ def _entity_extract(llm_client: LLMClient, paragraph: str) -> List[str]:
     return entity_extract_result
 
 
-def _rdf_triple_extract(
+def _rdf_triple_extract(llm_client: LLMClient, paragraph: str, entities: list) -> List[List[str]]:
-    llm_client: LLMClient, paragraph: str, entities: list
-) -> List[List[str]]:
     """对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
     entity_extract_context = prompt_template.build_rdf_triple_extract_context(
         paragraph, entities=json.dumps(entities, ensure_ascii=False)
     )
-    _, request_result = llm_client.send_chat_request(
+    _, request_result = llm_client.send_chat_request(global_config["rdf_build"]["llm"]["model"], entity_extract_context)
-        global_config["rdf_build"]["llm"]["model"], entity_extract_context
-    )
 
     # 去除‘{’前的内容（结果中可能有多个‘{’）
     if "[" in request_result:
@@ -60,11 +56,7 @@ def _rdf_triple_extract(
     entity_extract_result = json.loads(fix_broken_generated_json(request_result))
 
     for triple in entity_extract_result:
-        if (
+        if len(triple) != 3 or (triple[0] is None or triple[1] is None or triple[2] is None) or "" in triple:
-            len(triple) != 3
-            or (triple[0] is None or triple[1] is None or triple[2] is None)
-            or "" in triple
-        ):
             raise Exception("RDF提取结果格式错误")
 
     return entity_extract_result
@@ -91,9 +83,7 @@ def info_extract_from_str(
     try_count = 0
     while True:
         try:
-            rdf_triple_extract_result = _rdf_triple_extract(
+            rdf_triple_extract_result = _rdf_triple_extract(llm_client_for_rdf, paragraph, entity_extract_result)
-                llm_client_for_rdf, paragraph, entity_extract_result
-            )
             break
         except Exception as e:
             logger.warning(f"实体提取失败，错误信息：{e}")

src/plugins/knowledge/src/kg_manager.py

@@ -22,6 +22,7 @@ from .config import (
 
 from .global_logger import logger
 
+
 class KGManager:
     def __init__(self):
         # 会被保存的字段
@@ -35,9 +36,7 @@ class KGManager:
         # 持久化相关
         self.dir_path = global_config["persistence"]["rag_data_dir"]
         self.graph_data_path = self.dir_path + "/" + RAG_GRAPH_NAMESPACE + ".graphmlz"
-        self.ent_cnt_data_path = (
+        self.ent_cnt_data_path = self.dir_path + "/" + RAG_ENT_CNT_NAMESPACE + ".parquet"
-            self.dir_path + "/" + RAG_ENT_CNT_NAMESPACE + ".parquet"
-        )
         self.pg_hash_file_path = self.dir_path + "/" + RAG_PG_HASH_NAMESPACE + ".json"
 
     def save_to_file(self):
@@ -50,9 +49,7 @@ class KGManager:
         nx.write_graphml(self.graph, path=self.graph_data_path, encoding="utf-8")
 
         # 保存实体计数到文件
-        ent_cnt_df = pd.DataFrame(
+        ent_cnt_df = pd.DataFrame([{"hash_key": k, "appear_cnt": v} for k, v in self.ent_appear_cnt.items()])
-            [{"hash_key": k, "appear_cnt": v} for k, v in self.ent_appear_cnt.items()]
-        )
         ent_cnt_df.to_parquet(self.ent_cnt_data_path, engine="pyarrow", index=False)
 
         # 保存段落hash到文件
@@ -77,9 +74,7 @@ class KGManager:
 
         # 加载实体计数
         ent_cnt_df = pd.read_parquet(self.ent_cnt_data_path, engine="pyarrow")
-        self.ent_appear_cnt = dict(
+        self.ent_appear_cnt = dict({row["hash_key"]: row["appear_cnt"] for _, row in ent_cnt_df.iterrows()})
-            {row["hash_key"]: row["appear_cnt"] for _, row in ent_cnt_df.iterrows()}
-        )
 
         # 加载KG
         self.graph = nx.read_graphml(self.graph_data_path)
@@ -101,20 +96,14 @@ class KGManager:
                 # 一个triple就是一条边（同时构建双向联系）
                 hash_key1 = ENT_NAMESPACE + "-" + get_sha256(triple[0])
                 hash_key2 = ENT_NAMESPACE + "-" + get_sha256(triple[2])
-                node_to_node[(hash_key1, hash_key2)] = (
+                node_to_node[(hash_key1, hash_key2)] = node_to_node.get((hash_key1, hash_key2), 0) + 1.0
-                    node_to_node.get((hash_key1, hash_key2), 0) + 1.0
+                node_to_node[(hash_key2, hash_key1)] = node_to_node.get((hash_key2, hash_key1), 0) + 1.0
-                )
-                node_to_node[(hash_key2, hash_key1)] = (
-                    node_to_node.get((hash_key2, hash_key1), 0) + 1.0
-                )
                 entity_set.add(hash_key1)
                 entity_set.add(hash_key2)
 
             # 实体出现次数统计
             for hash_key in entity_set:
-                self.ent_appear_cnt[hash_key] = (
+                self.ent_appear_cnt[hash_key] = self.ent_appear_cnt.get(hash_key, 0) + 1.0
-                    self.ent_appear_cnt.get(hash_key, 0) + 1.0
-                )
 
     @staticmethod
     def _build_edges_between_ent_pg(
@@ -126,9 +115,7 @@ class KGManager:
             for triple in triple_list_data[idx]:
                 ent_hash_key = ENT_NAMESPACE + "-" + get_sha256(triple[0])
                 pg_hash_key = PG_NAMESPACE + "-" + str(idx)
-                node_to_node[(ent_hash_key, pg_hash_key)] = (
+                node_to_node[(ent_hash_key, pg_hash_key)] = node_to_node.get((ent_hash_key, pg_hash_key), 0) + 1.0
-                    node_to_node.get((ent_hash_key, pg_hash_key), 0) + 1.0
-                )
 
     @staticmethod
     def _synonym_connect(
@@ -177,9 +164,7 @@ class KGManager:
                 new_edge_cnt += 1
                 res_ent.append(
                     (
-                        embedding_manager.entities_embedding_store.store[
+                        embedding_manager.entities_embedding_store.store[res_ent_hash].str,
-                            res_ent_hash
-                        ].str,
                         similarity,
                     )
                 )  # Debug
@@ -236,22 +221,16 @@ class KGManager:
                 if node_hash not in existed_nodes:
                     if node_hash.startswith(ENT_NAMESPACE):
                         # 新增实体节点
-                        node = embedding_manager.entities_embedding_store.store[
+                        node = embedding_manager.entities_embedding_store.store[node_hash]
-                            node_hash
-                        ]
                         assert isinstance(node, EmbeddingStoreItem)
                         self.graph.nodes[node_hash]["content"] = node.str
                         self.graph.nodes[node_hash]["type"] = "ent"
                     elif node_hash.startswith(PG_NAMESPACE):
                         # 新增文段节点
-                        node = embedding_manager.paragraphs_embedding_store.store[
+                        node = embedding_manager.paragraphs_embedding_store.store[node_hash]
-                            node_hash
-                        ]
                         assert isinstance(node, EmbeddingStoreItem)
                         content = node.str.replace("\n", " ")
-                        self.graph.nodes[node_hash]["content"] = (
+                        self.graph.nodes[node_hash]["content"] = content if len(content) < 8 else content[:8] + "..."
-                            content if len(content) < 8 else content[:8] + "..."
-                        )
                         self.graph.nodes[node_hash]["type"] = "pg"
 
     def build_kg(
@@ -319,9 +298,7 @@ class KGManager:
         ent_sim_scores = {}
         for relation_hash, similarity, _ in relation_search_result:
             # 提取主宾短语
-            relation = embed_manager.relation_embedding_store.store.get(
+            relation = embed_manager.relation_embedding_store.store.get(relation_hash).str
-                relation_hash
-            ).str
             assert relation is not None  # 断言：relation不为空
             # 关系三元组
             triple = relation[2:-2].split("', '")
@@ -335,9 +312,7 @@ class KGManager:
         ent_mean_scores = {}  # 记录实体的平均相似度
         for ent_hash, scores in ent_sim_scores.items():
             # 先对相似度进行累加，然后与实体计数相除获取最终权重
-            ent_weights[ent_hash] = (
+            ent_weights[ent_hash] = float(np.sum(scores)) / self.ent_appear_cnt[ent_hash]
-                float(np.sum(scores)) / self.ent_appear_cnt[ent_hash]
-            )
             # 记录实体的平均相似度，用于后续的top_k筛选
             ent_mean_scores[ent_hash] = float(np.mean(scores))
         del ent_sim_scores
@@ -354,21 +329,14 @@ class KGManager:
             for ent_hash, score in ent_weights.items():
                 # 缩放相似度
                 ent_weights[ent_hash] = (
-                    (score - ent_weights_min)
+                    (score - ent_weights_min) * (1 - down_edge) / (ent_weights_max - ent_weights_min)
-                    * (1 - down_edge)
-                    / (ent_weights_max - ent_weights_min)
                 ) + down_edge
 
         # 取平均相似度的top_k实体
         top_k = global_config["qa"]["params"]["ent_filter_top_k"]
         if len(ent_mean_scores) > top_k:
             # 从大到小排序，取后len - k个
-            ent_mean_scores = {
+            ent_mean_scores = {k: v for k, v in sorted(ent_mean_scores.items(), key=lambda item: item[1], reverse=True)}
-                k: v
-                for k, v in sorted(
-                    ent_mean_scores.items(), key=lambda item: item[1], reverse=True
-                )
-            }
             for ent_hash, _ in ent_mean_scores.items():
                 # 删除被淘汰的实体节点权重设置
                 del ent_weights[ent_hash]
@@ -389,9 +357,7 @@ class KGManager:
         # 归一化
         for pg_hash, similarity in pg_sim_scores.items():
             # 归一化相似度
-            pg_sim_scores[pg_hash] = (similarity - pg_sim_score_min) / (
+            pg_sim_scores[pg_hash] = (similarity - pg_sim_score_min) / (pg_sim_score_max - pg_sim_score_min)
-                pg_sim_score_max - pg_sim_score_min
-            )
         del pg_sim_score_max, pg_sim_score_min
 
         for pg_hash, score in pg_sim_scores.items():
@@ -401,9 +367,7 @@ class KGManager:
         del pg_sim_scores
 
         # 最终权重数据 = 实体权重 + 文段权重
-        ppr_node_weights = {
+        ppr_node_weights = {k: v for d in [ent_weights, pg_weights] for k, v in d.items()}
-            k: v for d in [ent_weights, pg_weights] for k, v in d.items()
-        }
         del ent_weights, pg_weights
 
         # PersonalizedPageRank
@@ -418,14 +382,12 @@ class KGManager:
         # 从搜索结果中提取文段节点的结果
         passage_node_res = [
             (node_key, score)
-            for node_key, score in ppr_res.items() # Iterate over dictionary items
+            for node_key, score in ppr_res.items()  # Iterate over dictionary items
             if node_key.startswith(PG_NAMESPACE)
         ]
         del ppr_res
 
         # 排序：按照分数从大到小
-        passage_node_res = sorted(
+        passage_node_res = sorted(passage_node_res, key=lambda item: item[1], reverse=True)
-            passage_node_res, key=lambda item: item[1], reverse=True
-        )
 
         return passage_node_res, ppr_node_weights

src/plugins/knowledge/src/llm_client.py

@@ -21,20 +21,14 @@ class LLMClient:
 
     def send_chat_request(self, model, messages):
         """发送对话请求，等待返回结果"""
-        response = self.client.chat.completions.create(
+        response = self.client.chat.completions.create(model=model, messages=messages, stream=False)
-            model=model, messages=messages, stream=False
-        )
         if hasattr(response.choices[0].message, "reasoning_content"):
             # 有单独的推理内容块
             reasoning_content = response.choices[0].message.reasoning_content
             content = response.choices[0].message.content
         else:
             # 无单独的推理内容块
-            response = (
+            response = response.choices[0].message.content.split("<think>")[-1].split("</think>")
-                response.choices[0]
-                .message.content.split("<think>")[-1]
-                .split("</think>")
-            )
             # 如果有推理内容，则分割推理内容和内容
             if len(response) == 2:
                 reasoning_content = response[0]
@@ -48,6 +42,4 @@ class LLMClient:
     def send_embedding_request(self, model, text):
         """发送嵌入请求，等待返回结果"""
         text = text.replace("\n", " ")
-        return (
+        return self.client.embeddings.create(input=[text], model=model).data[0].embedding
-            self.client.embeddings.create(input=[text], model=model).data[0].embedding
-        )

src/plugins/knowledge/src/lpmmconfig.py

@@ -58,7 +58,6 @@ def _load_config(config, config_file_path):
         config["persistence"] = file_config["persistence"]
     print(config)
     print("Configurations loaded from file: ", config_file_path)
-    
 
 
 parser = argparse.ArgumentParser(description="Configurations for the pipeline")
@@ -122,9 +121,9 @@ global_config = dict(
             "embedding_data_dir": "data/embedding",
             "rag_data_dir": "data/rag",
         },
-        "info_extraction":{
+        "info_extraction": {
             "workers": 10,
-        }
+        },
     }
 )
 

src/plugins/knowledge/src/mem_active_manager.py

@@ -16,13 +16,9 @@ class MemoryActiveManager:
     def get_activation(self, question: str) -> float:
         """获取记忆激活度"""
         # 生成问题的Embedding
-        question_embedding = self.embedding_client.send_embedding_request(
+        question_embedding = self.embedding_client.send_embedding_request("text-embedding", question)
-            "text-embedding", question
-        )
         # 查询关系库中的相似度
-        rel_search_res = self.embed_manager.relation_embedding_store.search_top_k(
+        rel_search_res = self.embed_manager.relation_embedding_store.search_top_k(question_embedding, 10)
-            question_embedding, 10
-        )
 
         # 动态过滤阈值
         rel_scores = dyn_select_top_k(rel_search_res, 0.5, 1.0)

src/plugins/knowledge/src/open_ie.py

@@ -9,12 +9,7 @@ def _filter_invalid_entities(entities: List[str]) -> List[str]:
     """过滤无效的实体"""
     valid_entities = set()
     for entity in entities:
-        if (
+        if not isinstance(entity, str) or entity.strip() == "" or entity in INVALID_ENTITY or entity in valid_entities:
-            not isinstance(entity, str)
-            or entity.strip() == ""
-            or entity in INVALID_ENTITY
-            or entity in valid_entities
-        ):
             # 非字符串/空字符串/在无效实体列表中/重复
             continue
         valid_entities.add(entity)
@@ -74,9 +69,7 @@ class OpenIE:
 
         for doc in self.docs:
             # 过滤实体列表
-            doc["extracted_entities"] = _filter_invalid_entities(
+            doc["extracted_entities"] = _filter_invalid_entities(doc["extracted_entities"])
-                doc["extracted_entities"]
-            )
             # 过滤无效的三元组
             doc["extracted_triples"] = _filter_invalid_triples(doc["extracted_triples"])
 
@@ -100,9 +93,7 @@ class OpenIE:
     @staticmethod
     def load() -> "OpenIE":
         """从文件中加载OpenIE数据"""
-        with open(
+        with open(global_config["persistence"]["openie_data_path"], "r", encoding="utf-8") as f:
-            global_config["persistence"]["openie_data_path"], "r", encoding="utf-8"
-        ) as f:
             data = json.loads(f.read())
 
         openie_data = OpenIE._from_dict(data)
@@ -112,9 +103,7 @@ class OpenIE:
     @staticmethod
     def save(openie_data: "OpenIE"):
         """保存OpenIE数据到文件"""
-        with open(
+        with open(global_config["persistence"]["openie_data_path"], "w", encoding="utf-8") as f:
-            global_config["persistence"]["openie_data_path"], "w", encoding="utf-8"
-        ) as f:
             f.write(json.dumps(openie_data._to_dict(), ensure_ascii=False, indent=4))
 
     def extract_entity_dict(self):
@@ -141,7 +130,5 @@ class OpenIE:
 
     def extract_raw_paragraph_dict(self):
         """提取原始段落"""
-        raw_paragraph_dict = dict(
+        raw_paragraph_dict = dict({doc_item["idx"]: doc_item["passage"] for doc_item in self.docs})
-            {doc_item["idx"]: doc_item["passage"] for doc_item in self.docs}
-        )
         return raw_paragraph_dict

src/plugins/knowledge/src/prompt_template.py

@@ -41,9 +41,7 @@ rdf_triple_extract_system_prompt = """你是一个性能优异的RDF（资源描
 def build_rdf_triple_extract_context(paragraph: str, entities: str) -> List[LLMMessage]:
     messages = [
         LLMMessage("system", rdf_triple_extract_system_prompt).to_dict(),
-        LLMMessage(
+        LLMMessage("user", f"""段落：\n```\n{paragraph}```\n\n实体列表：\n```\n{entities}```""").to_dict(),
-            "user", f"""段落：\n```\n{paragraph}```\n\n实体列表：\n```\n{entities}```"""
-        ).to_dict(),
     ]
     return messages
 
@@ -58,16 +56,10 @@ qa_system_prompt = """
 """
 
 
-def build_qa_context(
+def build_qa_context(question: str, knowledge: list[(str, str, str)]) -> List[LLMMessage]:
-    question: str, knowledge: list[(str, str, str)]
+    knowledge = "\n".join([f"{i + 1}. 相关性：{k[0]}\n{k[1]}" for i, k in enumerate(knowledge)])
-) -> List[LLMMessage]:
-    knowledge = "\n".join(
-        [f"{i + 1}. 相关性：{k[0]}\n{k[1]}" for i, k in enumerate(knowledge)]
-    )
     messages = [
         LLMMessage("system", qa_system_prompt).to_dict(),
-        LLMMessage(
+        LLMMessage("user", f"问题：\n{question}\n\n可能有帮助的信息：\n{knowledge}").to_dict(),
-            "user", f"问题：\n{question}\n\n可能有帮助的信息：\n{knowledge}"
-        ).to_dict(),
     ]
     return messages

src/plugins/knowledge/src/qa_manager.py

@@ -2,6 +2,7 @@ import time
 from typing import Tuple, List, Dict
 
 from .global_logger import logger
+
 # from . import prompt_template
 from .embedding_store import EmbeddingManager
 from .llm_client import LLMClient
@@ -31,7 +32,7 @@ class QAManager:
         """处理查询"""
 
         # 生成问题的Embedding
-        part_start_time =time.perf_counter()
+        part_start_time = time.perf_counter()
         question_embedding = self.llm_client_list["embedding"].send_embedding_request(
             global_config["embedding"]["model"], question
         )
@@ -39,7 +40,7 @@ class QAManager:
         logger.debug(f"Embedding用时：{part_end_time - part_start_time:.5f}s")
 
         # 根据问题Embedding查询Relation Embedding库
-        part_start_time =time.perf_counter()
+        part_start_time = time.perf_counter()
         relation_search_res = self.embed_manager.relation_embedding_store.search_top_k(
             question_embedding,
             global_config["qa"]["params"]["relation_search_top_k"],
@@ -47,10 +48,7 @@ class QAManager:
         # 过滤阈值
         # 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
         relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0)
-        if (
+        if relation_search_res[0][1] < global_config["qa"]["params"]["relation_threshold"]:
-            relation_search_res[0][1]
-            < global_config["qa"]["params"]["relation_threshold"]
-        ):
             # 未找到相关关系
             relation_search_res = []
 
@@ -66,12 +64,10 @@ class QAManager:
         # part_start_time = time.time()
 
         # 根据问题Embedding查询Paragraph Embedding库
-        part_start_time =time.perf_counter()
+        part_start_time = time.perf_counter()
-        paragraph_search_res = (
+        paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
-            self.embed_manager.paragraphs_embedding_store.search_top_k(
+            question_embedding,
-                question_embedding,
+            global_config["qa"]["params"]["paragraph_search_top_k"],
-                global_config["qa"]["params"]["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")
@@ -79,7 +75,7 @@ class QAManager:
         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()
             result, ppr_node_weights = self.kg_manager.kg_search(
                 relation_search_res, paragraph_search_res, self.embed_manager
             )
@@ -94,9 +90,7 @@ class QAManager:
         result = dyn_select_top_k(result, 0.5, 1.0)
 
         for res in result:
-            raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[
+            raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
-                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
@@ -114,4 +108,4 @@ class QAManager:
             for res in query_res
         ]
         found_knowledge = "\n".join([f"{i + 1}. 相关性：{k[0]}\n{k[1]}" for i, k in enumerate(knowledge)])
-        return found_knowledge
+        return found_knowledge

src/plugins/knowledge/src/raw_processing.py

@@ -17,9 +17,7 @@ def load_raw_data() -> tuple[list[str], list[str]]:
     """
     # 读取import.json文件
     if os.path.exists(global_config["persistence"]["raw_data_path"]) is True:
-        with open(
+        with open(global_config["persistence"]["raw_data_path"], "r", encoding="utf-8") as f:
-            global_config["persistence"]["raw_data_path"], "r", encoding="utf-8"
-        ) as f:
             import_json = json.loads(f.read())
     else:
         raise Exception("原始数据文件读取失败")

src/plugins/knowledge/src/utils/data_loader.py

@@ -14,11 +14,7 @@ class DataLoader:
         Args:
             custom_data_dir: 可选的自定义数据目录路径，如果不提供则使用配置文件中的默认路径
         """
-        self.data_dir = (
+        self.data_dir = Path(custom_data_dir) if custom_data_dir else Path(config["persistence"]["data_root_path"])
-            Path(custom_data_dir)
-            if custom_data_dir
-            else Path(config["persistence"]["data_root_path"])
-        )
         if not self.data_dir.exists():
             raise FileNotFoundError(f"数据目录 {self.data_dir} 不存在")
 

src/plugins/knowledge/src/utils/dyn_topk.py

@@ -36,14 +36,10 @@ def dyn_select_top_k(
     # 计算均值
     mean_score = sum([s[2] for s in normalized_score]) / len(normalized_score)
     # 计算方差
-    var_score = sum([(s[2] - mean_score) ** 2 for s in normalized_score]) / len(
+    var_score = sum([(s[2] - mean_score) ** 2 for s in normalized_score]) / len(normalized_score)
-        normalized_score
-    )
 
     # 动态阈值
-    threshold = jmp_factor * jump_threshold + (1 - jmp_factor) * (
+    threshold = jmp_factor * jump_threshold + (1 - jmp_factor) * (mean_score + var_factor * var_score)
-        mean_score + var_factor * var_score
-    )
 
     # 重新过滤
     res = [s for s in normalized_score if s[2] > threshold]

src/plugins/knowledge/src/utils/json_fix.py

@@ -29,10 +29,7 @@ def _find_unclosed(json_str):
             elif char in "{[":
                 unclosed.append(char)
             elif char in "}]":
-                if unclosed and (
+                if unclosed and ((char == "}" and unclosed[-1] == "{") or (char == "]" and unclosed[-1] == "[")):
-                    (char == "}" and unclosed[-1] == "{")
-                    or (char == "]" and unclosed[-1] == "[")
-                ):
                     unclosed.pop()
 
     return unclosed

src/plugins/knowledge/src/utils/visualize_graph.py

@@ -14,4 +14,4 @@ def draw_graph_and_show(graph):
         font_family="Sarasa Mono SC",
         font_size=8,
     )
-    fig.show()
+    fig.show()