Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

scripts/import_openie.py

@@ -6,6 +6,7 @@
 
 import sys
 import os
+from time import sleep
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
@@ -19,9 +20,14 @@ from src.plugins.knowledge.src.utils.hash import get_sha256
 
 
 # 添加项目根目录到 sys.path
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+OPENIE_DIR = (
+    global_config["persistence"]["openie_data_path"]
+    if global_config["persistence"]["openie_data_path"]
+    else os.path.join(ROOT_PATH, "data/openie")
+)
 
-
-logger = get_module_logger("LPMM知识库-OpenIE导入")
+logger = get_module_logger("OpenIE导入")
 
 
 def hash_deduplicate(
@@ -66,8 +72,45 @@ def handle_import_openie(openie_data: OpenIE, embed_manager: EmbeddingManager, k
     entity_list_data = openie_data.extract_entity_dict()
     # 索引的三元组列表
     triple_list_data = openie_data.extract_triple_dict()
+    # print(openie_data.docs)
     if len(raw_paragraphs) != len(entity_list_data) or len(raw_paragraphs) != len(triple_list_data):
         logger.error("OpenIE数据存在异常")
+        logger.error(f"原始段落数量：{len(raw_paragraphs)}")
+        logger.error(f"实体列表数量：{len(entity_list_data)}")
+        logger.error(f"三元组列表数量：{len(triple_list_data)}")
+        logger.error("OpenIE数据段落数量与实体列表数量或三元组列表数量不一致")
+        logger.error("请保证你的原始数据分段良好，不要有类似于 “.....” 单独成一段的情况")
+        logger.error("或者一段中只有符号的情况")
+        # 新增：检查docs中每条数据的完整性
+        logger.error("系统将于2秒后开始检查数据完整性")
+        sleep(2)
+        found_missing = False
+        for doc in getattr(openie_data, "docs", []):
+            idx = doc.get("idx", "<无idx>")
+            passage = doc.get("passage", "<无passage>")
+            missing = []
+            # 检查字段是否存在且非空
+            if "passage" not in doc or not doc.get("passage"):
+                missing.append("passage")
+            if "extracted_entities" not in doc or not isinstance(doc.get("extracted_entities"), list):
+                missing.append("名词列表缺失")
+            elif len(doc.get("extracted_entities", [])) == 0:
+                missing.append("名词列表为空")
+            if "extracted_triples" not in doc or not isinstance(doc.get("extracted_triples"), list):
+                missing.append("主谓宾三元组缺失")
+            elif len(doc.get("extracted_triples", [])) == 0:
+                missing.append("主谓宾三元组为空")
+            # 输出所有doc的idx
+            # print(f"检查: idx={idx}")
+            if missing:
+                found_missing = True
+                logger.error("\n")
+                logger.error("数据缺失：")
+                logger.error(f"对应哈希值：{idx}")
+                logger.error(f"对应文段内容内容：{passage}")
+                logger.error(f"非法原因：{', '.join(missing)}")
+        if not found_missing:
+            print("所有数据均完整，没有发现缺失字段。")
         return False
     # 将索引换为对应段落的hash值
     logger.info("正在进行段落去重与重索引")
@@ -131,6 +174,7 @@ def main():
         embed_manager.load_from_file()
     except Exception as e:
         logger.error("从文件加载Embedding库时发生错误：{}".format(e))
+        logger.error("如果你是第一次导入知识，请忽略此错误")
     logger.info("Embedding库加载完成")
     # 初始化KG
     kg_manager = KGManager()
@@ -139,6 +183,7 @@ def main():
         kg_manager.load_from_file()
     except Exception as e:
         logger.error("从文件加载KG时发生错误：{}".format(e))
+        logger.error("如果你是第一次导入知识，请忽略此错误")
     logger.info("KG加载完成")
 
     logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
@@ -163,4 +208,5 @@ def main():
 
 
 if __name__ == "__main__":
+    # logger.info(f"111111111111111111111111{ROOT_PATH}")
     main()

scripts/info_extraction.py

@@ -4,11 +4,13 @@ import signal
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from threading import Lock, Event
 import sys
+import glob
+import datetime
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 # 添加项目根目录到 sys.path
 
-import tqdm
+from rich.progress import Progress  # 替换为 rich 进度条
 
 from src.common.logger import get_module_logger
 from src.plugins.knowledge.src.lpmmconfig import global_config
@@ -16,10 +18,31 @@ from src.plugins.knowledge.src.ie_process import info_extract_from_str
 from src.plugins.knowledge.src.llm_client import LLMClient
 from src.plugins.knowledge.src.open_ie import OpenIE
 from src.plugins.knowledge.src.raw_processing import load_raw_data
+from rich.progress import (
+    BarColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+    TaskProgressColumn,
+    MofNCompleteColumn,
+    SpinnerColumn,
+    TextColumn,
+)
 
 logger = get_module_logger("LPMM知识库-信息提取")
 
-TEMP_DIR = "./temp"
+
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+TEMP_DIR = os.path.join(ROOT_PATH, "temp")
+IMPORTED_DATA_PATH = (
+    global_config["persistence"]["raw_data_path"]
+    if global_config["persistence"]["raw_data_path"]
+    else os.path.join(ROOT_PATH, "data/imported_lpmm_data")
+)
+OPENIE_OUTPUT_DIR = (
+    global_config["persistence"]["openie_data_path"]
+    if global_config["persistence"]["openie_data_path"]
+    else os.path.join(ROOT_PATH, "data/openie")
+)
 
 # 创建一个线程安全的锁，用于保护文件操作和共享数据
 file_lock = Lock()
@@ -70,8 +93,7 @@ def process_single_text(pg_hash, raw_data, llm_client_list):
                 # 如果保存失败，确保不会留下损坏的文件
                 if os.path.exists(temp_file_path):
                     os.remove(temp_file_path)
-                # 设置shutdown_event以终止程序
-                shutdown_event.set()
+                sys.exit(0)
                 return None, pg_hash
         return doc_item, None
 
@@ -79,7 +101,7 @@ def process_single_text(pg_hash, raw_data, llm_client_list):
 def signal_handler(_signum, _frame):
     """处理Ctrl+C信号"""
     logger.info("\n接收到中断信号，正在优雅地关闭程序...")
-    shutdown_event.set()
+    sys.exit(0)
 
 
 def main():
@@ -110,33 +132,61 @@ def main():
             global_config["llm_providers"][key]["api_key"],
         )
 
-    logger.info("正在加载原始数据")
-    sha256_list, raw_datas = load_raw_data()
-    logger.info("原始数据加载完成\n")
+    # 检查 openie 输出目录
+    if not os.path.exists(OPENIE_OUTPUT_DIR):
+        os.makedirs(OPENIE_OUTPUT_DIR)
+        logger.info(f"已创建输出目录: {OPENIE_OUTPUT_DIR}")
 
-    # 创建临时目录
-    if not os.path.exists(f"{TEMP_DIR}"):
-        os.makedirs(f"{TEMP_DIR}")
+    # 确保 TEMP_DIR 目录存在
+    if not os.path.exists(TEMP_DIR):
+        os.makedirs(TEMP_DIR)
+        logger.info(f"已创建缓存目录: {TEMP_DIR}")
+
+    # 遍历IMPORTED_DATA_PATH下所有json文件
+    imported_files = sorted(glob.glob(os.path.join(IMPORTED_DATA_PATH, "*.json")))
+    if not imported_files:
+        logger.error(f"未在 {IMPORTED_DATA_PATH} 下找到任何json文件")
+        sys.exit(1)
+
+    all_sha256_list = []
+    all_raw_datas = []
+
+    for imported_file in imported_files:
+        logger.info(f"正在处理文件: {imported_file}")
+        try:
+            sha256_list, raw_datas = load_raw_data(imported_file)
+        except Exception as e:
+            logger.error(f"读取文件失败: {imported_file}, 错误: {e}")
+            continue
+        all_sha256_list.extend(sha256_list)
+        all_raw_datas.extend(raw_datas)
 
     failed_sha256 = []
     open_ie_doc = []
 
-    # 创建线程池，最大线程数为50
     workers = global_config["info_extraction"]["workers"]
     with ThreadPoolExecutor(max_workers=workers) as executor:
-        # 提交所有任务到线程池
         future_to_hash = {
             executor.submit(process_single_text, pg_hash, raw_data, llm_client_list): pg_hash
-            for pg_hash, raw_data in zip(sha256_list, raw_datas)
+            for pg_hash, raw_data in zip(all_sha256_list, all_raw_datas)
         }
 
-        # 使用tqdm显示进度
-        with tqdm.tqdm(total=len(future_to_hash), postfix="正在进行提取：") as pbar:
-            # 处理完成的任务
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            MofNCompleteColumn(),
+            "•",
+            TimeElapsedColumn(),
+            "<",
+            TimeRemainingColumn(),
+            transient=False,
+        ) as progress:
+            task = progress.add_task("正在进行提取：", total=len(future_to_hash))
             try:
                 for future in as_completed(future_to_hash):
                     if shutdown_event.is_set():
-                        # 取消所有未完成的任务
                         for f in future_to_hash:
                             if not f.done():
                                 f.cancel()
@@ -149,26 +199,38 @@ def main():
                     elif doc_item:
                         with open_ie_doc_lock:
                             open_ie_doc.append(doc_item)
-                    pbar.update(1)
+                    progress.update(task, advance=1)
             except KeyboardInterrupt:
-                # 如果在这里捕获到KeyboardInterrupt，说明signal_handler可能没有正常工作
                 logger.info("\n接收到中断信号，正在优雅地关闭程序...")
                 shutdown_event.set()
-                # 取消所有未完成的任务
                 for f in future_to_hash:
                     if not f.done():
                         f.cancel()
 
-    # 保存信息提取结果
-    sum_phrase_chars = sum([len(e) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
-    sum_phrase_words = sum([len(e.split()) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
-    num_phrases = sum([len(chunk["extracted_entities"]) for chunk in open_ie_doc])
-    openie_obj = OpenIE(
-        open_ie_doc,
-        round(sum_phrase_chars / num_phrases, 4),
-        round(sum_phrase_words / num_phrases, 4),
-    )
-    OpenIE.save(openie_obj)
+    # 合并所有文件的提取结果并保存
+    if open_ie_doc:
+        sum_phrase_chars = sum([len(e) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
+        sum_phrase_words = sum([len(e.split()) for chunk in open_ie_doc for e in chunk["extracted_entities"]])
+        num_phrases = sum([len(chunk["extracted_entities"]) for chunk in open_ie_doc])
+        openie_obj = OpenIE(
+            open_ie_doc,
+            round(sum_phrase_chars / num_phrases, 4) if num_phrases else 0,
+            round(sum_phrase_words / num_phrases, 4) if num_phrases else 0,
+        )
+        # 输出文件名格式：MM-DD-HH-ss-openie.json
+        now = datetime.datetime.now()
+        filename = now.strftime("%m-%d-%H-%S-openie.json")
+        output_path = os.path.join(OPENIE_OUTPUT_DIR, filename)
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(
+                openie_obj.to_dict() if hasattr(openie_obj, "to_dict") else openie_obj.__dict__,
+                f,
+                ensure_ascii=False,
+                indent=4,
+            )
+        logger.info(f"信息提取结果已保存到: {output_path}")
+    else:
+        logger.warning("没有可保存的信息提取结果")
 
     logger.info("--------信息提取完成--------")
     logger.info(f"提取失败的文段SHA256：{failed_sha256}")

scripts/raw_data_preprocessor.py

@@ -2,18 +2,22 @@ import json
 import os
 from pathlib import Path
 import sys  # 新增系统模块导入
+import datetime  # 新增导入
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from src.common.logger import get_module_logger
 
 logger = get_module_logger("LPMM数据库-原始数据处理")
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+RAW_DATA_PATH = os.path.join(ROOT_PATH, "data/lpmm_raw_data")
+IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data/imported_lpmm_data")
 
 # 添加项目根目录到 sys.path
 
 
 def check_and_create_dirs():
     """检查并创建必要的目录"""
-    required_dirs = ["data/lpmm_raw_data", "data/imported_lpmm_data"]
+    required_dirs = [RAW_DATA_PATH, IMPORTED_DATA_PATH]
 
     for dir_path in required_dirs:
         if not os.path.exists(dir_path):
@@ -58,17 +62,17 @@ def main():
     # 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"):
-        logger.error("错误: data/import.json 已存在，请先处理或删除该文件")
-        sys.exit(1)
+    # # 检查输出文件是否存在
+    # if os.path.exists(RAW_DATA_PATH):
+    #     logger.error("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"):
-        logger.error("错误: data/openie.json 已存在，请先处理或删除该文件")
-        sys.exit(1)
+    # if os.path.exists(RAW_DATA_PATH):
+    #     logger.error("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"))
+    raw_files = list(Path(RAW_DATA_PATH).glob("*.txt"))
     if not raw_files:
         logger.warning("警告: data/lpmm_raw_data 中没有找到任何 .txt 文件")
         sys.exit(1)
@@ -80,8 +84,10 @@ def main():
         paragraphs = process_text_file(file)
         all_paragraphs.extend(paragraphs)
 
-    # 保存合并后的结果
-    output_path = "data/import.json"
+    # 保存合并后的结果到 IMPORTED_DATA_PATH，文件名格式为 MM-DD-HH-ss-imported-data.json
+    now = datetime.datetime.now()
+    filename = now.strftime("%m-%d-%H-%S-imported-data.json")
+    output_path = os.path.join(IMPORTED_DATA_PATH, filename)
     with open(output_path, "w", encoding="utf-8") as f:
         json.dump(all_paragraphs, f, ensure_ascii=False, indent=4)
 
@@ -89,4 +95,6 @@ def main():
 
 
 if __name__ == "__main__":
+    print(f"Raw Data Path: {RAW_DATA_PATH}")
+    print(f"Imported Data Path: {IMPORTED_DATA_PATH}")
     main()

src/plugins/chat/message_sender.py

@@ -219,9 +219,9 @@ class MessageManager:
             # print(f"message.reply:{message.reply}")
 
             # --- 条件应用 set_reply 逻辑 ---
-            logger.debug(
-                f"[message.apply_set_reply_logic:{message.apply_set_reply_logic},message.is_head:{message.is_head},thinking_messages_count:{thinking_messages_count},thinking_messages_length:{thinking_messages_length},message.is_private_message():{message.is_private_message()}]"
-            )
+            # logger.debug(
+            #     f"[message.apply_set_reply_logic:{message.apply_set_reply_logic},message.is_head:{message.is_head},thinking_messages_count:{thinking_messages_count},thinking_messages_length:{thinking_messages_length},message.is_private_message():{message.is_private_message()}]"
+            # )
             if (
                 message.apply_set_reply_logic  # 检查标记
                 and message.is_head

src/plugins/heartFC_chat/normal_chat.py

@@ -178,8 +178,11 @@ class NormalChat:
         """更新关系情绪"""
         ori_response = ",".join(response_set)
         stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
+        user_info = message.message_info.user_info
+        platform = user_info.platform
         await relationship_manager.calculate_update_relationship_value(
-            chat_stream=self.chat_stream,
+            user_info,
+            platform,
             label=emotion,
             stance=stance,  # 使用 self.chat_stream
         )

src/plugins/knowledge/knowledge_lib.py

@@ -26,6 +26,7 @@ try:
     embed_manager.load_from_file()
 except Exception as e:
     logger.error("从文件加载Embedding库时发生错误：{}".format(e))
+    logger.error("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
 logger.info("Embedding库加载完成")
 # 初始化KG
 kg_manager = KGManager()
@@ -34,6 +35,7 @@ try:
     kg_manager.load_from_file()
 except Exception as e:
     logger.error("从文件加载KG时发生错误：{}".format(e))
+    logger.error("如果你是第一次导入知识，或者还未导入知识，请忽略此错误")
 logger.info("KG加载完成")
 
 logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")

src/plugins/knowledge/src/embedding_store.py

@@ -13,9 +13,21 @@ from .lpmmconfig import ENT_NAMESPACE, PG_NAMESPACE, REL_NAMESPACE, global_confi
 from .utils.hash import get_sha256
 from .global_logger import logger
 from rich.traceback import install
+from rich.progress import (
+    Progress,
+    BarColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+    TaskProgressColumn,
+    MofNCompleteColumn,
+    SpinnerColumn,
+    TextColumn,
+)
 
 install(extra_lines=3)
 
+TOTAL_EMBEDDING_TIMES = 3  # 统计嵌入次数
+
 
 @dataclass
 class EmbeddingStoreItem:
@@ -52,20 +64,35 @@ class EmbeddingStore:
     def _get_embedding(self, s: str) -> List[float]:
         return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s)
 
-    def batch_insert_strs(self, strs: List[str]) -> None:
+    def batch_insert_strs(self, strs: List[str], times: int) -> None:
         """向库中存入字符串"""
-        # 逐项处理
-        for s in tqdm.tqdm(strs, desc="存入嵌入库", unit="items"):
-            # 计算hash去重
-            item_hash = self.namespace + "-" + get_sha256(s)
-            if item_hash in self.store:
-                continue
+        total = len(strs)
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            MofNCompleteColumn(),
+            "•",
+            TimeElapsedColumn(),
+            "<",
+            TimeRemainingColumn(),
+            transient=False,
+        ) as progress:
+            task = progress.add_task(f"存入嵌入库：({times}/{TOTAL_EMBEDDING_TIMES})", total=total)
+            for s in strs:
+                # 计算hash去重
+                item_hash = self.namespace + "-" + get_sha256(s)
+                if item_hash in self.store:
+                    progress.update(task, advance=1)
+                    continue
 
-            # 获取embedding
-            embedding = self._get_embedding(s)
+                # 获取embedding
+                embedding = self._get_embedding(s)
 
-            # 存入
-            self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                # 存入
+                self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                progress.update(task, advance=1)
 
     def save_to_file(self) -> None:
         """保存到文件"""
@@ -191,7 +218,7 @@ class EmbeddingManager:
 
     def _store_pg_into_embedding(self, raw_paragraphs: Dict[str, str]):
         """将段落编码存入Embedding库"""
-        self.paragraphs_embedding_store.batch_insert_strs(list(raw_paragraphs.values()))
+        self.paragraphs_embedding_store.batch_insert_strs(list(raw_paragraphs.values()), times=1)
 
     def _store_ent_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
         """将实体编码存入Embedding库"""
@@ -200,7 +227,7 @@ class EmbeddingManager:
             for triple in triple_list:
                 entities.add(triple[0])
                 entities.add(triple[2])
-        self.entities_embedding_store.batch_insert_strs(list(entities))
+        self.entities_embedding_store.batch_insert_strs(list(entities), times=2)
 
     def _store_rel_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
         """将关系编码存入Embedding库"""
@@ -208,7 +235,7 @@ 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([str(triple) for triple in graph_triples])
+        self.relation_embedding_store.batch_insert_strs([str(triple) for triple in graph_triples], times=3)
 
     def load_from_file(self):
         """从文件加载"""

src/plugins/knowledge/src/kg_manager.py

@@ -5,7 +5,16 @@ from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
-import tqdm
+from rich.progress import (
+    Progress,
+    BarColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+    TaskProgressColumn,
+    MofNCompleteColumn,
+    SpinnerColumn,
+    TextColumn,
+)
 from quick_algo import di_graph, pagerank
 
 
@@ -132,41 +141,56 @@ class KGManager:
         ent_hash_list = list(ent_hash_list)
 
         synonym_hash_set = set()
-
         synonym_result = dict()
 
-        # 对每个实体节点，查找其相似的实体节点，建立扩展连接
-        for ent_hash in tqdm.tqdm(ent_hash_list):
-            if ent_hash in synonym_hash_set:
-                # 避免同一批次内重复添加
-                continue
-            ent = embedding_manager.entities_embedding_store.store.get(ent_hash)
-            assert isinstance(ent, EmbeddingStoreItem)
-            if ent is None:
-                continue
-            # 查询相似实体
-            similar_ents = embedding_manager.entities_embedding_store.search_top_k(
-                ent.embedding, global_config["rag"]["params"]["synonym_search_top_k"]
-            )
-            res_ent = []  # Debug
-            for res_ent_hash, similarity in similar_ents:
-                if res_ent_hash == ent_hash:
-                    # 避免自连接
+        # rich 进度条
+        total = len(ent_hash_list)
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            MofNCompleteColumn(),
+            "•",
+            TimeElapsedColumn(),
+            "<",
+            TimeRemainingColumn(),
+            transient=False,
+        ) as progress:
+            task = progress.add_task("同义词连接", total=total)
+            for ent_hash in ent_hash_list:
+                if ent_hash in synonym_hash_set:
+                    progress.update(task, advance=1)
                     continue
-                if similarity < global_config["rag"]["params"]["synonym_threshold"]:
-                    # 相似度阈值
+                ent = embedding_manager.entities_embedding_store.store.get(ent_hash)
+                assert isinstance(ent, EmbeddingStoreItem)
+                if ent is None:
+                    progress.update(task, advance=1)
                     continue
-                node_to_node[(res_ent_hash, ent_hash)] = similarity
-                node_to_node[(ent_hash, res_ent_hash)] = similarity
-                synonym_hash_set.add(res_ent_hash)
-                new_edge_cnt += 1
-                res_ent.append(
-                    (
-                        embedding_manager.entities_embedding_store.store[res_ent_hash].str,
-                        similarity,
-                    )
-                )  # Debug
-                synonym_result[ent.str] = res_ent
+                # 查询相似实体
+                similar_ents = embedding_manager.entities_embedding_store.search_top_k(
+                    ent.embedding, global_config["rag"]["params"]["synonym_search_top_k"]
+                )
+                res_ent = []  # Debug
+                for res_ent_hash, similarity in similar_ents:
+                    if res_ent_hash == ent_hash:
+                        # 避免自连接
+                        continue
+                    if similarity < global_config["rag"]["params"]["synonym_threshold"]:
+                        # 相似度阈值
+                        continue
+                    node_to_node[(res_ent_hash, ent_hash)] = similarity
+                    node_to_node[(ent_hash, res_ent_hash)] = similarity
+                    synonym_hash_set.add(res_ent_hash)
+                    new_edge_cnt += 1
+                    res_ent.append(
+                        (
+                            embedding_manager.entities_embedding_store.store[res_ent_hash].str,
+                            similarity,
+                        )
+                    )  # Debug
+                    synonym_result[ent.str] = res_ent
+                progress.update(task, advance=1)
 
         for k, v in synonym_result.items():
             print(f'"{k}"的相似实体为：{v}')

src/plugins/knowledge/src/open_ie.py

@@ -1,9 +1,13 @@
 import json
+import os
+import glob
 from typing import Any, Dict, List
 
 
 from .lpmmconfig import INVALID_ENTITY, global_config
 
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
+
 
 def _filter_invalid_entities(entities: List[str]) -> List[str]:
     """过滤无效的实体"""
@@ -74,12 +78,22 @@ class OpenIE:
             doc["extracted_triples"] = _filter_invalid_triples(doc["extracted_triples"])
 
     @staticmethod
-    def _from_dict(data):
-        """从字典中获取OpenIE对象"""
+    def _from_dict(data_list):
+        """从多个字典合并OpenIE对象"""
+        # data_list: List[dict]
+        all_docs = []
+        for data in data_list:
+            all_docs.extend(data.get("docs", []))
+        # 重新计算统计
+        sum_phrase_chars = sum([len(e) for chunk in all_docs for e in chunk["extracted_entities"]])
+        sum_phrase_words = sum([len(e.split()) for chunk in all_docs for e in chunk["extracted_entities"]])
+        num_phrases = sum([len(chunk["extracted_entities"]) for chunk in all_docs])
+        avg_ent_chars = round(sum_phrase_chars / num_phrases, 4) if num_phrases else 0
+        avg_ent_words = round(sum_phrase_words / num_phrases, 4) if num_phrases else 0
         return OpenIE(
-            docs=data["docs"],
-            avg_ent_chars=data["avg_ent_chars"],
-            avg_ent_words=data["avg_ent_words"],
+            docs=all_docs,
+            avg_ent_chars=avg_ent_chars,
+            avg_ent_words=avg_ent_words,
         )
 
     def _to_dict(self):
@@ -92,12 +106,20 @@ class OpenIE:
 
     @staticmethod
     def load() -> "OpenIE":
-        """从文件中加载OpenIE数据"""
-        with open(global_config["persistence"]["openie_data_path"], "r", encoding="utf-8") as f:
-            data = json.loads(f.read())
-
-        openie_data = OpenIE._from_dict(data)
-
+        """从OPENIE_DIR下所有json文件合并加载OpenIE数据"""
+        openie_dir = os.path.join(ROOT_PATH, global_config["persistence"]["openie_data_path"])
+        if not os.path.exists(openie_dir):
+            raise Exception(f"OpenIE数据目录不存在: {openie_dir}")
+        json_files = sorted(glob.glob(os.path.join(openie_dir, "*.json")))
+        data_list = []
+        for file in json_files:
+            with open(file, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                data_list.append(data)
+        if not data_list:
+            # print(f"111111111111111111111Root Path : \n{ROOT_PATH}")
+            raise Exception(f"未在 {openie_dir} 找到任何OpenIE json文件")
+        openie_data = OpenIE._from_dict(data_list)
         return openie_data
 
     @staticmethod
@@ -132,3 +154,8 @@ class OpenIE:
         """提取原始段落"""
         raw_paragraph_dict = dict({doc_item["idx"]: doc_item["passage"] for doc_item in self.docs})
         return raw_paragraph_dict
+
+
+if __name__ == "__main__":
+    # 测试代码
+    print(ROOT_PATH)

src/plugins/knowledge/src/raw_processing.py

@@ -6,21 +6,25 @@ from .lpmmconfig import global_config
 from .utils.hash import get_sha256
 
 
-def load_raw_data() -> tuple[list[str], list[str]]:
+def load_raw_data(path: str = None) -> tuple[list[str], list[str]]:
     """加载原始数据文件
 
     读取原始数据文件，将原始数据加载到内存中
 
+    Args:
+        path: 可选，指定要读取的json文件绝对路径
+
     Returns:
-        - raw_data: 原始数据字典
-        - md5_set: 原始数据的SHA256集合
+        - raw_data: 原始数据列表
+        - sha256_list: 原始数据的SHA256集合
     """
-    # 读取import.json文件
-    if os.path.exists(global_config["persistence"]["raw_data_path"]) is True:
-        with open(global_config["persistence"]["raw_data_path"], "r", encoding="utf-8") as f:
+    # 读取指定路径或默认路径的json文件
+    json_path = path if path else global_config["persistence"]["raw_data_path"]
+    if os.path.exists(json_path):
+        with open(json_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")
+        raise Exception(f"原始数据文件读取失败: {json_path}")
     # import_json内容示例：
     # import_json = [
     #       "The capital of China is Beijing. The capital of France is Paris.",

src/plugins/person_info/relationship_manager.py

@@ -5,6 +5,7 @@ from bson.decimal128 import Decimal128
 from .person_info import person_info_manager
 import time
 import random
+from maim_message import UserInfo, Seg
 # import re
 # import traceback
 
@@ -102,7 +103,7 @@ class RelationshipManager:
         # await person_info_manager.update_one_field(person_id, "user_avatar", user_avatar)
         await person_info_manager.qv_person_name(person_id, user_nickname, user_cardname, user_avatar)
 
-    async def calculate_update_relationship_value(self, chat_stream: ChatStream, label: str, stance: str) -> tuple:
+    async def calculate_update_relationship_value(self, user_info: UserInfo, platform: str, label: str, stance: str):
         """计算并变更关系值
         新的关系值变更计算方式：
             将关系值限定在-1000到1000
@@ -134,11 +135,11 @@ class RelationshipManager:
             "困惑": 0.5,
         }
 
-        person_id = person_info_manager.get_person_id(chat_stream.user_info.platform, chat_stream.user_info.user_id)
+        person_id = person_info_manager.get_person_id(platform, user_info.user_id)
         data = {
-            "platform": chat_stream.user_info.platform,
-            "user_id": chat_stream.user_info.user_id,
-            "nickname": chat_stream.user_info.user_nickname,
+            "platform": platform,
+            "user_id": user_info.user_id,
+            "nickname": user_info.user_nickname,
             "konw_time": int(time.time()),
         }
         old_value = await person_info_manager.get_value(person_id, "relationship_value")
@@ -178,7 +179,7 @@ class RelationshipManager:
         level_num = self.calculate_level_num(old_value + value)
         relationship_level = ["厌恶", "冷漠", "一般", "友好", "喜欢", "暧昧"]
         logger.info(
-            f"用户: {chat_stream.user_info.user_nickname}"
+            f"用户: {user_info.user_nickname}"
             f"当前关系: {relationship_level[level_num]}, "
             f"关系值: {old_value:.2f}, "
             f"当前立场情感: {stance}-{label}, "
@@ -187,8 +188,6 @@ class RelationshipManager:
 
         await person_info_manager.update_one_field(person_id, "relationship_value", old_value + value, data)
 
-        return chat_stream.user_info.user_nickname, value, relationship_level[level_num]
-
     async def calculate_update_relationship_value_with_reason(
         self, chat_stream: ChatStream, label: str, stance: str, reason: str
     ) -> tuple:

template/lpmm_config_template.toml

@@ -51,7 +51,7 @@ res_top_k = 3                 # 最终提供的文段TopK
 [persistence]
 # 持久化配置（存储中间数据，防止重复计算）
 data_root_path = "data"                              # 数据根目录
-raw_data_path = "data/import.json"                   # 原始数据路径
-openie_data_path = "data/openie.json"                # OpenIE数据路径
+raw_data_path = "data/imported_lpmm_data"            # 原始数据路径
+openie_data_path = "data/openie"                     # OpenIE数据路径
 embedding_data_dir = "data/embedding"                # 嵌入数据目录
 rag_data_dir = "data/rag"                            # RAG数据目录