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

changes.md

@@ -20,3 +20,4 @@
   - `chat_api.py`中获取流的参数中可以使用一个特殊的枚举类型来获得所有平台的 ChatStream 了。
   - `config_api.py`中的`get_global_config`和`get_plugin_config`方法现在支持嵌套访问的配置键名。
   - `database_api.py`中的`db_query`方法调整了参数顺序以增强参数限制的同时，保证了typing正确；`db_get`方法增加了`single_result`参数，与`db_query`保持一致。
+4. 现在增加了参数类型检查，完善了对应注释

scripts/import_openie.py

@@ -9,19 +9,17 @@ import os
 from time import sleep
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-
-from src.chat.knowledge.lpmmconfig import PG_NAMESPACE, global_config
 from src.chat.knowledge.embedding_store import EmbeddingManager
-from src.chat.knowledge.llm_client import LLMClient
 from src.chat.knowledge.open_ie import OpenIE
 from src.chat.knowledge.kg_manager import KGManager
 from src.common.logger import get_logger
 from src.chat.knowledge.utils.hash import get_sha256
+from src.manager.local_store_manager import local_storage
 
 
 # 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"] or os.path.join(ROOT_PATH, "data", "openie")
+OPENIE_DIR = os.path.join(ROOT_PATH, "data", "openie")
 
 logger = get_logger("OpenIE导入")
 
@@ -63,7 +61,7 @@ def hash_deduplicate(
     ):
         # 段落hash
         paragraph_hash = get_sha256(raw_paragraph)
-        if f"{PG_NAMESPACE}-{paragraph_hash}" in stored_pg_hashes and paragraph_hash in stored_paragraph_hashes:
+        if f"{local_storage['pg_namespace']}-{paragraph_hash}" in stored_pg_hashes and paragraph_hash in stored_paragraph_hashes:
             continue
         new_raw_paragraphs[paragraph_hash] = raw_paragraph
         new_triple_list_data[paragraph_hash] = triple_list
@@ -193,15 +191,9 @@ def main():  # sourcery skip: dict-comprehension
     logger.info("----开始导入openie数据----\n")
 
     logger.info("创建LLM客户端")
-    llm_client_list = {}
-    for key in global_config["llm_providers"]:
-        llm_client_list[key] = LLMClient(
-            global_config["llm_providers"][key]["base_url"],
-            global_config["llm_providers"][key]["api_key"],
-        )
 
     # 初始化Embedding库
-    embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
+    embed_manager = EmbeddingManager()
     logger.info("正在从文件加载Embedding库")
     try:
         embed_manager.load_from_file()
@@ -230,7 +222,7 @@ def main():  # sourcery skip: dict-comprehension
 
     # 数据比对：Embedding库与KG的段落hash集合
     for pg_hash in kg_manager.stored_paragraph_hashes:
-        key = f"{PG_NAMESPACE}-{pg_hash}"
+        key = f"{local_storage['pg_namespace']}-{pg_hash}"
         if key not in embed_manager.stored_pg_hashes:
             logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
 

scripts/info_extraction.py

@@ -4,7 +4,6 @@ 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__), "..")))
@@ -13,11 +12,9 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from rich.progress import Progress  # 替换为 rich 进度条
 
 from src.common.logger import get_logger
-from src.chat.knowledge.lpmmconfig import global_config
+# from src.chat.knowledge.lpmmconfig import global_config
 from src.chat.knowledge.ie_process import info_extract_from_str
-from src.chat.knowledge.llm_client import LLMClient
 from src.chat.knowledge.open_ie import OpenIE
-from src.chat.knowledge.raw_processing import load_raw_data
 from rich.progress import (
     BarColumn,
     TimeElapsedColumn,
@@ -27,24 +24,17 @@ from rich.progress import (
     SpinnerColumn,
     TextColumn,
 )
+from raw_data_preprocessor import RAW_DATA_PATH, process_multi_files, load_raw_data
+from src.config.config import global_config
+from src.llm_models.utils_model import LLMRequest
 
 logger = get_logger("LPMM知识库-信息提取")
 
 
 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"]["imported_data_path"] or os.path.join(
+# IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data", "imported_lpmm_data")
-    ROOT_PATH, "data", "imported_lpmm_data"
+OPENIE_OUTPUT_DIR = os.path.join(ROOT_PATH, "data", "openie")
-)
-OPENIE_OUTPUT_DIR = global_config["persistence"]["openie_data_path"] or 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()
-open_ie_doc_lock = Lock()
-
-# 创建一个事件标志，用于控制程序终止
-shutdown_event = Event()
-
 
 def ensure_dirs():
     """确保临时目录和输出目录存在"""
@@ -54,12 +44,26 @@ def ensure_dirs():
     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}")
-    if not os.path.exists(IMPORTED_DATA_PATH):
+    if not os.path.exists(RAW_DATA_PATH):
-        os.makedirs(IMPORTED_DATA_PATH)
+        os.makedirs(RAW_DATA_PATH)
-        logger.info(f"已创建导入数据目录: {IMPORTED_DATA_PATH}")
+        logger.info(f"已创建原始数据目录: {RAW_DATA_PATH}")
 
+# 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()
+open_ie_doc_lock = Lock()
 
-def process_single_text(pg_hash, raw_data, llm_client_list):
+# 创建一个事件标志，用于控制程序终止
+shutdown_event = Event()
+
+lpmm_entity_extract_llm = LLMRequest(
+    model=global_config.model.lpmm_entity_extract,
+    request_type="lpmm.entity_extract"
+)
+lpmm_rdf_build_llm = LLMRequest(
+    model=global_config.model.lpmm_rdf_build,
+    request_type="lpmm.rdf_build"
+)
+def process_single_text(pg_hash, raw_data):
     """处理单个文本的函数，用于线程池"""
     temp_file_path = f"{TEMP_DIR}/{pg_hash}.json"
 
@@ -77,8 +81,8 @@ def process_single_text(pg_hash, raw_data, llm_client_list):
                 os.remove(temp_file_path)
 
     entity_list, rdf_triple_list = info_extract_from_str(
-        llm_client_list[global_config["entity_extract"]["llm"]["provider"]],
+        lpmm_entity_extract_llm,
-        llm_client_list[global_config["rdf_build"]["llm"]["provider"]],
+        lpmm_rdf_build_llm,
         raw_data,
     )
     if entity_list is None or rdf_triple_list is None:
@@ -113,7 +117,7 @@ def signal_handler(_signum, _frame):
 def main():  # sourcery skip: comprehension-to-generator, extract-method
     # 设置信号处理器
     signal.signal(signal.SIGINT, signal_handler)
-
+    ensure_dirs()  # 确保目录存在
     # 新增用户确认提示
     print("=== 重要操作确认，请认真阅读以下内容哦 ===")
     print("实体提取操作将会花费较多api余额和时间，建议在空闲时段执行。")
@@ -130,50 +134,17 @@ def main():  # sourcery skip: comprehension-to-generator, extract-method
     ensure_dirs()  # 确保目录存在
     logger.info("--------进行信息提取--------\n")
 
-    logger.info("创建LLM客户端")
+    # 加载原始数据
-    llm_client_list = {
+    logger.info("正在加载原始数据")
-        key: LLMClient(
+    all_sha256_list, all_raw_datas = load_raw_data()
-            global_config["llm_providers"][key]["base_url"],
-            global_config["llm_providers"][key]["api_key"],
-        )
-        for key in global_config["llm_providers"]
-    }
-    # 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}")
-
-    # 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 = []
 
-    workers = global_config["info_extraction"]["workers"]
+    workers = global_config.lpmm_knowledge.info_extraction_workers
     with ThreadPoolExecutor(max_workers=workers) as executor:
         future_to_hash = {
-            executor.submit(process_single_text, pg_hash, raw_data, llm_client_list): pg_hash
+            executor.submit(process_single_text, pg_hash, raw_data): pg_hash
             for pg_hash, raw_data in zip(all_sha256_list, all_raw_datas, strict=False)
         }
 

scripts/raw_data_preprocessor.py

@@ -1,40 +1,16 @@
-import json
 import os
 from pathlib import Path
 import sys  # 新增系统模块导入
-import datetime  # 新增导入
+from src.chat.knowledge.utils.hash import get_sha256
-
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from src.common.logger import get_logger
-from src.chat.knowledge.lpmmconfig import global_config
 
 logger = get_logger("lpmm")
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 RAW_DATA_PATH = os.path.join(ROOT_PATH, "data/lpmm_raw_data")
-# 新增：确保 RAW_DATA_PATH 存在
+# IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data/imported_lpmm_data")
-if not os.path.exists(RAW_DATA_PATH):
-    os.makedirs(RAW_DATA_PATH, exist_ok=True)
-    logger.info(f"已创建目录: {RAW_DATA_PATH}")
 
-if global_config.get("persistence", {}).get("raw_data_path") is not None:
+def _process_text_file(file_path):
-    IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, global_config["persistence"]["raw_data_path"])
-else:
-    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 = [RAW_DATA_PATH, IMPORTED_DATA_PATH]
-
-    for dir_path in required_dirs:
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-            logger.info(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:
         raw = f.read()
@@ -55,54 +31,45 @@ def process_text_file(file_path):
     return paragraphs
 
 
-def main():
+def _process_multi_files() -> list:
-    # 新增用户确认提示
-    print("=== 数据预处理脚本 ===")
-    print(f"本脚本将处理 '{RAW_DATA_PATH}' 目录下的所有 .txt 文件。")
-    print(f"处理后的段落数据将合并，并以 MM-DD-HH-SS-imported-data.json 的格式保存在 '{IMPORTED_DATA_PATH}' 目录中。")
-    print("请确保原始数据已放置在正确的目录中。")
-    confirm = input("确认继续执行？(y/n): ").strip().lower()
-    if confirm != "y":
-        logger.info("操作已取消")
-        sys.exit(1)
-    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(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(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(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)
-
     # 处理所有文件
     all_paragraphs = []
     for file in raw_files:
         logger.info(f"正在处理文件: {file.name}")
-        paragraphs = process_text_file(file)
+        paragraphs = _process_text_file(file)
         all_paragraphs.extend(paragraphs)
+    return all_paragraphs
 
-    # 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
+def load_raw_data() -> tuple[list[str], list[str]]:
-    now = datetime.datetime.now()
+    """加载原始数据文件
-    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)
 
-    logger.info(f"处理完成，结果已保存到: {output_path}")
+    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
 
-if __name__ == "__main__":
+    Returns:
-    logger.info(f"原始数据路径: {RAW_DATA_PATH}")
+        - raw_data: 原始数据列表
-    logger.info(f"处理后的数据路径: {IMPORTED_DATA_PATH}")
+        - sha256_list: 原始数据的SHA256集合
-    main()
+    """
+    raw_data = _process_multi_files()
+    sha256_list = []
+    sha256_set = set()
+    for item in raw_data:
+        if not isinstance(item, str):
+            logger.warning(f"数据类型错误：{item}")
+            continue
+        pg_hash = get_sha256(item)
+        if pg_hash in sha256_set:
+            logger.warning(f"重复数据：{item}")
+            continue
+        sha256_set.add(pg_hash)
+        sha256_list.append(pg_hash)
+        raw_data.append(item)
+    logger.info(f"共读取到{len(raw_data)}条数据")
+
+    return sha256_list, raw_data

src/chat/emoji_system/emoji_manager.py

@@ -47,7 +47,7 @@ class MaiEmoji:
         self.embedding = []
         self.hash = ""  # 初始为空，在创建实例时会计算
         self.description = ""
-        self.emotion = []
+        self.emotion: List[str] = []
         self.usage_count = 0
         self.last_used_time = time.time()
         self.register_time = time.time()

src/chat/focus_chat/heartFC_chat.py

@@ -243,6 +243,8 @@ class HeartFChatting:
             loop_start_time = time.time()
             await self.relationship_builder.build_relation()
 
+            available_actions = {}
+
             # 第一步：动作修改
             with Timer("动作修改", cycle_timers):
                 try:

src/chat/knowledge/embedding_store.py

@@ -10,8 +10,8 @@ import pandas as pd
 # import tqdm
 import faiss
 
-from .llm_client import LLMClient
+# from .llm_client import LLMClient
-from .lpmmconfig import ENT_NAMESPACE, PG_NAMESPACE, REL_NAMESPACE, global_config
+from .lpmmconfig import global_config
 from .utils.hash import get_sha256
 from .global_logger import logger
 from rich.traceback import install
@@ -25,6 +25,9 @@ from rich.progress import (
     SpinnerColumn,
     TextColumn,
 )
+from src.manager.local_store_manager import local_storage
+from src.chat.utils.utils import get_embedding
+
 
 install(extra_lines=3)
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
@@ -86,21 +89,20 @@ class EmbeddingStoreItem:
 
 
 class EmbeddingStore:
-    def __init__(self, llm_client: LLMClient, namespace: str, dir_path: str):
+    def __init__(self, namespace: str, dir_path: str):
         self.namespace = namespace
-        self.llm_client = llm_client
         self.dir = dir_path
-        self.embedding_file_path = dir_path + "/" + namespace + ".parquet"
+        self.embedding_file_path = f"{dir_path}/{namespace}.parquet"
-        self.index_file_path = dir_path + "/" + namespace + ".index"
+        self.index_file_path = f"{dir_path}/{namespace}.index"
         self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json"
 
-        self.store = dict()
+        self.store = {}
 
         self.faiss_index = None
         self.idx2hash = None
 
     def _get_embedding(self, s: str) -> List[float]:
-        return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s)
+        return get_embedding(s)
 
     def get_test_file_path(self):
         return EMBEDDING_TEST_FILE
@@ -293,20 +295,17 @@ class EmbeddingStore:
 
 
 class EmbeddingManager:
-    def __init__(self, llm_client: LLMClient):
+    def __init__(self):
         self.paragraphs_embedding_store = EmbeddingStore(
-            llm_client,
+            local_storage['pg_namespace'],
-            PG_NAMESPACE,
             EMBEDDING_DATA_DIR_STR,
         )
         self.entities_embedding_store = EmbeddingStore(
-            llm_client,
+            local_storage['pg_namespace'],
-            ENT_NAMESPACE,
             EMBEDDING_DATA_DIR_STR,
         )
         self.relation_embedding_store = EmbeddingStore(
-            llm_client,
+            local_storage['pg_namespace'],
-            REL_NAMESPACE,
             EMBEDDING_DATA_DIR_STR,
         )
         self.stored_pg_hashes = set()

src/chat/knowledge/ie_process.py

@@ -4,28 +4,35 @@ from typing import List, Union
 
 from .global_logger import logger
 from . import prompt_template
-from .lpmmconfig import global_config, INVALID_ENTITY
+from .knowledge_lib import INVALID_ENTITY
-from .llm_client import LLMClient
+from src.llm_models.utils_model import LLMRequest
-from src.chat.knowledge.utils.json_fix import new_fix_broken_generated_json
+from json_repair import repair_json
+def _extract_json_from_text(text: str) -> dict:
+    """从文本中提取JSON数据的高容错方法"""
+    try:
+        fixed_json = repair_json(text)
+        if isinstance(fixed_json, str):
+            parsed_json = json.loads(fixed_json)
+        else:
+            parsed_json = fixed_json
 
+        if isinstance(parsed_json, list) and parsed_json:
+            parsed_json = parsed_json[0]
 
-def _entity_extract(llm_client: LLMClient, paragraph: str) -> List[str]:
+        if isinstance(parsed_json, dict):
+            return parsed_json
+
+    except Exception as e:
+        logger.error(f"JSON提取失败: {e}, 原始文本: {text[:100]}...")
+
+def _entity_extract(llm_req: LLMRequest, paragraph: str) -> 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_entity_extract_context(paragraph)
-    _, request_result = llm_client.send_chat_request(
+    response, (reasoning_content, model_name) = llm_req.generate_response_async(entity_extract_context)
-        global_config["entity_extract"]["llm"]["model"], entity_extract_context
-    )
-
-    # 去除‘{’前的内容（结果中可能有多个‘{’）
-    if "[" in request_result:
-        request_result = request_result[request_result.index("[") :]
-
-    # 去除最后一个‘}’后的内容（结果中可能有多个‘}’）
-    if "]" in request_result:
-        request_result = request_result[: request_result.rindex("]") + 1]
-
-    entity_extract_result = json.loads(new_fix_broken_generated_json(request_result))
 
+    entity_extract_result = _extract_json_from_text(response)
+    # 尝试load JSON数据
+    json.loads(entity_extract_result)
     entity_extract_result = [
         entity
         for entity in entity_extract_result
@@ -38,23 +45,16 @@ def _entity_extract(llm_client: LLMClient, paragraph: str) -> List[str]:
     return entity_extract_result
 
 
-def _rdf_triple_extract(llm_client: LLMClient, paragraph: str, entities: list) -> List[List[str]]:
+def _rdf_triple_extract(llm_req: LLMRequest, 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(
+    rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
         paragraph, entities=json.dumps(entities, ensure_ascii=False)
     )
-    _, request_result = llm_client.send_chat_request(global_config["rdf_build"]["llm"]["model"], entity_extract_context)
+    response, (reasoning_content, model_name) = llm_req.generate_response_async(rdf_extract_context)
-
-    # 去除‘{’前的内容（结果中可能有多个‘{’）
-    if "[" in request_result:
-        request_result = request_result[request_result.index("[") :]
-
-    # 去除最后一个‘}’后的内容（结果中可能有多个‘}’）
-    if "]" in request_result:
-        request_result = request_result[: request_result.rindex("]") + 1]
-
-    entity_extract_result = json.loads(new_fix_broken_generated_json(request_result))
 
+    entity_extract_result = _extract_json_from_text(response)
+    # 尝试load JSON数据
+    json.loads(entity_extract_result)
     for triple in entity_extract_result:
         if 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")
@@ -63,7 +63,7 @@ def _rdf_triple_extract(llm_client: LLMClient, paragraph: str, entities: list) -
 
 
 def info_extract_from_str(
-    llm_client_for_ner: LLMClient, llm_client_for_rdf: LLMClient, paragraph: str
+    llm_client_for_ner: LLMRequest, llm_client_for_rdf: LLMRequest, paragraph: str
 ) -> Union[tuple[None, None], tuple[list[str], list[list[str]]]]:
     try_count = 0
     while True:

src/chat/knowledge/kg_manager.py

@@ -20,24 +20,37 @@ from quick_algo import di_graph, pagerank
 
 from .utils.hash import get_sha256
 from .embedding_store import EmbeddingManager, EmbeddingStoreItem
-from .lpmmconfig import (
+from .lpmmconfig import global_config
-    ENT_NAMESPACE,
+from src.manager.local_store_manager import local_storage
-    PG_NAMESPACE,
-    RAG_ENT_CNT_NAMESPACE,
-    RAG_GRAPH_NAMESPACE,
-    RAG_PG_HASH_NAMESPACE,
-    global_config,
-)
 
 from .global_logger import logger
 
-ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+
-KG_DIR = (
+def _get_kg_dir():
-    os.path.join(ROOT_PATH, "data/rag")
+    """
-    if global_config["persistence"]["rag_data_dir"] is None
+    安全地获取KG数据目录路径
-    else os.path.join(ROOT_PATH, global_config["persistence"]["rag_data_dir"])
+    """
-)
+    root_path = local_storage['root_path']
-KG_DIR_STR = str(KG_DIR).replace("\\", "/")
+    if root_path is None:
+        # 如果 local_storage 中没有 root_path，使用当前文件的相对路径作为备用
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        root_path = os.path.abspath(os.path.join(current_dir, "..", "..", ".."))
+        logger.warning(f"local_storage 中未找到 root_path，使用备用路径: {root_path}")
+    
+    # 获取RAG数据目录
+    rag_data_dir = global_config["persistence"]["rag_data_dir"]
+    if rag_data_dir is None:
+        kg_dir = os.path.join(root_path, "data/rag")
+    else:
+        kg_dir = os.path.join(root_path, rag_data_dir)
+    
+    return str(kg_dir).replace("\\", "/")
+
+
+# 延迟初始化，避免在模块加载时就访问可能未初始化的 local_storage
+def get_kg_dir_str():
+    """获取KG目录字符串"""
+    return _get_kg_dir()
 
 
 class KGManager:
@@ -46,15 +59,15 @@ class KGManager:
         # 存储段落的hash值，用于去重
         self.stored_paragraph_hashes = set()
         # 实体出现次数
-        self.ent_appear_cnt = dict()
+        self.ent_appear_cnt = {}
         # KG
         self.graph = di_graph.DiGraph()
 
-        # 持久化相关
+        # 持久化相关 - 使用延迟初始化的路径
-        self.dir_path = KG_DIR_STR
+        self.dir_path = get_kg_dir_str()
-        self.graph_data_path = self.dir_path + "/" + RAG_GRAPH_NAMESPACE + ".graphml"
+        self.graph_data_path = self.dir_path + "/" + local_storage['rag_graph_namespace'] + ".graphml"
-        self.ent_cnt_data_path = self.dir_path + "/" + RAG_ENT_CNT_NAMESPACE + ".parquet"
+        self.ent_cnt_data_path = self.dir_path + "/" + local_storage['rag_ent_cnt_namespace'] + ".parquet"
-        self.pg_hash_file_path = self.dir_path + "/" + RAG_PG_HASH_NAMESPACE + ".json"
+        self.pg_hash_file_path = self.dir_path + "/" + local_storage['rag_pg_hash_namespace'] + ".json"
 
     def save_to_file(self):
         """将KG数据保存到文件"""
@@ -109,8 +122,8 @@ class KGManager:
                     # 避免自连接
                     continue
                 # 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_key1 = local_storage['ent_namespace'] + "-" + get_sha256(triple[0])
-                hash_key2 = ENT_NAMESPACE + "-" + get_sha256(triple[2])
+                hash_key2 = local_storage['ent_namespace'] + "-" + get_sha256(triple[2])
                 node_to_node[(hash_key1, hash_key2)] = 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
                 entity_set.add(hash_key1)
@@ -128,8 +141,8 @@ class KGManager:
         """构建实体节点与文段节点之间的关系"""
         for idx in triple_list_data:
             for triple in triple_list_data[idx]:
-                ent_hash_key = ENT_NAMESPACE + "-" + get_sha256(triple[0])
+                ent_hash_key = local_storage['ent_namespace'] + "-" + get_sha256(triple[0])
-                pg_hash_key = PG_NAMESPACE + "-" + str(idx)
+                pg_hash_key = local_storage['pg_namespace'] + "-" + str(idx)
                 node_to_node[(ent_hash_key, pg_hash_key)] = node_to_node.get((ent_hash_key, pg_hash_key), 0) + 1.0
 
     @staticmethod
@@ -144,8 +157,8 @@ class KGManager:
         ent_hash_list = set()
         for triple_list in triple_list_data.values():
             for triple in triple_list:
-                ent_hash_list.add(ENT_NAMESPACE + "-" + get_sha256(triple[0]))
+                ent_hash_list.add(local_storage['ent_namespace'] + "-" + get_sha256(triple[0]))
-                ent_hash_list.add(ENT_NAMESPACE + "-" + get_sha256(triple[2]))
+                ent_hash_list.add(local_storage['ent_namespace'] + "-" + get_sha256(triple[2]))
         ent_hash_list = list(ent_hash_list)
 
         synonym_hash_set = set()
@@ -250,7 +263,7 @@ class KGManager:
         for src_tgt in node_to_node.keys():
             for node_hash in src_tgt:
                 if node_hash not in existed_nodes:
-                    if node_hash.startswith(ENT_NAMESPACE):
+                    if node_hash.startswith(local_storage['ent_namespace']):
                         # 新增实体节点
                         node = embedding_manager.entities_embedding_store.store[node_hash]
                         assert isinstance(node, EmbeddingStoreItem)
@@ -259,7 +272,7 @@ class KGManager:
                         node_item["type"] = "ent"
                         node_item["create_time"] = now_time
                         self.graph.update_node(node_item)
-                    elif node_hash.startswith(PG_NAMESPACE):
+                    elif node_hash.startswith(local_storage['pg_namespace']):
                         # 新增文段节点
                         node = embedding_manager.paragraphs_embedding_store.store[node_hash]
                         assert isinstance(node, EmbeddingStoreItem)
@@ -340,7 +353,7 @@ class KGManager:
             # 关系三元组
             triple = relation[2:-2].split("', '")
             for ent in [(triple[0]), (triple[2])]:
-                ent_hash = ENT_NAMESPACE + "-" + get_sha256(ent)
+                ent_hash = local_storage['ent_namespace'] + "-" + get_sha256(ent)
                 if ent_hash in existed_nodes:  # 该实体需在KG中存在
                     if ent_hash not in ent_sim_scores:  # 尚未记录的实体
                         ent_sim_scores[ent_hash] = []
@@ -418,7 +431,7 @@ class KGManager:
         # 获取最终结果
         # 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() if node_key.startswith(PG_NAMESPACE)
+            (node_key, score) for node_key, score in ppr_res.items() if node_key.startswith(local_storage['pg_namespace'])
         ]
         del ppr_res
 

src/chat/knowledge/knowledge_lib.py

@@ -1,4 +1,4 @@
-from src.chat.knowledge.lpmmconfig import PG_NAMESPACE, global_config
+from src.chat.knowledge.lpmmconfig import global_config
 from src.chat.knowledge.embedding_store import EmbeddingManager
 from src.chat.knowledge.llm_client import LLMClient
 from src.chat.knowledge.mem_active_manager import MemoryActiveManager
@@ -6,10 +6,80 @@ from src.chat.knowledge.qa_manager import QAManager
 from src.chat.knowledge.kg_manager import KGManager
 from src.chat.knowledge.global_logger import logger
 from src.config.config import global_config as bot_global_config
-# try:
+from src.manager.local_store_manager import local_storage
-#     import quick_algo
+import os
-# except ImportError:
+
-#     print("quick_algo not found, please install it first")
+INVALID_ENTITY = [
+    "",
+    "你",
+    "他",
+    "她",
+    "它",
+    "我们",
+    "你们",
+    "他们",
+    "她们",
+    "它们",
+]
+PG_NAMESPACE = "paragraph"
+ENT_NAMESPACE = "entity"
+REL_NAMESPACE = "relation"
+
+RAG_GRAPH_NAMESPACE = "rag-graph"
+RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
+RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
+
+
+ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+DATA_PATH = os.path.join(ROOT_PATH, "data")
+
+def _initialize_knowledge_local_storage():
+    """
+    初始化知识库相关的本地存储配置
+    使用字典批量设置，避免重复的if判断
+    """
+    # 定义所有需要初始化的配置项
+    default_configs = {
+        # 路径配置
+        'root_path': ROOT_PATH,
+        'data_path': f"{ROOT_PATH}/data",
+        
+        # 实体和命名空间配置
+        'lpmm_invalid_entity': INVALID_ENTITY,
+        'pg_namespace': PG_NAMESPACE,
+        'ent_namespace': ENT_NAMESPACE,
+        'rel_namespace': REL_NAMESPACE,
+        
+        # RAG相关命名空间配置
+        'rag_graph_namespace': RAG_GRAPH_NAMESPACE,
+        'rag_ent_cnt_namespace': RAG_ENT_CNT_NAMESPACE,
+        'rag_pg_hash_namespace': RAG_PG_HASH_NAMESPACE
+    }
+    
+    # 日志级别映射：重要配置用info，其他用debug
+    important_configs = {'root_path', 'data_path'}
+    
+    # 批量设置配置项
+    initialized_count = 0
+    for key, default_value in default_configs.items():
+        if local_storage[key] is None:
+            local_storage[key] = default_value
+            
+            # 根据重要性选择日志级别
+            if key in important_configs:
+                logger.info(f"设置{key}: {default_value}")
+            else:
+                logger.debug(f"设置{key}: {default_value}")
+            
+            initialized_count += 1
+    
+    if initialized_count > 0:
+        logger.info(f"知识库本地存储初始化完成，共设置 {initialized_count} 项配置")
+    else:
+        logger.debug("知识库本地存储配置已存在，跳过初始化")
+    
+# 初始化本地存储路径
+_initialize_knowledge_local_storage()
 
 # 检查LPMM知识库是否启用
 if bot_global_config.lpmm_knowledge.enable:
@@ -23,7 +93,7 @@ if bot_global_config.lpmm_knowledge.enable:
         )
 
     # 初始化Embedding库
-    embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]])
+    embed_manager = EmbeddingManager()
     logger.info("正在从文件加载Embedding库")
     try:
         embed_manager.load_from_file()
@@ -54,9 +124,6 @@ if bot_global_config.lpmm_knowledge.enable:
     qa_manager = QAManager(
         embed_manager,
         kg_manager,
-        llm_client_list[global_config["embedding"]["provider"]],
-        llm_client_list[global_config["qa"]["llm"]["provider"]],
-        llm_client_list[global_config["qa"]["llm"]["provider"]],
     )
 
     # 记忆激活（用于记忆库）

src/chat/knowledge/open_ie.py

@@ -4,9 +4,8 @@ import glob
 from typing import Any, Dict, List
 
 
-from .lpmmconfig import INVALID_ENTITY, global_config
+from .knowledge_lib import INVALID_ENTITY, ROOT_PATH, DATA_PATH
-
+# from src.manager.local_store_manager import local_storage
-ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
 
 
 def _filter_invalid_entities(entities: List[str]) -> List[str]:
@@ -107,7 +106,7 @@ class OpenIE:
     @staticmethod
     def load() -> "OpenIE":
         """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"])
+        openie_dir = os.path.join(DATA_PATH, "openie")
         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")))
@@ -122,12 +121,6 @@ class OpenIE:
         openie_data = OpenIE._from_dict(data_list)
         return openie_data
 
-    @staticmethod
-    def save(openie_data: "OpenIE"):
-        """保存OpenIE数据到文件"""
-        with open(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):
         """提取实体列表"""
         ner_output_dict = dict(

src/chat/knowledge/qa_manager.py

@@ -5,11 +5,13 @@ from .global_logger import logger
 
 # from . import prompt_template
 from .embedding_store import EmbeddingManager
-from .llm_client import LLMClient
+# from .llm_client import LLMClient
 from .kg_manager import KGManager
-from .lpmmconfig import global_config
+# from .lpmmconfig import global_config
 from .utils.dyn_topk import dyn_select_top_k
-
+from src.llm_models.utils_model import LLMRequest
+from src.chat.utils.utils import get_embedding
+from src.config.config import global_config
 
 MAX_KNOWLEDGE_LENGTH = 10000  # 最大知识长度
 
@@ -19,26 +21,25 @@ class QAManager:
         self,
         embed_manager: EmbeddingManager,
         kg_manager: KGManager,
-        llm_client_embedding: LLMClient,
+
-        llm_client_filter: LLMClient,
-        llm_client_qa: LLMClient,
     ):
         self.embed_manager = embed_manager
         self.kg_manager = kg_manager
-        self.llm_client_list = {
+        # TODO: API-Adapter修改标记
-            "embedding": llm_client_embedding,
+        self.qa_model = LLMRequest(
-            "message_filter": llm_client_filter,
+            model=global_config.model.lpmm_qa,
-            "qa": llm_client_qa,
+            request_type="lpmm.qa"
-        }
+        )
 
     def process_query(self, question: str) -> Tuple[List[Tuple[str, float, float]], Optional[Dict[str, float]]]:
         """处理查询"""
 
         # 生成问题的Embedding
         part_start_time = time.perf_counter()
-        question_embedding = self.llm_client_list["embedding"].send_embedding_request(
+        question_embedding = get_embedding(question)
-            global_config["embedding"]["model"], question
+        if question_embedding is None:
-        )
+            logger.error("生成问题Embedding失败")
+            return None
         part_end_time = time.perf_counter()
         logger.debug(f"Embedding用时：{part_end_time - part_start_time:.5f}s")
 
@@ -46,14 +47,15 @@ class QAManager:
         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"],
+            global_config.lpmm_knowledge.qa_relation_search_top_k,
         )
         if relation_search_res is not None:
             # 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 relation_search_res[0][1] < global_config["qa"]["params"]["relation_threshold"]:
+            if relation_search_res[0][1] < global_config.lpmm_knowledge.qa_relation_threshold:
                 # 未找到相关关系
+                logger.debug("未找到相关关系，跳过关系检索")
                 relation_search_res = []
 
             part_end_time = time.perf_counter()
@@ -71,7 +73,7 @@ class QAManager:
             part_start_time = time.perf_counter()
             paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
                 question_embedding,
-                global_config["qa"]["params"]["paragraph_search_top_k"],
+                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")

src/chat/message_receive/uni_message_sender.py

@@ -38,7 +38,9 @@ class HeartFCSender:
     def __init__(self):
         self.storage = MessageStorage()
 
-    async def send_message(self, message: MessageSending, typing=False, set_reply=False, storage_message=True, show_log=True):
+    async def send_message(
+        self, message: MessageSending, typing=False, set_reply=False, storage_message=True, show_log=True
+    ):
         """
         处理、发送并存储一条消息。
 

src/chat/planner_actions/planner.py

@@ -79,7 +79,9 @@ class ActionPlanner:
 
         self.last_obs_time_mark = 0.0
 
-    async def plan(self, mode: ChatMode = ChatMode.FOCUS) -> Dict[str, Dict[str, Any] | str]:  # sourcery skip: dict-comprehension
+    async def plan(
+        self, mode: ChatMode = ChatMode.FOCUS
+    ) -> Dict[str, Dict[str, Any] | str]:  # sourcery skip: dict-comprehension
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
         """

src/chat/replyer/default_generator.py

@@ -507,7 +507,7 @@ class DefaultReplyer:
         # 构建背景对话 prompt
         background_dialogue_prompt = ""
         if background_dialogue_list:
-            latest_25_msgs = background_dialogue_list[-int(global_config.chat.max_context_size*0.6):]
+            latest_25_msgs = background_dialogue_list[-int(global_config.chat.max_context_size * 0.6) :]
             background_dialogue_prompt_str = build_readable_messages(
                 latest_25_msgs,
                 replace_bot_name=True,
@@ -520,7 +520,7 @@ class DefaultReplyer:
         # 构建核心对话 prompt
         core_dialogue_prompt = ""
         if core_dialogue_list:
-            core_dialogue_list = core_dialogue_list[-int(global_config.chat.max_context_size*2):]  # 限制消息数量
+            core_dialogue_list = core_dialogue_list[-int(global_config.chat.max_context_size * 2) :]  # 限制消息数量
 
             core_dialogue_prompt_str = build_readable_messages(
                 core_dialogue_list,
@@ -585,7 +585,6 @@ class DefaultReplyer:
             limit=global_config.chat.max_context_size * 2,
         )
 
-            
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_id,
             timestamp=time.time(),
@@ -712,8 +711,6 @@ class DefaultReplyer:
             # 根据sender通过person_info_manager反向查找person_id，再获取user_id
             person_id = person_info_manager.get_person_id_by_person_name(sender)
 
-
-
         # 根据配置选择使用哪种 prompt 构建模式
         if global_config.chat.use_s4u_prompt_mode and person_id:
             # 使用 s4u 对话构建模式：分离当前对话对象和其他对话
@@ -725,7 +722,6 @@ class DefaultReplyer:
                 logger.warning(f"无法从person_id {person_id} 获取user_id: {e}")
                 target_user_id = ""
 
-            
             # 构建分离的对话 prompt
             core_dialogue_prompt, background_dialogue_prompt = self.build_s4u_chat_history_prompts(
                 message_list_before_now_long, target_user_id

src/config/official_configs.py

@@ -106,7 +106,6 @@ class ChatConfig(ConfigBase):
     focus_value: float = 1.0
     """麦麦的专注思考能力，越低越容易专注，消耗token也越多"""
 
-
     def get_current_talk_frequency(self, chat_stream_id: Optional[str] = None) -> float:
         """
         根据当前时间和聊天流获取对应的 talk_frequency
@@ -246,6 +245,7 @@ class ChatConfig(ConfigBase):
         except (ValueError, IndexError):
             return None
 
+
 @dataclass
 class MessageReceiveConfig(ConfigBase):
     """消息接收配置类"""
@@ -274,8 +274,6 @@ class NormalChatConfig(ConfigBase):
     """@bot 必然回复"""
 
 
-
-
 @dataclass
 class ExpressionConfig(ConfigBase):
     """表达配置类"""
@@ -627,3 +625,12 @@ class ModelConfig(ConfigBase):
 
     embedding: dict[str, Any] = field(default_factory=lambda: {})
     """嵌入模型配置"""
+
+    lpmm_entity_extract: dict[str, Any] = field(default_factory=lambda: {})
+    """LPMM实体提取模型配置"""
+
+    lpmm_rdf_build: dict[str, Any] = field(default_factory=lambda: {})
+    """LPMM RDF构建模型配置"""
+
+    lpmm_qa: dict[str, Any] = field(default_factory=lambda: {})
+    """LPMM问答模型配置"""

src/individuality/individuality.py

@@ -41,10 +41,10 @@ class Individuality:
             personality_side: 人格侧面描述
             identity: 身份细节描述
         """
-        bot_nickname=global_config.bot.nickname
+        bot_nickname = global_config.bot.nickname
-        personality_core=global_config.personality.personality_core
+        personality_core = global_config.personality.personality_core
-        personality_side=global_config.personality.personality_side
+        personality_side = global_config.personality.personality_side
-        identity=global_config.personality.identity
+        identity = global_config.personality.identity
 
         logger.info("正在初始化个体特征")
         person_info_manager = get_person_info_manager()
@@ -146,7 +146,6 @@ class Individuality:
         else:
             logger.error("人设构建失败")
 
-
     async def get_personality_block(self) -> str:
         person_info_manager = get_person_info_manager()
         bot_person_id = person_info_manager.get_person_id("system", "bot_id")
@@ -178,7 +177,6 @@ class Individuality:
 
         return identity_block
 
-
     def _get_config_hash(
         self, bot_nickname: str, personality_core: str, personality_side: str, identity: list
     ) -> tuple[str, str]:
@@ -273,7 +271,6 @@ class Individuality:
         except IOError as e:
             logger.error(f"保存meta_info文件失败: {e}")
 
-
     async def _create_personality(self, personality_core: str, personality_side: str) -> str:
         # sourcery skip: merge-list-append, move-assign
         """使用LLM创建压缩版本的impression

src/individuality/personality.py

@@ -40,7 +40,15 @@ class Personality:
         return cls._instance
 
     @classmethod
-    def initialize(cls, bot_nickname: str, personality_core: str, personality_side: str, identity: List[str] = None, compress_personality: bool = True, compress_identity: bool = True) -> "Personality":
+    def initialize(
+        cls,
+        bot_nickname: str,
+        personality_core: str,
+        personality_side: str,
+        identity: List[str] = None,
+        compress_personality: bool = True,
+        compress_identity: bool = True,
+    ) -> "Personality":
         """初始化人格特质
 
         Args:

src/mais4u/mais4u_chat/loading.py

@@ -1,6 +1,7 @@
 
 from src.plugin_system.apis import send_api
 
+
 async def send_loading(chat_id: str, content: str):
     await send_api.custom_to_stream(
         message_type="loading",
@@ -10,6 +11,7 @@ async def send_loading(chat_id: str, content: str):
         show_log=True,
     )
 
+
 async def send_unloading(chat_id: str):
     await send_api.custom_to_stream(
         message_type="loading",
@@ -18,4 +20,3 @@ async def send_unloading(chat_id: str):
         storage_message=False,
         show_log=True,
     )
-    

src/mais4u/mais4u_chat/s4u_mood_manager.py

@@ -322,7 +322,7 @@ class ChatMood:
             "joy": mood_values.get("joy", 5),
             "anger": mood_values.get("anger", 1),
             "sorrow": mood_values.get("sorrow", 1),
-            "fear": mood_values.get("fear", 1)
+            "fear": mood_values.get("fear", 1),
         }
 
         await send_api.custom_to_stream(
@@ -379,14 +379,18 @@ class MoodRegressionTask(AsyncTask):
                     logger.debug(f"[回归任务] {chat_info} 已达到最大回归次数(3次)，停止回归")
                     continue
 
-                logger.info(f"[回归任务] {chat_info} 开始情绪回归 ({regress_reason}，第{mood.regression_count + 1}次回归)")
+                logger.info(
+                    f"[回归任务] {chat_info} 开始情绪回归 ({regress_reason}，第{mood.regression_count + 1}次回归)"
+                )
                 await mood.regress_mood()
                 regression_executed += 1
             else:
                 if has_extreme_emotion:
                     remaining_time = 5 - time_since_last_change
                     high_emotion_str = ", ".join([f"{k}={v}" for k, v in high_emotions.items()])
-                    logger.debug(f"[回归任务] {chat_info} 存在极端情绪({high_emotion_str})，距离快速回归还需等待{int(remaining_time)}秒")
+                    logger.debug(
+                        f"[回归任务] {chat_info} 存在极端情绪({high_emotion_str})，距离快速回归还需等待{int(remaining_time)}秒"
+                    )
                 else:
                     remaining_time = 120 - time_since_last_change
                     logger.debug(f"[回归任务] {chat_info} 距离回归还需等待{int(remaining_time)}秒")

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -107,7 +107,6 @@ class S4UStreamGenerator:
         model_name: str,
         **kwargs,
     ) -> AsyncGenerator[str, None]:
-
         buffer = ""
         delimiters = "，。！？,.!?\n\r"  # For final trimming
         punctuation_buffer = ""

src/mais4u/mais4u_chat/s4u_watching_manager.py

@@ -43,6 +43,7 @@ logger = get_logger("watching")
 
 class WatchingState(Enum):
     """视线状态枚举"""
+
     WANDERING = "wandering"  # 随意看
     DANMU = "danmu"  # 看弹幕
     LENS = "lens"  # 看镜头
@@ -109,20 +110,14 @@ class ChatWatching:
         await asyncio.sleep(self.danmu_viewing_duration)
 
         # 检查是否仍需要切换（可能状态已经被其他事件改变）
-        if (self.reply_finished_time is not None and 
+        if self.reply_finished_time is not None and self.current_state == WatchingState.DANMU and not self.is_replying:
-            self.current_state == WatchingState.DANMU and
-            not self.is_replying):
-            
             await self._change_state(WatchingState.LENS, "看弹幕时间结束")
             self.reply_finished_time = None  # 重置完成时间
 
     async def _send_watching_update(self):
         """立即发送视线状态更新"""
         await send_api.custom_to_stream(
-            message_type="watching",
+            message_type="watching", content=self.current_state.value, stream_id=self.chat_id, storage_message=False
-            content=self.current_state.value,
-            stream_id=self.chat_id,
-            storage_message=False
         )
 
         logger.info(f"[{self.chat_id}] 发送视线状态更新: {self.current_state.value}")
@@ -139,11 +134,10 @@ class ChatWatching:
             "current_state": self.current_state.value,
             "is_replying": self.is_replying,
             "reply_finished_time": self.reply_finished_time,
-            "state_needs_update": self.state_needs_update
+            "state_needs_update": self.state_needs_update,
         }
 
 
-
 class WatchingManager:
     def __init__(self):
         self.watching_list: list[ChatWatching] = []
@@ -200,10 +194,7 @@ class WatchingManager:
 
     def get_all_watching_info(self) -> dict:
         """获取所有聊天的视线状态信息（用于调试）"""
-        return {
+        return {watching.chat_id: watching.get_state_info() for watching in self.watching_list}
-            watching.chat_id: watching.get_state_info() 
-            for watching in self.watching_list
-        }
 
 
 # 全局视线管理器实例

src/mood/mood_manager.py

@@ -92,7 +92,7 @@ class ChatMood:
             chat_id=self.chat_id,
             timestamp_start=self.last_change_time,
             timestamp_end=message_time,
-            limit=int(global_config.chat.max_context_size/3),
+            limit=int(global_config.chat.max_context_size / 3),
             limit_mode="last",
         )
         chat_talking_prompt = build_readable_messages(
@@ -121,8 +121,6 @@ class ChatMood:
             mood_state=self.mood_state,
         )
 
-
-
         response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
         if global_config.debug.show_prompt:
             logger.info(f"{self.log_prefix} prompt: {prompt}")
@@ -170,7 +168,6 @@ class ChatMood:
             mood_state=self.mood_state,
         )
 
-        
         response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
 
         if global_config.debug.show_prompt:

src/plugin_system/apis/chat_api.py

@@ -39,7 +39,12 @@ class ChatManager:
 
         Returns:
             List[ChatStream]: 聊天流列表
+
+        Raises:
+            TypeError: 如果 platform 不是字符串或 SpecialTypes 枚举类型
         """
+        if not isinstance(platform, (str, SpecialTypes)):
+            raise TypeError("platform 必须是字符串或是 SpecialTypes 枚举")
         streams = []
         try:
             for _, stream in get_chat_manager().streams.items():
@@ -60,6 +65,8 @@ class ChatManager:
         Returns:
             List[ChatStream]: 群聊聊天流列表
         """
+        if not isinstance(platform, (str, SpecialTypes)):
+            raise TypeError("platform 必须是字符串或是 SpecialTypes 枚举")
         streams = []
         try:
             for _, stream in get_chat_manager().streams.items():
@@ -79,7 +86,12 @@ class ChatManager:
 
         Returns:
             List[ChatStream]: 私聊聊天流列表
+
+        Raises:
+            TypeError: 如果 platform 不是字符串或 SpecialTypes 枚举类型
         """
+        if not isinstance(platform, (str, SpecialTypes)):
+            raise TypeError("platform 必须是字符串或是 SpecialTypes 枚举")
         streams = []
         try:
             for _, stream in get_chat_manager().streams.items():
@@ -102,7 +114,17 @@ class ChatManager:
 
         Returns:
             Optional[ChatStream]: 聊天流对象，如果未找到返回None
+
+        Raises:
+            ValueError: 如果 group_id 为空字符串
+            TypeError: 如果 group_id 不是字符串类型或 platform 不是字符串或 SpecialTypes
         """
+        if not isinstance(group_id, str):
+            raise TypeError("group_id 必须是字符串类型")
+        if not isinstance(platform, (str, SpecialTypes)):
+            raise TypeError("platform 必须是字符串或是 SpecialTypes 枚举")
+        if not group_id:
+            raise ValueError("group_id 不能为空")
         try:
             for _, stream in get_chat_manager().streams.items():
                 if (
@@ -129,7 +151,17 @@ class ChatManager:
 
         Returns:
             Optional[ChatStream]: 聊天流对象，如果未找到返回None
+
+        Raises:
+            ValueError: 如果 user_id 为空字符串
+            TypeError: 如果 user_id 不是字符串类型或 platform 不是字符串或 SpecialTypes
         """
+        if not isinstance(user_id, str):
+            raise TypeError("user_id 必须是字符串类型")
+        if not isinstance(platform, (str, SpecialTypes)):
+            raise TypeError("platform 必须是字符串或是 SpecialTypes 枚举")
+        if not user_id:
+            raise ValueError("user_id 不能为空")
         try:
             for _, stream in get_chat_manager().streams.items():
                 if (
@@ -153,9 +185,15 @@ class ChatManager:
 
         Returns:
             str: 聊天类型 ("group", "private", "unknown")
+
+        Raises:
+            TypeError: 如果 chat_stream 不是 ChatStream 类型
+            ValueError: 如果 chat_stream 为空
         """
+        if not isinstance(chat_stream, ChatStream):
+            raise TypeError("chat_stream 必须是 ChatStream 类型")
         if not chat_stream:
-            raise ValueError("chat_stream cannot be None")
+            raise ValueError("chat_stream 不能为 None")
 
         if hasattr(chat_stream, "group_info"):
             return "group" if chat_stream.group_info else "private"
@@ -170,9 +208,15 @@ class ChatManager:
 
         Returns:
             Dict[str, Any]: 聊天流信息字典
+
+        Raises:
+            TypeError: 如果 chat_stream 不是 ChatStream 类型
+            ValueError: 如果 chat_stream 为空
         """
         if not chat_stream:
-            return {}
+            raise ValueError("chat_stream 不能为 None")
+        if not isinstance(chat_stream, ChatStream):
+            raise TypeError("chat_stream 必须是 ChatStream 类型")
 
         try:
             info: Dict[str, Any] = {

src/plugin_system/apis/emoji_api.py

@@ -8,6 +8,8 @@
     count = emoji_api.get_count()
 """
 
+import random
+
 from typing import Optional, Tuple, List
 from src.common.logger import get_logger
 from src.chat.emoji_system.emoji_manager import get_emoji_manager
@@ -29,7 +31,15 @@ async def get_by_description(description: str) -> Optional[Tuple[str, str, str]]
 
     Returns:
         Optional[Tuple[str, str, str]]: (base64编码, 表情包描述, 匹配的情感标签) 或 None
+
+    Raises:
+        ValueError: 如果描述为空字符串
+        TypeError: 如果描述不是字符串类型
     """
+    if not description:
+        raise ValueError("描述不能为空")
+    if not isinstance(description, str):
+        raise TypeError("描述必须是字符串类型")
     try:
         logger.debug(f"[EmojiAPI] 根据描述获取表情包: {description}")
 
@@ -55,7 +65,7 @@ async def get_by_description(description: str) -> Optional[Tuple[str, str, str]]
         return None
 
 
-async def get_random(count: int = 1) -> Optional[List[Tuple[str, str, str]]]:
+async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str, str]]]:
     """随机获取指定数量的表情包
 
     Args:
@@ -63,8 +73,17 @@ async def get_random(count: int = 1) -> Optional[List[Tuple[str, str, str]]]:
 
     Returns:
         Optional[List[Tuple[str, str, str]]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表，如果失败则为None
+
+    Raises:
+        TypeError: 如果count不是整数类型
+        ValueError: 如果count为负数
     """
-    if count <= 0:
+    if not isinstance(count, int):
+        raise TypeError("count 必须是整数类型")
+    if count < 0:
+        raise ValueError("count 不能为负数")
+    if count == 0:
+        logger.warning("[EmojiAPI] count 为0，返回空列表")
         return []
 
     try:
@@ -90,8 +109,6 @@ async def get_random(count: int = 1) -> Optional[List[Tuple[str, str, str]]]:
             count = len(valid_emojis)
 
         # 随机选择
-        import random
-
         selected_emojis = random.sample(valid_emojis, count)
 
         results = []
@@ -128,7 +145,15 @@ async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
 
     Returns:
         Optional[Tuple[str, str, str]]: (base64编码, 表情包描述, 匹配的情感标签) 或 None
+
+    Raises:
+        ValueError: 如果情感标签为空字符串
+        TypeError: 如果情感标签不是字符串类型
     """
+    if not emotion:
+        raise ValueError("情感标签不能为空")
+    if not isinstance(emotion, str):
+        raise TypeError("情感标签必须是字符串类型")
     try:
         logger.info(f"[EmojiAPI] 根据情感获取表情包: {emotion}")
 
@@ -146,8 +171,6 @@ async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
             return None
 
         # 随机选择匹配的表情包
-        import random
-
         selected_emoji = random.choice(matching_emojis)
         emoji_base64 = image_path_to_base64(selected_emoji.full_path)
 
@@ -185,11 +208,11 @@ def get_count() -> int:
         return 0
 
 
-def get_info() -> dict:
+def get_info():
     """获取表情包系统信息
 
     Returns:
-        dict: 包含表情包数量、最大数量等信息
+        dict: 包含表情包数量、最大数量、可用数量信息
     """
     try:
         emoji_manager = get_emoji_manager()
@@ -203,7 +226,7 @@ def get_info() -> dict:
         return {"current_count": 0, "max_count": 0, "available_emojis": 0}
 
 
-def get_emotions() -> list:
+def get_emotions() -> List[str]:
     """获取所有可用的情感标签
 
     Returns:
@@ -223,7 +246,7 @@ def get_emotions() -> list:
         return []
 
 
-def get_descriptions() -> list:
+def get_descriptions() -> List[str]:
     """获取所有表情包描述
 
     Returns:

src/plugin_system/apis/generator_api.py

@@ -5,11 +5,12 @@
 使用方式：
     from src.plugin_system.apis import generator_api
     replyer = generator_api.get_replyer(chat_stream)
-    success, reply_set = await generator_api.generate_reply(chat_stream, action_data, reasoning)
+    success, reply_set, _ = await generator_api.generate_reply(chat_stream, action_data, reasoning)
 """
 
 import traceback
 from typing import Tuple, Any, Dict, List, Optional
+from rich.traceback import install
 from src.common.logger import get_logger
 from src.chat.replyer.default_generator import DefaultReplyer
 from src.chat.message_receive.chat_stream import ChatStream
@@ -17,6 +18,8 @@ from src.chat.utils.utils import process_llm_response
 from src.chat.replyer.replyer_manager import replyer_manager
 from src.plugin_system.base.component_types import ActionInfo
 
+install(extra_lines=3)
+
 logger = get_logger("generator_api")
 
 
@@ -44,7 +47,12 @@ def get_replyer(
 
     Returns:
         Optional[DefaultReplyer]: 回复器对象，如果获取失败则返回None
+
+    Raises:
+        ValueError: chat_stream 和 chat_id 均为空
     """
+    if not chat_id and not chat_stream:
+        raise ValueError("chat_stream 和 chat_id 不可均为空")
     try:
         logger.debug(f"[GeneratorAPI] 正在获取回复器，chat_id: {chat_id}, chat_stream: {'有' if chat_stream else '无'}")
         return replyer_manager.get_replyer(

src/plugin_system/apis/llm_api.py

@@ -14,7 +14,6 @@ from src.config.config import global_config
 
 logger = get_logger("llm_api")
 
-
 # =============================================================================
 # LLM模型API函数
 # =============================================================================
@@ -31,8 +30,21 @@ def get_available_models() -> Dict[str, Any]:
             logger.error("[LLMAPI] 无法获取模型列表：全局配置中未找到 model 配置")
             return {}
 
+        # 自动获取所有属性并转换为字典形式
+        rets = {}
         models = global_config.model
-        return models
+        attrs = dir(models)
+        for attr in attrs:
+            if not attr.startswith("__"):
+                try:
+                    value = getattr(models, attr)
+                    if not callable(value):  # 排除方法
+                        rets[attr] = value
+                except Exception as e:
+                    logger.debug(f"[LLMAPI] 获取属性 {attr} 失败: {e}")
+                    continue
+        return rets
+
     except Exception as e:
         logger.error(f"[LLMAPI] 获取可用模型失败: {e}")
         return {}

src/plugin_system/apis/send_api.py

@@ -114,7 +114,11 @@ async def _send_to_target(
 
         # 发送消息
         sent_msg = await heart_fc_sender.send_message(
-            bot_message, typing=typing, set_reply=(anchor_message is not None), storage_message=storage_message, show_log=show_log
+            bot_message,
+            typing=typing,
+            set_reply=(anchor_message is not None),
+            storage_message=storage_message,
+            show_log=show_log,
         )
 
         if sent_msg:
@@ -362,7 +366,9 @@ async def custom_to_stream(
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target(message_type, content, stream_id, display_message, typing, reply_to, storage_message, show_log)
+    return await _send_to_target(
+        message_type, content, stream_id, display_message, typing, reply_to, storage_message, show_log
+    )
 
 
 async def text_to_group(