mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-31 19:33:26 +08:00
794 lines
26 KiB
Python
794 lines
26 KiB
Python
import importlib
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import importlib.util
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import os
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from pathlib import Path
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from typing import Dict, Any
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import json
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import logging
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logger = logging.getLogger()
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class ConfigBasic:
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log_verbose: bool
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CHATCHAT_ROOT: str
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DATA_PATH: str
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IMG_DIR: str
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NLTK_DATA_PATH: str
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LOG_FORMAT: str
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LOG_PATH: str
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MEDIA_PATH: str
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BASE_TEMP_DIR: str
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class ConfigBasicFactory:
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"""Basic config for ChatChat """
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def __init__(self):
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# 日志格式
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self.LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s"
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logging.basicConfig(format=self.LOG_FORMAT)
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self.LOG_VERBOSE = False
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self.CHATCHAT_ROOT = str(Path(__file__).absolute().parent.parent)
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# 用户数据根目录
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self.DATA_PATH = os.path.join(self.CHATCHAT_ROOT, "data")
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self._DATA_PATH = os.path.join(self.CHATCHAT_ROOT, "data")
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if not os.path.exists(self._DATA_PATH):
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os.mkdir(self.DATA_PATH)
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self._init_data_dir()
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# 项目相关图片
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self.IMG_DIR = os.path.join(self.CHATCHAT_ROOT, "img")
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if not os.path.exists(self.IMG_DIR):
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os.mkdir(self.IMG_DIR)
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def log_verbose(self, verbose: bool):
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self.LOG_VERBOSE = verbose
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def chatchat_root(self, root: str):
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self.CHATCHAT_ROOT = root
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def data_path(self, path: str):
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self.DATA_PATH = path
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if not os.path.exists(self.DATA_PATH):
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os.mkdir(self.DATA_PATH)
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# 复制_DATA_PATH数据到DATA_PATH
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os.system(f"cp -r {self._DATA_PATH} {self.DATA_PATH}")
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self._init_data_dir()
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def log_format(self, log_format: str):
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self.LOG_FORMAT = log_format
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logging.basicConfig(format=self.LOG_FORMAT)
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def _init_data_dir(self):
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logger.info(f"Init data dir: {self.DATA_PATH}")
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# nltk 模型存储路径
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self.NLTK_DATA_PATH = os.path.join(self.DATA_PATH, "nltk_data")
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import nltk
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nltk.data.path = [self.NLTK_DATA_PATH] + nltk.data.path
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# 日志存储路径
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self.LOG_PATH = os.path.join(self.DATA_PATH, "logs")
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if not os.path.exists(self.LOG_PATH):
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os.mkdir(self.LOG_PATH)
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# 模型生成内容（图片、视频、音频等）保存位置
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self.MEDIA_PATH = os.path.join(self.DATA_PATH, "media")
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if not os.path.exists(self.MEDIA_PATH):
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os.mkdir(self.MEDIA_PATH)
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os.mkdir(os.path.join(self.MEDIA_PATH, "image"))
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os.mkdir(os.path.join(self.MEDIA_PATH, "audio"))
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os.mkdir(os.path.join(self.MEDIA_PATH, "video"))
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# 临时文件目录，主要用于文件对话
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self.BASE_TEMP_DIR = os.path.join(self.DATA_PATH, "temp")
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if not os.path.exists(self.BASE_TEMP_DIR):
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os.mkdir(self.BASE_TEMP_DIR)
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logger.info(f"Init data dir: {self.DATA_PATH} success.")
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def get_config(self) -> ConfigBasic:
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config = ConfigBasic()
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config.log_verbose = self.LOG_VERBOSE
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config.CHATCHAT_ROOT = self.CHATCHAT_ROOT
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config.DATA_PATH = self.DATA_PATH
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config.IMG_DIR = self.IMG_DIR
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config.NLTK_DATA_PATH = self.NLTK_DATA_PATH
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config.LOG_FORMAT = self.LOG_FORMAT
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config.LOG_PATH = self.LOG_PATH
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config.MEDIA_PATH = self.MEDIA_PATH
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config.BASE_TEMP_DIR = self.BASE_TEMP_DIR
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return config
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class ConfigWorkSpace:
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"""
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工作空间的配置预设，提供ConfigBasic建造方法产生实例。
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该类的实例对象用于存储工作空间的配置信息，如工作空间的路径等
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工作空间的配置信息存储在用户的家目录下的.config/chatchat/workspace/workspace_config.json文件中。
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注意：不存在则读取默认
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"""
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_config_factory: ConfigBasicFactory = ConfigBasicFactory()
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def __init__(self):
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self.workspace = os.path.join(os.path.expanduser("~"), ".config", "chatchat/workspace")
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if not os.path.exists(self.workspace):
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os.makedirs(self.workspace, exist_ok=True)
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self.workspace_config = os.path.join(self.workspace, "workspace_config.json")
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# 初始化工作空间配置，转换成json格式，实现ConfigBasic的实例化
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with open(self.workspace_config, "w") as f:
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config_json = json.loads(f.read())
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if config_json:
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_config_factory = ConfigBasicFactory()
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if config_json.get("log_verbose"):
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_config_factory.log_verbose(config_json.get("log_verbose"))
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if config_json.get("CHATCHAT_ROOT"):
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_config_factory.chatchat_root(config_json.get("CHATCHAT_ROOT"))
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if config_json.get("DATA_PATH"):
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_config_factory.data_path(config_json.get("DATA_PATH"))
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if config_json.get("LOG_FORMAT"):
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_config_factory.log_format(config_json.get("LOG_FORMAT"))
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self._config_factory = _config_factory
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def get_config(self) -> ConfigBasic:
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return self._config_factory.get_config()
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def set_log_verbose(self, verbose: bool):
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self._config_factory.log_verbose(verbose)
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self._store_config()
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def set_chatchat_root(self, root: str):
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self._config_factory.chatchat_root(root)
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self._store_config()
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def set_data_path(self, path: str):
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self._config_factory.data_path(path)
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self._store_config()
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def set_log_format(self, log_format: str):
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self._config_factory.log_format(log_format)
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self._store_config()
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def _store_config(self):
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with open(self.workspace_config, "w") as f:
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config = self._config_factory.get_config()
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config_json = {
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"log_verbose": config.log_verbose,
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"CHATCHAT_ROOT": config.CHATCHAT_ROOT,
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"DATA_PATH": config.DATA_PATH,
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"LOG_FORMAT": config.LOG_FORMAT
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}
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f.write(json.dumps(config_json, indent=4, ensure_ascii=False))
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def _load_mod(mod, attr):
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attr_cfg = None
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for name, obj in vars(mod).items():
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if name == attr:
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attr_cfg = obj
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break
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if attr_cfg is None:
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logger.warning(
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f"Missing attr_cfg:{attr} in {mod}, Skip."
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)
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return attr_cfg
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return attr_cfg
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def _import_config_mod_load(import_config_mod: str) -> Dict:
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# 加载用户空间的配置
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user_config_path = os.path.join(os.path.expanduser("~"), ".config", "chatchat/configs")
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user_import = True # 默认加载用户配置
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if os.path.exists(user_config_path):
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try:
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file_names = os.listdir(user_config_path)
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if import_config_mod + ".py" not in file_names:
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logger.warning(
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f"Missing {file_names}.py file in {user_config_path}, Skip."
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)
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user_import = False
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if user_import:
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# Dynamic loading {config}.py file
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py_path = os.path.join(user_config_path, import_config_mod + ".py")
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spec = importlib.util.spec_from_file_location(
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f"*",
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py_path
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)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return {
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"user_import": user_import,
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"user_config_path": user_config_path,
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"load_mod": _load_mod,
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"module": module,
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}
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except ImportError as e:
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logger.error(
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f"Failed to load user config from {user_config_path}, Skip.", e
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)
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pass
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else:
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user_import = False
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if user_import:
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logger.error(
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f"Failed to load user config from {user_config_path}, Skip."
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)
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raise RuntimeError(f"Failed to load user config from {user_config_path}")
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# 当前文件路径
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py_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), import_config_mod + ".py")
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spec = importlib.util.spec_from_file_location(f"*",
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py_path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return {
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"user_import": user_import,
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"user_config_path": user_config_path,
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"load_mod": _load_mod,
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"module": module,
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}
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CONFIG_IMPORTS = {
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"_basic_config.py": _import_config_mod_load("_basic_config"),
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"_kb_config.py": _import_config_mod_load("_kb_config"),
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"_model_config.py": _import_config_mod_load("_model_config"),
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"_prompt_config.py": _import_config_mod_load("_prompt_config"),
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"_server_config.py": _import_config_mod_load("_server_config"),
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}
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def _import_log_verbose() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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log_verbose = load_mod(basic_config_load.get("module"), "log_verbose")
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return log_verbose
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def _import_chatchat_root() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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CHATCHAT_ROOT = load_mod(basic_config_load.get("module"), "CHATCHAT_ROOT")
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return CHATCHAT_ROOT
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def _import_data_path() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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DATA_PATH = load_mod(basic_config_load.get("module"), "DATA_PATH")
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return DATA_PATH
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def _import_img_dir() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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IMG_DIR = load_mod(basic_config_load.get("module"), "IMG_DIR")
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return IMG_DIR
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def _import_nltk_data_path() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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NLTK_DATA_PATH = load_mod(basic_config_load.get("module"), "NLTK_DATA_PATH")
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return NLTK_DATA_PATH
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def _import_log_format() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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LOG_FORMAT = load_mod(basic_config_load.get("module"), "LOG_FORMAT")
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return LOG_FORMAT
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def _import_log_path() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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LOG_PATH = load_mod(basic_config_load.get("module"), "LOG_PATH")
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return LOG_PATH
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def _import_media_path() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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MEDIA_PATH = load_mod(basic_config_load.get("module"), "MEDIA_PATH")
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return MEDIA_PATH
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def _import_base_temp_dir() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_basic_config.py")
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load_mod = basic_config_load.get("load_mod")
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BASE_TEMP_DIR = load_mod(basic_config_load.get("module"), "BASE_TEMP_DIR")
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return BASE_TEMP_DIR
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def _import_default_knowledge_base() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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DEFAULT_KNOWLEDGE_BASE = load_mod(kb_config_load.get("module"), "DEFAULT_KNOWLEDGE_BASE")
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return DEFAULT_KNOWLEDGE_BASE
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def _import_default_vs_type() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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DEFAULT_VS_TYPE = load_mod(kb_config_load.get("module"), "DEFAULT_VS_TYPE")
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return DEFAULT_VS_TYPE
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def _import_cached_vs_num() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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CACHED_VS_NUM = load_mod(kb_config_load.get("module"), "CACHED_VS_NUM")
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return CACHED_VS_NUM
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def _import_cached_memo_vs_num() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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CACHED_MEMO_VS_NUM = load_mod(kb_config_load.get("module"), "CACHED_MEMO_VS_NUM")
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return CACHED_MEMO_VS_NUM
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def _import_chunk_size() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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CHUNK_SIZE = load_mod(kb_config_load.get("module"), "CHUNK_SIZE")
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return CHUNK_SIZE
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def _import_overlap_size() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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OVERLAP_SIZE = load_mod(kb_config_load.get("module"), "OVERLAP_SIZE")
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return OVERLAP_SIZE
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def _import_vector_search_top_k() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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VECTOR_SEARCH_TOP_K = load_mod(kb_config_load.get("module"), "VECTOR_SEARCH_TOP_K")
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return VECTOR_SEARCH_TOP_K
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def _import_score_threshold() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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SCORE_THRESHOLD = load_mod(kb_config_load.get("module"), "SCORE_THRESHOLD")
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return SCORE_THRESHOLD
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def _import_default_search_engine() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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DEFAULT_SEARCH_ENGINE = load_mod(kb_config_load.get("module"), "DEFAULT_SEARCH_ENGINE")
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return DEFAULT_SEARCH_ENGINE
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def _import_search_engine_top_k() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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SEARCH_ENGINE_TOP_K = load_mod(kb_config_load.get("module"), "SEARCH_ENGINE_TOP_K")
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return SEARCH_ENGINE_TOP_K
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def _import_zh_title_enhance() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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ZH_TITLE_ENHANCE = load_mod(kb_config_load.get("module"), "ZH_TITLE_ENHANCE")
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return ZH_TITLE_ENHANCE
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def _import_pdf_ocr_threshold() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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PDF_OCR_THRESHOLD = load_mod(kb_config_load.get("module"), "PDF_OCR_THRESHOLD")
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return PDF_OCR_THRESHOLD
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def _import_kb_info() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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KB_INFO = load_mod(kb_config_load.get("module"), "KB_INFO")
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return KB_INFO
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def _import_kb_root_path() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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KB_ROOT_PATH = load_mod(kb_config_load.get("module"), "KB_ROOT_PATH")
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return KB_ROOT_PATH
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def _import_db_root_path() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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DB_ROOT_PATH = load_mod(kb_config_load.get("module"), "DB_ROOT_PATH")
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return DB_ROOT_PATH
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def _import_sqlalchemy_database_uri() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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SQLALCHEMY_DATABASE_URI = load_mod(kb_config_load.get("module"), "SQLALCHEMY_DATABASE_URI")
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return SQLALCHEMY_DATABASE_URI
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def _import_kbs_config() -> Any:
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kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
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load_mod = kb_config_load.get("load_mod")
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kbs_config = load_mod(kb_config_load.get("module"), "kbs_config")
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return kbs_config
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def _import_text_splitter_dict() -> Any:
|
||
kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
|
||
load_mod = kb_config_load.get("load_mod")
|
||
text_splitter_dict = load_mod(kb_config_load.get("module"), "text_splitter_dict")
|
||
|
||
return text_splitter_dict
|
||
|
||
|
||
def _import_text_splitter_name() -> Any:
|
||
kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
|
||
load_mod = kb_config_load.get("load_mod")
|
||
TEXT_SPLITTER_NAME = load_mod(kb_config_load.get("module"), "TEXT_SPLITTER_NAME")
|
||
|
||
return TEXT_SPLITTER_NAME
|
||
|
||
|
||
def _import_embedding_keyword_file() -> Any:
|
||
kb_config_load = CONFIG_IMPORTS.get("_kb_config.py")
|
||
load_mod = kb_config_load.get("load_mod")
|
||
EMBEDDING_KEYWORD_FILE = load_mod(kb_config_load.get("module"), "EMBEDDING_KEYWORD_FILE")
|
||
|
||
return EMBEDDING_KEYWORD_FILE
|
||
|
||
|
||
def _import_default_llm_model() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
DEFAULT_LLM_MODEL = load_mod(model_config_load.get("module"), "DEFAULT_LLM_MODEL")
|
||
|
||
return DEFAULT_LLM_MODEL
|
||
|
||
|
||
def _import_default_embedding_model() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
DEFAULT_EMBEDDING_MODEL = load_mod(model_config_load.get("module"), "DEFAULT_EMBEDDING_MODEL")
|
||
|
||
return DEFAULT_EMBEDDING_MODEL
|
||
|
||
|
||
def _import_agent_model() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
Agent_MODEL = load_mod(model_config_load.get("module"), "Agent_MODEL")
|
||
|
||
return Agent_MODEL
|
||
|
||
|
||
def _import_history_len() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
HISTORY_LEN = load_mod(model_config_load.get("module"), "HISTORY_LEN")
|
||
|
||
return HISTORY_LEN
|
||
|
||
|
||
def _import_max_tokens() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
MAX_TOKENS = load_mod(model_config_load.get("module"), "MAX_TOKENS")
|
||
|
||
return MAX_TOKENS
|
||
|
||
|
||
def _import_temperature() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
TEMPERATURE = load_mod(model_config_load.get("module"), "TEMPERATURE")
|
||
|
||
return TEMPERATURE
|
||
|
||
|
||
def _import_support_agent_models() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
SUPPORT_AGENT_MODELS = load_mod(model_config_load.get("module"), "SUPPORT_AGENT_MODELS")
|
||
|
||
return SUPPORT_AGENT_MODELS
|
||
|
||
|
||
def _import_llm_model_config() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
LLM_MODEL_CONFIG = load_mod(model_config_load.get("module"), "LLM_MODEL_CONFIG")
|
||
|
||
return LLM_MODEL_CONFIG
|
||
|
||
|
||
def _import_model_platforms() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
MODEL_PLATFORMS = load_mod(model_config_load.get("module"), "MODEL_PLATFORMS")
|
||
|
||
return MODEL_PLATFORMS
|
||
|
||
|
||
def _import_model_providers_cfg_path() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
MODEL_PROVIDERS_CFG_PATH_CONFIG = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_PATH_CONFIG")
|
||
|
||
return MODEL_PROVIDERS_CFG_PATH_CONFIG
|
||
|
||
|
||
def _import_model_providers_cfg_host() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
MODEL_PROVIDERS_CFG_HOST = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_HOST")
|
||
|
||
return MODEL_PROVIDERS_CFG_HOST
|
||
|
||
|
||
def _import_model_providers_cfg_port() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
MODEL_PROVIDERS_CFG_PORT = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_PORT")
|
||
|
||
return MODEL_PROVIDERS_CFG_PORT
|
||
|
||
|
||
def _import_tool_config() -> Any:
|
||
model_config_load = CONFIG_IMPORTS.get("_model_config.py")
|
||
load_mod = model_config_load.get("load_mod")
|
||
TOOL_CONFIG = load_mod(model_config_load.get("module"), "TOOL_CONFIG")
|
||
|
||
return TOOL_CONFIG
|
||
|
||
|
||
def _import_prompt_templates() -> Any:
|
||
prompt_config_load = CONFIG_IMPORTS.get("_prompt_config.py")
|
||
load_mod = prompt_config_load.get("load_mod")
|
||
PROMPT_TEMPLATES = load_mod(prompt_config_load.get("module"), "PROMPT_TEMPLATES")
|
||
|
||
return PROMPT_TEMPLATES
|
||
|
||
|
||
def _import_httpx_default_timeout() -> Any:
|
||
server_config_load = CONFIG_IMPORTS.get("_server_config.py")
|
||
load_mod = server_config_load.get("load_mod")
|
||
HTTPX_DEFAULT_TIMEOUT = load_mod(server_config_load.get("module"), "HTTPX_DEFAULT_TIMEOUT")
|
||
|
||
return HTTPX_DEFAULT_TIMEOUT
|
||
|
||
|
||
def _import_open_cross_domain() -> Any:
|
||
server_config_load = CONFIG_IMPORTS.get("_server_config.py")
|
||
load_mod = server_config_load.get("load_mod")
|
||
OPEN_CROSS_DOMAIN = load_mod(server_config_load.get("module"), "OPEN_CROSS_DOMAIN")
|
||
|
||
return OPEN_CROSS_DOMAIN
|
||
|
||
|
||
def _import_default_bind_host() -> Any:
|
||
server_config_load = CONFIG_IMPORTS.get("_server_config.py")
|
||
load_mod = server_config_load.get("load_mod")
|
||
DEFAULT_BIND_HOST = load_mod(server_config_load.get("module"), "DEFAULT_BIND_HOST")
|
||
|
||
return DEFAULT_BIND_HOST
|
||
|
||
|
||
def _import_webui_server() -> Any:
|
||
server_config_load = CONFIG_IMPORTS.get("_server_config.py")
|
||
load_mod = server_config_load.get("load_mod")
|
||
WEBUI_SERVER = load_mod(server_config_load.get("module"), "WEBUI_SERVER")
|
||
|
||
return WEBUI_SERVER
|
||
|
||
|
||
def _import_api_server() -> Any:
|
||
server_config_load = CONFIG_IMPORTS.get("_server_config.py")
|
||
load_mod = server_config_load.get("load_mod")
|
||
API_SERVER = load_mod(server_config_load.get("module"), "API_SERVER")
|
||
|
||
return API_SERVER
|
||
|
||
|
||
def __getattr__(name: str) -> Any:
|
||
if name == "log_verbose":
|
||
return _import_log_verbose()
|
||
elif name == "CHATCHAT_ROOT":
|
||
return _import_chatchat_root()
|
||
elif name == "DATA_PATH":
|
||
return _import_data_path()
|
||
elif name == "IMG_DIR":
|
||
return _import_img_dir()
|
||
elif name == "NLTK_DATA_PATH":
|
||
return _import_nltk_data_path()
|
||
elif name == "LOG_FORMAT":
|
||
return _import_log_format()
|
||
elif name == "LOG_PATH":
|
||
return _import_log_path()
|
||
elif name == "MEDIA_PATH":
|
||
return _import_media_path()
|
||
elif name == "BASE_TEMP_DIR":
|
||
return _import_base_temp_dir()
|
||
elif name == "DEFAULT_KNOWLEDGE_BASE":
|
||
return _import_default_knowledge_base()
|
||
elif name == "DEFAULT_VS_TYPE":
|
||
return _import_default_vs_type()
|
||
elif name == "CACHED_VS_NUM":
|
||
return _import_cached_vs_num()
|
||
elif name == "CACHED_MEMO_VS_NUM":
|
||
return _import_cached_memo_vs_num()
|
||
elif name == "CHUNK_SIZE":
|
||
return _import_chunk_size()
|
||
elif name == "OVERLAP_SIZE":
|
||
return _import_overlap_size()
|
||
elif name == "VECTOR_SEARCH_TOP_K":
|
||
return _import_vector_search_top_k()
|
||
elif name == "SCORE_THRESHOLD":
|
||
return _import_score_threshold()
|
||
elif name == "DEFAULT_SEARCH_ENGINE":
|
||
return _import_default_search_engine()
|
||
elif name == "SEARCH_ENGINE_TOP_K":
|
||
return _import_search_engine_top_k()
|
||
elif name == "ZH_TITLE_ENHANCE":
|
||
return _import_zh_title_enhance()
|
||
elif name == "PDF_OCR_THRESHOLD":
|
||
return _import_pdf_ocr_threshold()
|
||
elif name == "KB_INFO":
|
||
return _import_kb_info()
|
||
elif name == "KB_ROOT_PATH":
|
||
return _import_kb_root_path()
|
||
elif name == "DB_ROOT_PATH":
|
||
return _import_db_root_path()
|
||
elif name == "SQLALCHEMY_DATABASE_URI":
|
||
return _import_sqlalchemy_database_uri()
|
||
elif name == "kbs_config":
|
||
return _import_kbs_config()
|
||
elif name == "text_splitter_dict":
|
||
return _import_text_splitter_dict()
|
||
elif name == "TEXT_SPLITTER_NAME":
|
||
return _import_text_splitter_name()
|
||
elif name == "EMBEDDING_KEYWORD_FILE":
|
||
return _import_embedding_keyword_file()
|
||
elif name == "DEFAULT_LLM_MODEL":
|
||
return _import_default_llm_model()
|
||
elif name == "DEFAULT_EMBEDDING_MODEL":
|
||
return _import_default_embedding_model()
|
||
elif name == "Agent_MODEL":
|
||
return _import_agent_model()
|
||
elif name == "HISTORY_LEN":
|
||
return _import_history_len()
|
||
elif name == "MAX_TOKENS":
|
||
return _import_max_tokens()
|
||
elif name == "TEMPERATURE":
|
||
return _import_temperature()
|
||
elif name == "SUPPORT_AGENT_MODELS":
|
||
return _import_support_agent_models()
|
||
elif name == "LLM_MODEL_CONFIG":
|
||
return _import_llm_model_config()
|
||
elif name == "MODEL_PLATFORMS":
|
||
return _import_model_platforms()
|
||
elif name == "MODEL_PROVIDERS_CFG_PATH_CONFIG":
|
||
return _import_model_providers_cfg_path()
|
||
elif name == "MODEL_PROVIDERS_CFG_HOST":
|
||
return _import_model_providers_cfg_host()
|
||
elif name == "MODEL_PROVIDERS_CFG_PORT":
|
||
return _import_model_providers_cfg_port()
|
||
elif name == "TOOL_CONFIG":
|
||
return _import_tool_config()
|
||
elif name == "PROMPT_TEMPLATES":
|
||
return _import_prompt_templates()
|
||
elif name == "HTTPX_DEFAULT_TIMEOUT":
|
||
return _import_httpx_default_timeout()
|
||
elif name == "OPEN_CROSS_DOMAIN":
|
||
return _import_default_bind_host()
|
||
elif name == "WEBUI_SERVER":
|
||
return _import_webui_server()
|
||
elif name == "API_SERVER":
|
||
return _import_api_server()
|
||
|
||
|
||
VERSION = "v0.3.0-preview"
|
||
|
||
__all__ = [
|
||
"VERSION",
|
||
"log_verbose",
|
||
"CHATCHAT_ROOT",
|
||
"DATA_PATH",
|
||
"IMG_DIR",
|
||
"NLTK_DATA_PATH",
|
||
"LOG_FORMAT",
|
||
"LOG_PATH",
|
||
"MEDIA_PATH",
|
||
"BASE_TEMP_DIR",
|
||
"DEFAULT_KNOWLEDGE_BASE",
|
||
"DEFAULT_VS_TYPE",
|
||
"CACHED_VS_NUM",
|
||
"CACHED_MEMO_VS_NUM",
|
||
"CHUNK_SIZE",
|
||
"OVERLAP_SIZE",
|
||
"VECTOR_SEARCH_TOP_K",
|
||
"SCORE_THRESHOLD",
|
||
"DEFAULT_SEARCH_ENGINE",
|
||
"SEARCH_ENGINE_TOP_K",
|
||
"ZH_TITLE_ENHANCE",
|
||
"PDF_OCR_THRESHOLD",
|
||
"KB_INFO",
|
||
"KB_ROOT_PATH",
|
||
"DB_ROOT_PATH",
|
||
"SQLALCHEMY_DATABASE_URI",
|
||
"kbs_config",
|
||
"text_splitter_dict",
|
||
"TEXT_SPLITTER_NAME",
|
||
"EMBEDDING_KEYWORD_FILE",
|
||
"DEFAULT_LLM_MODEL",
|
||
"DEFAULT_EMBEDDING_MODEL",
|
||
"Agent_MODEL",
|
||
"HISTORY_LEN",
|
||
"MAX_TOKENS",
|
||
"TEMPERATURE",
|
||
"SUPPORT_AGENT_MODELS",
|
||
"LLM_MODEL_CONFIG",
|
||
"MODEL_PLATFORMS",
|
||
"MODEL_PROVIDERS_CFG_PATH_CONFIG",
|
||
"MODEL_PROVIDERS_CFG_HOST",
|
||
"MODEL_PROVIDERS_CFG_PORT",
|
||
"TOOL_CONFIG",
|
||
"PROMPT_TEMPLATES",
|
||
"HTTPX_DEFAULT_TIMEOUT",
|
||
"OPEN_CROSS_DOMAIN",
|
||
"WEBUI_SERVER",
|
||
"API_SERVER",
|
||
|
||
|
||
"ConfigBasic",
|
||
"ConfigBasicFactory",
|
||
"ConfigWorkSpace",
|
||
|
||
]
|