使用click增加配置中心子命令 (#4164)

使用click增加配置中心子命令
新增 ConfigModelWorkSpace

libs/chatchat-server/chatchat/config_work_space.py

@@ -1,52 +1,88 @@
-from chatchat.configs import config_basic_workspace as workspace
+from chatchat.configs import (
+    config_basic_workspace,
+    config_model_workspace,
+)
+
+# We cannot lazy-load click here because its used via decorators.
+import click
 
 
+@click.group(help="指令` chatchat-config` 工作空间配置")
 def main():
-    import argparse
+    pass
 
-    parser = argparse.ArgumentParser(description="指令` chatchat-config` 工作空间配置")
-    # 只能选择true或false
-    parser.add_argument(
-        "-v",
-        "--verbose",
-        choices=["true", "false"],
-        help="是否开启详细日志"
-    )
-    parser.add_argument(
-        "-d",
-        "--data",
-        help="数据存放路径"
-    )
-    parser.add_argument(
-        "-f",
-        "--format",
-        help="日志格式"
-    )
-    parser.add_argument(
-        "--clear",
-        action="store_true",
-        help="清除配置"
-    )
-    parser.add_argument(
-        "--show",
-        action="store_true",
-        help="显示配置"
-    )
-    args = parser.parse_args()
 
-    if args.verbose:
-        if args.verbose.lower() == "true":
-            workspace.set_log_verbose(True)
+@main.command("basic", help="基础配置")
+@click.option("--verbose", type=click.Choice(["true", "false"]), help="是否开启详细日志")
+@click.option("--data", help="数据存放路径")
+@click.option("--format", help="日志格式")
+@click.option("--clear", is_flag=True, help="清除配置")
+@click.option("--show", is_flag=True, help="显示配置")
+def basic(**kwargs):
+
+    if kwargs["verbose"]:
+        if kwargs["verbose"].lower() == "true":
+            config_basic_workspace.set_log_verbose(True)
         else:
-            workspace.set_log_verbose(False)
-    if args.data:
-        workspace.set_data_path(args.data)
-    if args.format:
-        workspace.set_log_format(args.format)
-    if args.clear:
-        workspace.clear()
-    if args.show:
-        print(workspace.get_config())
+            config_basic_workspace.set_log_verbose(False)
+    if kwargs["data"]:
+        config_basic_workspace.set_data_path(kwargs["data"])
+    if kwargs["format"]:
+        config_basic_workspace.set_log_format(kwargs["format"])
+    if kwargs["clear"]:
+        config_basic_workspace.clear()
+    if kwargs["show"]:
+        print(config_basic_workspace.get_config())
+
+
+@main.command("model", help="模型配置")
+@click.option("--default_llm_model", help="默认llm模型")
+@click.option("--default_embedding_model", help="默认embedding模型")
+@click.option("--agent_model", help="agent模型")
+@click.option("--history_len", type=int, help="历史长度")
+@click.option("--max_tokens", type=int, help="最大tokens")
+@click.option("--temperature", type=float, help="温度")
+@click.option("--support_agent_models", multiple=True, help="支持的agent模型")
+@click.option("--model_providers_cfg_path_config", help="模型平台配置文件路径")
+@click.option("--model_providers_cfg_host", help="模型平台配置服务host")
+@click.option("--model_providers_cfg_port", type=int, help="模型平台配置服务port")
+@click.option("--clear", is_flag=True, help="清除配置")
+@click.option("--show", is_flag=True, help="显示配置")
+def model(**kwargs):
+
+    if kwargs["default_llm_model"]:
+        config_model_workspace.set_default_llm_model(llm_model=kwargs["default_llm_model"])
+    if kwargs["default_embedding_model"]:
+        config_model_workspace.set_default_embedding_model(embedding_model=kwargs["default_embedding_model"])
+
+    if kwargs["agent_model"]:
+        config_model_workspace.set_agent_model(agent_model=kwargs["agent_model"])
+
+    if kwargs["history_len"]:
+        config_model_workspace.set_history_len(history_len=kwargs["history_len"])
+
+    if kwargs["max_tokens"]:
+        config_model_workspace.set_max_tokens(max_tokens=kwargs["max_tokens"])
+
+    if kwargs["temperature"]:
+        config_model_workspace.set_temperature(temperature=kwargs["temperature"])
+
+    if kwargs["support_agent_models"]:
+        config_model_workspace.set_support_agent_models(support_agent_models=kwargs["support_agent_models"])
+
+    if kwargs["model_providers_cfg_path_config"]:
+        config_model_workspace.set_model_providers_cfg_path_config(model_providers_cfg_path_config=kwargs["model_providers_cfg_path_config"])
+
+    if kwargs["model_providers_cfg_host"]:
+        config_model_workspace.set_model_providers_cfg_host(model_providers_cfg_host=kwargs["model_providers_cfg_host"])
+
+    if kwargs["model_providers_cfg_port"]:
+        config_model_workspace.set_model_providers_cfg_port(model_providers_cfg_port=kwargs["model_providers_cfg_port"])
+
+    if kwargs["clear"]:
+        config_model_workspace.clear()
+    if kwargs["show"]:
+        print(config_model_workspace.get_config())
 
 
 if __name__ == "__main__":

libs/chatchat-server/chatchat/configs/__init__.py

@@ -363,108 +363,140 @@ def _import_embedding_keyword_file() -> Any:
     return EMBEDDING_KEYWORD_FILE
 
 
+def _import_ConfigModel() -> Any:
+    basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
+    load_mod = basic_config_load.get("load_mod")
+    ConfigModel = load_mod(basic_config_load.get("module"), "ConfigModel")
+
+    return ConfigModel
+
+
+def _import_ConfigModelFactory() -> Any:
+    basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
+    load_mod = basic_config_load.get("load_mod")
+    ConfigModelFactory = load_mod(basic_config_load.get("module"), "ConfigModelFactory")
+
+    return ConfigModelFactory
+
+
+def _import_ConfigModelWorkSpace() -> Any:
+    basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
+    load_mod = basic_config_load.get("load_mod")
+    ConfigModelWorkSpace = load_mod(basic_config_load.get("module"), "ConfigModelWorkSpace")
+
+    return ConfigModelWorkSpace
+
+
+def _import_config_model_workspace() -> Any:
+    model_config_load = CONFIG_IMPORTS.get("_model_config.py")
+    load_mod = model_config_load.get("load_mod")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
+    return config_model_workspace
+
+
 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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return DEFAULT_LLM_MODEL
+    return config_model_workspace.get_config().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
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
+
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return Agent_MODEL
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return HISTORY_LEN
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return MAX_TOKENS
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return TEMPERATURE
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return SUPPORT_AGENT_MODELS
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return LLM_MODEL_CONFIG
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return MODEL_PLATFORMS
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return MODEL_PROVIDERS_CFG_PATH_CONFIG
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return MODEL_PROVIDERS_CFG_HOST
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return MODEL_PROVIDERS_CFG_PORT
+    return config_model_workspace.get_config().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")
+    config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
 
-    return TOOL_CONFIG
+    return config_model_workspace.get_config().TOOL_CONFIG
 
 
 def _import_prompt_templates() -> Any:
@@ -524,6 +556,14 @@ def __getattr__(name: str) -> Any:
         return _import_ConfigBasicWorkSpace()
     elif name == "config_basic_workspace":
         return _import_config_basic_workspace()
+    elif name == "ConfigModel":
+        return _import_ConfigModel()
+    elif name == "ConfigModelFactory":
+        return _import_ConfigModelFactory()
+    elif name == "ConfigModelWorkSpace":
+        return _import_ConfigModelWorkSpace()
+    elif name == "config_model_workspace":
+        return _import_config_model_workspace()
     elif name == "log_verbose":
         return _import_log_verbose()
     elif name == "CHATCHAT_ROOT":
@@ -624,7 +664,6 @@ VERSION = "v0.3.0-preview"
 
 __all__ = [
     "VERSION",
-    "config_basic_workspace",
     "log_verbose",
     "CHATCHAT_ROOT",
     "DATA_PATH",
@@ -677,4 +716,12 @@ __all__ = [
     "ConfigBasicFactory",
     "ConfigBasicWorkSpace",
 
+    "config_basic_workspace",
+
+    "ConfigModel",
+    "ConfigModelFactory",
+    "ConfigModelWorkSpace",
+    
+    "config_model_workspace",
+
 ]

libs/chatchat-server/chatchat/configs/_basic_config.py

@@ -6,7 +6,6 @@ import sys
 import logging
 from typing import Any, Optional
 
-from chatchat.configs._core_config import CF
 
 sys.path.append(str(Path(__file__).parent))
 import _core_config as core_config
@@ -128,6 +127,9 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
     """
     config_factory_cls = ConfigBasicFactory
 
+    def __init__(self):
+        super().__init__()
+
     def _build_config_factory(self, config_json: Any) -> ConfigBasicFactory:
 
         _config_factory = self.config_factory_cls()
@@ -145,9 +147,6 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
     def get_type(cls) -> str:
         return ConfigBasic.class_name()
 
-    def __init__(self):
-        super().__init__()
-
     def get_config(self) -> ConfigBasic:
         return self._config_factory.get_config()
 
@@ -163,9 +162,5 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
         self._config_factory.log_format(log_format)
         self.store_config()
 
-    def clear(self):
-        logger.info("Clear workspace config.")
-        os.remove(self.workspace_config)
-
 
 config_basic_workspace: ConfigBasicWorkSpace = ConfigBasicWorkSpace()

libs/chatchat-server/chatchat/configs/_core_config.py

@@ -62,15 +62,13 @@ class ConfigWorkSpace(Generic[CF, F], ABC):
         self.workspace_config = os.path.join(self.workspace, "workspace_config.json")
         # 初始化工作空间配置，转换成json格式，实现Config的实例化
 
-        config_type_json = self._load_config()
-        if config_type_json is None:
+        _load_config = self._load_config()
+        if _load_config is None:
             self._config_factory = self._build_config_factory(config_json={})
             self.store_config()
 
         else:
-            config_type = config_type_json.get("type", None)
-            if self.get_type() != config_type:
-                raise ValueError(f"Config type mismatch: {self.get_type()} != {config_type}")
+            config_type_json = self.get_config_by_type(self.get_type())
 
             config_json = config_type_json.get("config")
             self._config_factory = self._build_config_factory(config_json)
@@ -98,9 +96,39 @@ class ConfigWorkSpace(Generic[CF, F], ABC):
         except FileNotFoundError:
             return None
 
+    @staticmethod
+    def _get_store_cfg_index_by_type(store_cfg, store_cfg_type) -> int:
+        if store_cfg is None:
+            raise RuntimeError("store_cfg is None.")
+        for cfg in store_cfg:
+            if cfg.get("type") == store_cfg_type:
+                return store_cfg.index(cfg)
+
+        return -1
+
+    def get_config_by_type(self, cfg_type) -> Dict[str, Any]:
+        store_cfg = self._load_config()
+        if store_cfg is None:
+            raise RuntimeError("store_cfg is None.")
+
+        get_lambda = lambda store_cfg_type: store_cfg[self._get_store_cfg_index_by_type(store_cfg, store_cfg_type)]
+        return get_lambda(cfg_type)
+
     def store_config(self):
         logger.info("Store workspace config.")
+        _load_config = self._load_config()
         with open(self.workspace_config, "w") as f:
             config_json = self.get_config().to_dict()
+
+            if _load_config is None:
+                _load_config = []
+            config_json_index = self._get_store_cfg_index_by_type(
+                store_cfg=_load_config,
+                store_cfg_type=self.get_type()
+            )
             config_type_json = {"type": self.get_type(), "config": config_json}
-            f.write(json.dumps(config_type_json, indent=4, ensure_ascii=False))
+            if config_json_index == -1:
+                _load_config.append(config_type_json)
+            else:
+                _load_config[config_json_index] = config_type_json
+            f.write(json.dumps(_load_config, indent=4, ensure_ascii=False))

libs/chatchat-server/chatchat/configs/_model_config.py

@@ -1,260 +1,450 @@
 import os
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Optional, List, Dict
 
-# 默认选用的 LLM 名称
-DEFAULT_LLM_MODEL = "chatglm3-6b"
+from dataclasses import dataclass
 
-# 默认选用的 Embedding 名称
-DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
+sys.path.append(str(Path(__file__).parent))
+import _core_config as core_config
+
+logger = logging.getLogger()
 
 
-# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
-Agent_MODEL = None
+class ConfigModel(core_config.Config):
+    DEFAULT_LLM_MODEL: Optional[str] = None
+    """默认选用的 LLM 名称"""
+    DEFAULT_EMBEDDING_MODEL: Optional[str] = None
+    """默认选用的 Embedding 名称"""
+    Agent_MODEL: Optional[str] = None
+    """AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])"""
+    HISTORY_LEN: Optional[int] = None
+    """历史对话轮数"""
+    MAX_TOKENS: Optional[int] = None
+    """大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度"""
+    TEMPERATURE: Optional[float] = None
+    """LLM通用对话参数"""
+    SUPPORT_AGENT_MODELS: Optional[List[str]] = None
+    """支持的Agent模型"""
+    LLM_MODEL_CONFIG: Optional[Dict[str, Dict[str, Any]]] = None
+    """LLM模型配置，包括了不同模态初始化参数"""
+    MODEL_PLATFORMS: Optional[List[Dict[str, Any]]] = None
+    """模型平台配置"""
+    MODEL_PROVIDERS_CFG_PATH_CONFIG: Optional[str] = None
+    """模型平台配置文件路径"""
+    MODEL_PROVIDERS_CFG_HOST: Optional[str] = None
+    """模型平台配置服务host"""
+    MODEL_PROVIDERS_CFG_PORT: Optional[int] = None
+    """模型平台配置服务port"""
+    TOOL_CONFIG: Optional[Dict[str, Any]] = None
+    """工具配置项"""
 
-# 历史对话轮数
-HISTORY_LEN = 3
+    @classmethod
+    def class_name(cls) -> str:
+        return cls.__name__
 
-# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
-MAX_TOKENS = None
-
-# LLM通用对话参数
-TEMPERATURE = 0.7
-# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
-
-SUPPORT_AGENT_MODELS = [
-    "chatglm3-6b",
-    "openai-api",
-    "Qwen-14B-Chat",
-    "Qwen-7B-Chat",
-    "qwen-turbo",
-]
+    def __str__(self):
+        return self.to_json()
 
 
-LLM_MODEL_CONFIG = {
-    # 意图识别不需要输出，模型后台知道就行
-    "preprocess_model": {
-        DEFAULT_LLM_MODEL: {
-            "temperature": 0.05,
-            "max_tokens": 4096,
-            "history_len": 100,
-            "prompt_name": "default",
-            "callbacks": False
-        },
-    },
-    "llm_model": {
-        DEFAULT_LLM_MODEL: {
-            "temperature": 0.9,
-            "max_tokens": 4096,
-            "history_len": 10,
-            "prompt_name": "default",
-            "callbacks": True
-        },
-    },
-    "action_model": {
-        DEFAULT_LLM_MODEL: {
-            "temperature": 0.01,
-            "max_tokens": 4096,
-            "prompt_name": "ChatGLM3",
-            "callbacks": True
-        },
-    },
-    "postprocess_model": {
-        DEFAULT_LLM_MODEL: {
-            "temperature": 0.01,
-            "max_tokens": 4096,
-            "prompt_name": "default",
-            "callbacks": True
-        }
-    },
-    "image_model": {
-        "sd-turbo": {
-            "size": "256*256",
-        }
-    }
-}
+@dataclass
+class ConfigModelFactory(core_config.ConfigFactory[ConfigModel]):
+    """ConfigModel工厂类"""
 
-# 可以通过 model_providers 提供转换不同平台的接口为openai endpoint的能力，启动后下面变量会自动增加相应的平台
-#   ### 如果您已经有了一个openai endpoint的能力的地址，可以在这里直接配置
-#   - platform_name 可以任意填写，不要重复即可
-#   - platform_type 以后可能根据平台类型做一些功能区分,与platform_name一致即可
-#   - 将框架部署的模型填写到对应列表即可。不同框架可以加载同名模型，项目会自动做负载均衡。
+    def __init__(self):
+        # 默认选用的 LLM 名称
+        self.DEFAULT_LLM_MODEL = "chatglm3-6b"
 
+        # 默认选用的 Embedding 名称
+        self.DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
 
-# 创建一个全局的共享字典
-MODEL_PLATFORMS = [
+        # AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
+        self.Agent_MODEL = None
 
-    {
-        "platform_name": "oneapi",
-        "platform_type": "oneapi",
-        "api_base_url": "http://127.0.0.1:3000/v1",
-        "api_key": "sk-",
-        "api_concurrencies": 5,
-        "llm_models": [
-            # 智谱 API
-            "chatglm_pro",
-            "chatglm_turbo",
-            "chatglm_std",
-            "chatglm_lite",
-            # 千问 API
+        # 历史对话轮数
+        self.HISTORY_LEN = 3
+
+        # 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
+        self.MAX_TOKENS = None
+
+        # LLM通用对话参数
+        self.TEMPERATURE = 0.7
+        # TOP_P = 0.95 # ChatOpenAI暂不支持该参数
+
+        self.SUPPORT_AGENT_MODELS = [
+            "chatglm3-6b",
+            "openai-api",
+            "Qwen-14B-Chat",
+            "Qwen-7B-Chat",
             "qwen-turbo",
-            "qwen-plus",
-            "qwen-max",
-            "qwen-max-longcontext",
-            # 千帆 API
-            "ERNIE-Bot",
-            "ERNIE-Bot-turbo",
-            "ERNIE-Bot-4",
-            # 星火 API
-            "SparkDesk",
-        ],
-        "embed_models": [
-            # 千问 API
-            "text-embedding-v1",
-            # 千帆 API
-            "Embedding-V1",
-        ],
-        "image_models": [],
-        "reranking_models": [],
-        "speech2text_models": [],
-        "tts_models": [],
-    },
+        ]
 
-    {
-        "platform_name": "xinference",
-        "platform_type": "xinference",
-        "api_base_url": "http://127.0.0.1:9997/v1",
-        "api_key": "EMPTY",
-        "api_concurrencies": 5,
-        "llm_models": [
-            "glm-4",
-            "qwen2-instruct",
-            "qwen1.5-chat",
-        ],
-        "embed_models": [
-            "bge-large-zh-v1.5",
-        ],
-        "image_models": [],
-        "reranking_models": [],
-        "speech2text_models": [],
-        "tts_models": [],
-    },
+        self.MODEL_PROVIDERS_CFG_PATH_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                                            "model_providers.yaml")
+        self.MODEL_PROVIDERS_CFG_HOST = "127.0.0.1"
 
-]
+        self.MODEL_PROVIDERS_CFG_PORT = 20000
 
-MODEL_PROVIDERS_CFG_PATH_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model_providers.yaml")
-MODEL_PROVIDERS_CFG_HOST = "127.0.0.1"
+        self._init_llm_work_config()
 
-MODEL_PROVIDERS_CFG_PORT = 20000
-# 工具配置项
-TOOL_CONFIG = {
-    "search_local_knowledgebase": {
-        "use": False,
-        "top_k": 3,
-        "score_threshold": 1.0,
-        "conclude_prompt": {
-            "with_result":
-                '<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'
-                '不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
-                '<已知信息>{{ context }}</已知信息>\n'
-                '<问题>{{ question }}</问题>\n',
-            "without_result":
-                '请你根据我的提问回答我的问题:\n'
-                '{{ question }}\n'
-                '请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n',
-        }
-    },
-    "search_internet": {
-        "use": False,
-        "search_engine_name": "bing",
-        "search_engine_config":
-            {
-                "bing": {
-                    "result_len": 3,
-                    "bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
-                    "bing_key": "",
+    def _init_llm_work_config(self):
+        """初始化知识库runtime的一些配置"""
+
+        self.LLM_MODEL_CONFIG = {
+            # 意图识别不需要输出，模型后台知道就行
+            "preprocess_model": {
+                self.DEFAULT_LLM_MODEL: {
+                    "temperature": 0.05,
+                    "max_tokens": 4096,
+                    "history_len": 100,
+                    "prompt_name": "default",
+                    "callbacks": False
                 },
-                "metaphor": {
-                    "result_len": 3,
-                    "metaphor_api_key": "",
-                    "split_result": False,
-                    "chunk_size": 500,
-                    "chunk_overlap": 0,
+            },
+            "llm_model": {
+                self.DEFAULT_LLM_MODEL: {
+                    "temperature": 0.9,
+                    "max_tokens": 4096,
+                    "history_len": 10,
+                    "prompt_name": "default",
+                    "callbacks": True
                 },
-                "duckduckgo": {
-                    "result_len": 3
+            },
+            "action_model": {
+                self.DEFAULT_LLM_MODEL: {
+                    "temperature": 0.01,
+                    "max_tokens": 4096,
+                    "prompt_name": "ChatGLM3",
+                    "callbacks": True
+                },
+            },
+            "postprocess_model": {
+                self.DEFAULT_LLM_MODEL: {
+                    "temperature": 0.01,
+                    "max_tokens": 4096,
+                    "prompt_name": "default",
+                    "callbacks": True
                 }
             },
-        "top_k": 10,
-        "verbose": "Origin",
-        "conclude_prompt":
-            "<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
-            "</指令>\n<已知信息>{{ context }}</已知信息>\n"
-            "<问题>\n"
-            "{{ question }}\n"
-            "</问题>\n"
-    },
-    "arxiv": {
-        "use": False,
-    },
-    "shell": {
-        "use": False,
-    },
-    "weather_check": {
-        "use": False,
-        "api_key": "S8vrB4U_-c5mvAMiK",
-    },
-    "search_youtube": {
-        "use": False,
-    },
-    "wolfram": {
-        "use": False,
-        "appid": "",
-    },
-    "calculate": {
-        "use": False,
-    },
-    "vqa_processor": {
-        "use": False,
-        "model_path": "your model path",
-        "tokenizer_path": "your tokenizer path",
-        "device": "cuda:1"
-    },
-    "aqa_processor": {
-        "use": False,
-        "model_path": "your model path",
-        "tokenizer_path": "yout tokenizer path",
-        "device": "cuda:2"
-    },
-    "text2images": {
-        "use": False,
-    },
-    # text2sql使用建议
-    # 1、因大模型生成的sql可能与预期有偏差，请务必在测试环境中进行充分测试、评估；
-    # 2、生产环境中，对于查询操作，由于不确定查询效率，推荐数据库采用主从数据库架构，让text2sql连接从数据库，防止可能的慢查询影响主业务；
-    # 3、对于写操作应保持谨慎，如不需要写操作，设置read_only为True,最好再从数据库层面收回数据库用户的写权限，防止用户通过自然语言对数据库进行修改操作；
-    # 4、text2sql与大模型在意图理解、sql转换等方面的能力有关，可切换不同大模型进行测试；
-    # 5、数据库表名、字段名应与其实际作用保持一致、容易理解，且应对数据库表名、字段进行详细的备注说明，帮助大模型更好理解数据库结构；
-    # 6、若现有数据库表名难于让大模型理解，可配置下面table_comments字段，补充说明某些表的作用。
-    "text2sql": {
-        "use": False,
-        # SQLAlchemy连接字符串，支持的数据库有：
-        # crate、duckdb、googlesql、mssql、mysql、mariadb、oracle、postgresql、sqlite、clickhouse、prestodb
-        # 不同的数据库请查询SQLAlchemy，修改sqlalchemy_connect_str，配置对应的数据库连接，如sqlite为sqlite:///数据库文件路径，下面示例为mysql
-        # 如提示缺少对应数据库的驱动，请自行通过poetry安装
-        "sqlalchemy_connect_str": "mysql+pymysql://用户名:密码@主机地址/数据库名称e",
-        # 务必评估是否需要开启read_only,开启后会对sql语句进行检查，请确认text2sql.py中的intercept_sql拦截器是否满足你使用的数据库只读要求
-        # 优先推荐从数据库层面对用户权限进行限制
-        "read_only": False,
-        #限定返回的行数
-        "top_k":50,
-        #是否返回中间步骤
-        "return_intermediate_steps": True,
-        #如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
-        "table_names":[],
-        #对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
-        "table_comments":{
-            # 如果出现大模型选错表的情况，可尝试根据实际情况填写表名和说明
-            # "tableA":"这是一个用户表，存储了用户的基本信息",
-            # "tanleB":"角色表",
+            "image_model": {
+                "sd-turbo": {
+                    "size": "256*256",
+                }
+            }
         }
-    },
-}
+
+        # 可以通过 model_providers 提供转换不同平台的接口为openai endpoint的能力，启动后下面变量会自动增加相应的平台
+        #   ### 如果您已经有了一个openai endpoint的能力的地址，可以在这里直接配置
+        #   - platform_name 可以任意填写，不要重复即可
+        #   - platform_type 以后可能根据平台类型做一些功能区分,与platform_name一致即可
+        #   - 将框架部署的模型填写到对应列表即可。不同框架可以加载同名模型，项目会自动做负载均衡。
+
+        # 创建一个全局的共享字典
+        self.MODEL_PLATFORMS = [
+
+            {
+                "platform_name": "oneapi",
+                "platform_type": "oneapi",
+                "api_base_url": "http://127.0.0.1:3000/v1",
+                "api_key": "sk-",
+                "api_concurrencies": 5,
+                "llm_models": [
+                    # 智谱 API
+                    "chatglm_pro",
+                    "chatglm_turbo",
+                    "chatglm_std",
+                    "chatglm_lite",
+                    # 千问 API
+                    "qwen-turbo",
+                    "qwen-plus",
+                    "qwen-max",
+                    "qwen-max-longcontext",
+                    # 千帆 API
+                    "ERNIE-Bot",
+                    "ERNIE-Bot-turbo",
+                    "ERNIE-Bot-4",
+                    # 星火 API
+                    "SparkDesk",
+                ],
+                "embed_models": [
+                    # 千问 API
+                    "text-embedding-v1",
+                    # 千帆 API
+                    "Embedding-V1",
+                ],
+                "image_models": [],
+                "reranking_models": [],
+                "speech2text_models": [],
+                "tts_models": [],
+            },
+
+            {
+                "platform_name": "xinference",
+                "platform_type": "xinference",
+                "api_base_url": "http://127.0.0.1:9997/v1",
+                "api_key": "EMPTY",
+                "api_concurrencies": 5,
+                "llm_models": [
+                    "glm-4",
+                    "qwen2-instruct",
+                    "qwen1.5-chat",
+                ],
+                "embed_models": [
+                    "bge-large-zh-v1.5",
+                ],
+                "image_models": [],
+                "reranking_models": [],
+                "speech2text_models": [],
+                "tts_models": [],
+            },
+
+        ]
+        # 工具配置项
+        self.TOOL_CONFIG = {
+            "search_local_knowledgebase": {
+                "use": False,
+                "top_k": 3,
+                "score_threshold": 1.0,
+                "conclude_prompt": {
+                    "with_result":
+                        '<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'
+                        '不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
+                        '<已知信息>{{ context }}</已知信息>\n'
+                        '<问题>{{ question }}</问题>\n',
+                    "without_result":
+                        '请你根据我的提问回答我的问题:\n'
+                        '{{ question }}\n'
+                        '请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n',
+                }
+            },
+            "search_internet": {
+                "use": False,
+                "search_engine_name": "bing",
+                "search_engine_config":
+                    {
+                        "bing": {
+                            "result_len": 3,
+                            "bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
+                            "bing_key": "",
+                        },
+                        "metaphor": {
+                            "result_len": 3,
+                            "metaphor_api_key": "",
+                            "split_result": False,
+                            "chunk_size": 500,
+                            "chunk_overlap": 0,
+                        },
+                        "duckduckgo": {
+                            "result_len": 3
+                        }
+                    },
+                "top_k": 10,
+                "verbose": "Origin",
+                "conclude_prompt":
+                    "<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
+                    "</指令>\n<已知信息>{{ context }}</已知信息>\n"
+                    "<问题>\n"
+                    "{{ question }}\n"
+                    "</问题>\n"
+            },
+            "arxiv": {
+                "use": False,
+            },
+            "shell": {
+                "use": False,
+            },
+            "weather_check": {
+                "use": False,
+                "api_key": "S8vrB4U_-c5mvAMiK",
+            },
+            "search_youtube": {
+                "use": False,
+            },
+            "wolfram": {
+                "use": False,
+                "appid": "",
+            },
+            "calculate": {
+                "use": False,
+            },
+            "vqa_processor": {
+                "use": False,
+                "model_path": "your model path",
+                "tokenizer_path": "your tokenizer path",
+                "device": "cuda:1"
+            },
+            "aqa_processor": {
+                "use": False,
+                "model_path": "your model path",
+                "tokenizer_path": "yout tokenizer path",
+                "device": "cuda:2"
+            },
+            "text2images": {
+                "use": False,
+            },
+            # text2sql使用建议
+            # 1、因大模型生成的sql可能与预期有偏差，请务必在测试环境中进行充分测试、评估；
+            # 2、生产环境中，对于查询操作，由于不确定查询效率，推荐数据库采用主从数据库架构，让text2sql连接从数据库，防止可能的慢查询影响主业务；
+            # 3、对于写操作应保持谨慎，如不需要写操作，设置read_only为True,最好再从数据库层面收回数据库用户的写权限，防止用户通过自然语言对数据库进行修改操作；
+            # 4、text2sql与大模型在意图理解、sql转换等方面的能力有关，可切换不同大模型进行测试；
+            # 5、数据库表名、字段名应与其实际作用保持一致、容易理解，且应对数据库表名、字段进行详细的备注说明，帮助大模型更好理解数据库结构；
+            # 6、若现有数据库表名难于让大模型理解，可配置下面table_comments字段，补充说明某些表的作用。
+            "text2sql": {
+                "use": False,
+                # SQLAlchemy连接字符串，支持的数据库有：
+                # crate、duckdb、googlesql、mssql、mysql、mariadb、oracle、postgresql、sqlite、clickhouse、prestodb
+                # 不同的数据库请查询SQLAlchemy，修改sqlalchemy_connect_str，配置对应的数据库连接，如sqlite为sqlite:///数据库文件路径，下面示例为mysql
+                # 如提示缺少对应数据库的驱动，请自行通过poetry安装
+                "sqlalchemy_connect_str": "mysql+pymysql://用户名:密码@主机地址/数据库名称e",
+                # 务必评估是否需要开启read_only,开启后会对sql语句进行检查，请确认text2sql.py中的intercept_sql拦截器是否满足你使用的数据库只读要求
+                # 优先推荐从数据库层面对用户权限进行限制
+                "read_only": False,
+                # 限定返回的行数
+                "top_k": 50,
+                # 是否返回中间步骤
+                "return_intermediate_steps": True,
+                # 如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
+                "table_names": [],
+                # 对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
+                "table_comments": {
+                    # 如果出现大模型选错表的情况，可尝试根据实际情况填写表名和说明
+                    # "tableA":"这是一个用户表，存储了用户的基本信息",
+                    # "tanleB":"角色表",
+                }
+            },
+        }
+
+    def default_llm_model(self, llm_model: str):
+        self.DEFAULT_LLM_MODEL = llm_model
+
+    def default_embedding_model(self, embedding_model: str):
+        self.DEFAULT_EMBEDDING_MODEL = embedding_model
+
+    def agent_model(self, agent_model: str):
+        self.Agent_MODEL = agent_model
+
+    def history_len(self, history_len: int):
+        self.HISTORY_LEN = history_len
+
+    def max_tokens(self, max_tokens: int):
+        self.MAX_TOKENS = max_tokens
+
+    def temperature(self, temperature: float):
+        self.TEMPERATURE = temperature
+
+    def support_agent_models(self, support_agent_models: List[str]):
+        self.SUPPORT_AGENT_MODELS = support_agent_models
+
+    def model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
+        self.MODEL_PROVIDERS_CFG_PATH_CONFIG = model_providers_cfg_path_config
+
+    def model_providers_cfg_host(self, model_providers_cfg_host: str):
+        self.MODEL_PROVIDERS_CFG_HOST = model_providers_cfg_host
+
+    def model_providers_cfg_port(self, model_providers_cfg_port: int):
+        self.MODEL_PROVIDERS_CFG_PORT = model_providers_cfg_port
+
+    def get_config(self) -> ConfigModel:
+        config = ConfigModel()
+        config.DEFAULT_LLM_MODEL = self.DEFAULT_LLM_MODEL
+        config.DEFAULT_EMBEDDING_MODEL = self.DEFAULT_EMBEDDING_MODEL
+        config.Agent_MODEL = self.Agent_MODEL
+        config.HISTORY_LEN = self.HISTORY_LEN
+        config.MAX_TOKENS = self.MAX_TOKENS
+        config.TEMPERATURE = self.TEMPERATURE
+        config.SUPPORT_AGENT_MODELS = self.SUPPORT_AGENT_MODELS
+        config.LLM_MODEL_CONFIG = self.LLM_MODEL_CONFIG
+        config.MODEL_PLATFORMS = self.MODEL_PLATFORMS
+        config.MODEL_PROVIDERS_CFG_PATH_CONFIG = self.MODEL_PROVIDERS_CFG_PATH_CONFIG
+        config.MODEL_PROVIDERS_CFG_HOST = self.MODEL_PROVIDERS_CFG_HOST
+        config.MODEL_PROVIDERS_CFG_PORT = self.MODEL_PROVIDERS_CFG_PORT
+        config.TOOL_CONFIG = self.TOOL_CONFIG
+
+        return config
+
+
+class ConfigModelWorkSpace(core_config.ConfigWorkSpace[ConfigModelFactory, ConfigModel]):
+    """
+    工作空间的配置预设, 提供ConfigModel建造方法产生实例。
+    """
+    config_factory_cls = ConfigModelFactory
+
+    def __init__(self):
+        super().__init__()
+
+    def _build_config_factory(self, config_json: Any) -> ConfigModelFactory:
+
+        _config_factory = self.config_factory_cls()
+        if config_json.get("DEFAULT_LLM_MODEL"):
+            _config_factory.default_llm_model(config_json.get("DEFAULT_LLM_MODEL"))
+        if config_json.get("DEFAULT_EMBEDDING_MODEL"):
+            _config_factory.default_embedding_model(config_json.get("DEFAULT_EMBEDDING_MODEL"))
+        if config_json.get("Agent_MODEL"):
+            _config_factory.agent_model(config_json.get("Agent_MODEL"))
+        if config_json.get("HISTORY_LEN"):
+            _config_factory.history_len(config_json.get("HISTORY_LEN"))
+        if config_json.get("MAX_TOKENS"):
+            _config_factory.max_tokens(config_json.get("MAX_TOKENS"))
+        if config_json.get("TEMPERATURE"):
+            _config_factory.temperature(config_json.get("TEMPERATURE"))
+        if config_json.get("SUPPORT_AGENT_MODELS"):
+            _config_factory.support_agent_models(config_json.get("SUPPORT_AGENT_MODELS"))
+        if config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"):
+            _config_factory.model_providers_cfg_path_config(config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"))
+        if config_json.get("MODEL_PROVIDERS_CFG_HOST"):
+            _config_factory.model_providers_cfg_host(config_json.get("MODEL_PROVIDERS_CFG_HOST"))
+        if config_json.get("MODEL_PROVIDERS_CFG_PORT"):
+            _config_factory.model_providers_cfg_port(config_json.get("MODEL_PROVIDERS_CFG_PORT"))
+
+        return _config_factory
+
+    @classmethod
+    def get_type(cls) -> str:
+        return ConfigModel.class_name()
+
+    def get_config(self) -> ConfigModel:
+        return self._config_factory.get_config()
+
+    def set_default_llm_model(self, llm_model: str):
+        self._config_factory.default_llm_model(llm_model)
+        self.store_config()
+
+    def set_default_embedding_model(self, embedding_model: str):
+        self._config_factory.default_embedding_model(embedding_model)
+        self.store_config()
+
+    def set_agent_model(self, agent_model: str):
+        self._config_factory.agent_model(agent_model)
+        self.store_config()
+
+    def set_history_len(self, history_len: int):
+        self._config_factory.history_len(history_len)
+        self.store_config()
+
+    def set_max_tokens(self, max_tokens: int):
+        self._config_factory.max_tokens(max_tokens)
+        self.store_config()
+
+    def set_temperature(self, temperature: float):
+        self._config_factory.temperature(temperature)
+        self.store_config()
+
+    def set_support_agent_models(self, support_agent_models: List[str]):
+        self._config_factory.support_agent_models(support_agent_models)
+        self.store_config()
+
+    def set_model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
+        self._config_factory.model_providers_cfg_path_config(model_providers_cfg_path_config)
+        self.store_config()
+
+    def set_model_providers_cfg_host(self, model_providers_cfg_host: str):
+        self._config_factory.model_providers_cfg_host(model_providers_cfg_host)
+        self.store_config()
+
+    def set_model_providers_cfg_port(self, model_providers_cfg_port: int):
+        self._config_factory.model_providers_cfg_port(model_providers_cfg_port)
+        self.store_config()
+
+
+config_model_workspace: ConfigModelWorkSpace = ConfigModelWorkSpace()

libs/chatchat-server/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-chatchat"
-version = "0.3.0.20240610.1"
+version = "0.3.0.20240611"
 description = ""
 authors = ["chatchat"]
 readme = "README.md"

libs/chatchat-server/tests/unit_tests/config/test_config.py

@@ -1,6 +1,12 @@
 from pathlib import Path
 
-from chatchat.configs import ConfigBasicFactory, ConfigBasic, ConfigBasicWorkSpace
+from chatchat.configs import (
+    ConfigBasicFactory,
+    ConfigBasic,
+    ConfigBasicWorkSpace,
+    ConfigModelWorkSpace,
+    ConfigModel
+)
 import os
 
 
@@ -36,3 +42,56 @@ def test_workspace_default():
     assert LOG_FORMAT is not None
     assert LOG_PATH is not None
     assert MEDIA_PATH is not None
+
+
+def test_config_model_workspace():
+
+    config_model_workspace: ConfigModelWorkSpace = ConfigModelWorkSpace()
+
+    assert config_model_workspace.get_config() is not None
+
+    config_model_workspace.set_default_llm_model(llm_model="glm4")
+    config_model_workspace.set_default_embedding_model(embedding_model="text1")
+    config_model_workspace.set_agent_model(agent_model="agent")
+    config_model_workspace.set_history_len(history_len=1)
+    config_model_workspace.set_max_tokens(max_tokens=1000)
+    config_model_workspace.set_temperature(temperature=0.1)
+    config_model_workspace.set_support_agent_models(support_agent_models=["glm4"])
+    config_model_workspace.set_model_providers_cfg_path_config(model_providers_cfg_path_config="model_providers.yaml")
+    config_model_workspace.set_model_providers_cfg_host(model_providers_cfg_host="127.0.0.1")
+    config_model_workspace.set_model_providers_cfg_port(model_providers_cfg_port=8000)
+
+    config: ConfigModel = config_model_workspace.get_config()
+
+    assert config.DEFAULT_LLM_MODEL == "glm4"
+    assert config.DEFAULT_EMBEDDING_MODEL == "text1"
+    assert config.Agent_MODEL == "agent"
+    assert config.HISTORY_LEN == 1
+    assert config.MAX_TOKENS == 1000
+    assert config.TEMPERATURE == 0.1
+    assert config.SUPPORT_AGENT_MODELS == ["glm4"]
+    assert config.MODEL_PROVIDERS_CFG_PATH_CONFIG == "model_providers.yaml"
+    assert config.MODEL_PROVIDERS_CFG_HOST == "127.0.0.1"
+    assert config.MODEL_PROVIDERS_CFG_PORT == 8000
+    config_model_workspace.clear()
+
+
+def test_model_config():
+    from chatchat.configs import (
+        DEFAULT_LLM_MODEL, DEFAULT_EMBEDDING_MODEL, Agent_MODEL, HISTORY_LEN, MAX_TOKENS, TEMPERATURE,
+        SUPPORT_AGENT_MODELS, MODEL_PROVIDERS_CFG_PATH_CONFIG, MODEL_PROVIDERS_CFG_HOST, MODEL_PROVIDERS_CFG_PORT,
+        TOOL_CONFIG, MODEL_PLATFORMS, LLM_MODEL_CONFIG
+    )
+    assert DEFAULT_LLM_MODEL is not None
+    assert DEFAULT_EMBEDDING_MODEL is not None
+    assert Agent_MODEL is None
+    assert HISTORY_LEN is not None
+    assert MAX_TOKENS is None
+    assert TEMPERATURE is not None
+    assert SUPPORT_AGENT_MODELS is not None
+    assert MODEL_PROVIDERS_CFG_PATH_CONFIG is not None
+    assert MODEL_PROVIDERS_CFG_HOST is not None
+    assert MODEL_PROVIDERS_CFG_PORT is not None
+    assert TOOL_CONFIG is not None
+    assert MODEL_PLATFORMS is not None
+    assert LLM_MODEL_CONFIG is not None