ConfigModelWorkSpace实现

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/_model_config.py

@@ -1,26 +1,78 @@
 import os
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Optional, List, Dict
 
+from dataclasses import dataclass
+
+sys.path.append(str(Path(__file__).parent))
+import _core_config as core_config
+
+logger = logging.getLogger()
+
+
+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
+    """模型平台配置文件host"""
+    MODEL_PROVIDERS_CFG_PORT: Optional[int] = None
+    """模型平台配置文件port"""
+    TOOL_CONFIG: Optional[Dict[str, Any]] = None
+    """工具配置项"""
+
+    @classmethod
+    def class_name(cls) -> str:
+        return cls.__name__
+
+    def __str__(self):
+        return self.to_json()
+
+
+@dataclass
+class ConfigModelFactory(core_config.ConfigFactory[ConfigModel]):
+    """ConfigModel工厂类"""
+
+    def __init__(self):
         # 默认选用的 LLM 名称
-DEFAULT_LLM_MODEL = "chatglm3-6b"
+        self.DEFAULT_LLM_MODEL = "chatglm3-6b"
 
         # 默认选用的 Embedding 名称
-DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
+        self.DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
-
 
         # 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
+        self.Agent_MODEL = None
 
         # 历史对话轮数
-HISTORY_LEN = 3
+        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
-MAX_TOKENS = None
+        self.MAX_TOKENS = None
 
         # LLM通用对话参数
-TEMPERATURE = 0.7
+        self.TEMPERATURE = 0.7
         # TOP_P = 0.95 # ChatOpenAI暂不支持该参数
 
-SUPPORT_AGENT_MODELS = [
+        self.SUPPORT_AGENT_MODELS = [
             "chatglm3-6b",
             "openai-api",
             "Qwen-14B-Chat",
@@ -28,11 +80,21 @@ SUPPORT_AGENT_MODELS = [
             "qwen-turbo",
         ]
 
+        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"
 
-LLM_MODEL_CONFIG = {
+        self.MODEL_PROVIDERS_CFG_PORT = 20000
+
+        self._init_llm_work_config()
+
+    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": {
-        DEFAULT_LLM_MODEL: {
+                self.DEFAULT_LLM_MODEL: {
                     "temperature": 0.05,
                     "max_tokens": 4096,
                     "history_len": 100,
@@ -41,7 +103,7 @@ LLM_MODEL_CONFIG = {
                 },
             },
             "llm_model": {
-        DEFAULT_LLM_MODEL: {
+                self.DEFAULT_LLM_MODEL: {
                     "temperature": 0.9,
                     "max_tokens": 4096,
                     "history_len": 10,
@@ -50,7 +112,7 @@ LLM_MODEL_CONFIG = {
                 },
             },
             "action_model": {
-        DEFAULT_LLM_MODEL: {
+                self.DEFAULT_LLM_MODEL: {
                     "temperature": 0.01,
                     "max_tokens": 4096,
                     "prompt_name": "ChatGLM3",
@@ -58,7 +120,7 @@ LLM_MODEL_CONFIG = {
                 },
             },
             "postprocess_model": {
-        DEFAULT_LLM_MODEL: {
+                self.DEFAULT_LLM_MODEL: {
                     "temperature": 0.01,
                     "max_tokens": 4096,
                     "prompt_name": "default",
@@ -78,9 +140,8 @@ LLM_MODEL_CONFIG = {
         #   - 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
-MODEL_PLATFORMS = [
+        self.MODEL_PLATFORMS = [
 
             {
                 "platform_name": "oneapi",
@@ -139,13 +200,8 @@ MODEL_PLATFORMS = [
             },
 
         ]
-
-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"
-
-MODEL_PROVIDERS_CFG_PORT = 20000
         # 工具配置项
-TOOL_CONFIG = {
+        self.TOOL_CONFIG = {
             "search_local_knowledgebase": {
                 "use": False,
                 "top_k": 3,
@@ -258,3 +314,137 @@ TOOL_CONFIG = {
                 }
             },
         }
+
+    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 = "0.3.0.20240606"
+version = "0.3.0.20240611"
 description = ""
 authors = ["chatchat"]
 readme = "README.md"
@@ -121,7 +121,6 @@ extended_testing = [
     "xmltodict",
     "faiss-cpu",
     "openapi-pydantic",
-    "markdownify",
     "arxiv",
     "sqlite-vss",
     "rapidocr-onnxruntime",

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

@@ -1,6 +1,10 @@
 from pathlib import Path
 
-from chatchat.configs import ConfigBasicFactory, ConfigBasic, ConfigBasicWorkSpace
+from chatchat.configs import (
+    ConfigBasicFactory,
+    ConfigBasic,
+    ConfigBasicWorkSpace
+)
 import os
 
 
@@ -36,3 +40,6 @@ 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():