Set the default value of bing_api_key to null in websearch.yml, and u…

…pdate the quick start section in the README.

Set the default value of bing_api_key to null in websearch.yml, and update the quick start section in the README.

README.md

@@ -77,54 +77,52 @@ If you wish to use smart devices to access your agents, we provide a smartphone
 
 ## 🚀 Quick Start 
 ### Hello World
-1. **Adjust Python Path**: The script modifies the Python path to ensure it can locate necessary modules. Verify the path is correct for your setup:
+### 1、Configuration
 
-   ```python
-   CURRENT_PATH = Path(__file__).parents[0]
-   sys.path.append(os.path.abspath(CURRENT_PATH.joinpath('../../')))
+The container.yaml file is a configuration file that manages dependencies and settings for different components of the system. To set up your configuration:
+
+1. Generate the container.yaml file:
+   ```bash
+   cd examples/step2_outfit_with_switch
+   python compile_container.py
    ```
-   - **CURRENT_PATH**: This is the path to the current directory.
-   - **sys.path.append**: This adds the path to the current directory to the Python path. This is to allow importing packages from the examples directory later.
+   This will create a container.yaml file with default settings under `examples/step2_outfit_with_switch`.
 
 
-2. **Initialize Logging**: The script sets up logging to track application events. You can adjust the logging level (`INFO`, `DEBUG`, etc.) as needed:
 
-   ```python
-   logging.init_logger("omagent", "omagent", level="INFO")
-   ```
+2. Configure your LLM settings in `configs/llms/gpt.yml` and `configs/llms/text_res.yml`:
 
-3. **Create and Execute Workflow**: The script creates a workflow and adds a task to it. It then starts the agent client to execute the workflow:
-
-   ```python
-    from examples.step1_simpleVQA.agent.simple_vqa.simple_vqa import SimpleVQA
-    from examples.step1_simpleVQA.agent.input_interface.input_interface import InputIterface
-
-    workflow = ConductorWorkflow(name='example1')
-    task1 = simple_task(task_def_name='InputIterface', task_reference_name='input_task')
-    task2 = simple_task(task_def_name='SimpleVQA', task_reference_name='simple_vqa', inputs={'user_instruction': task1.output('user_instruction')})
-    workflow >> task1 >> task2
-
-
-    workflow.register(True)
-
-    agent_client = DefaultClient(interactor=workflow, config_path='examples/step1_simpleVQA/configs', workers=[InputIterface()])
-    agent_client.start_interactor()
+   - Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file
+   ```bash
+   export custom_openai_key="your_openai_api_key"
+   export custom_openai_endpoint="your_openai_endpoint"
    ```
 
-   - **Workflow**: Defines the sequence of tasks. 'name' is the name of the workflow， please make sure it is unique.
-   - **Task**: Represents a unit of work, in this case, we use SimpleVQA from the examples. 'task_def_name' represents the corresponding class name, 'task_reference_name' represents the name in the conductor.
-   - **AppClient**: Starts the agent client to execute the workflow. Here we use AppClient, if you want to use CLI, please use DefaultClient.
-   - **agent_client.start_interactor()**: This will start the worker corresponding to the registered task, in this case, it will start SimpleVQA and wait for the conductor's scheduling.
+3. Update settings in the generated `container.yaml`:
+      - Configure Redis connection settings, including host, port, credentials, and both `redis_stream_client` and `redis_stm_client` sections.
+   - Update the Conductor server URL under conductor_config section
+   - Adjust any other component settings as needed
+
+For more information about the container.yaml configuration, please refer to the [container module](./docs/concepts/container.md)
+
+### 2、Running the Example
 
-4. **Run the Script**  
-  Execute the script using Python:  
+1. Run the outfit with switch example:
+
+   For terminal/CLI usage: Input and output are in the terminal window
    ```bash
+   cd examples/step2_outfit_with_switch
+   python run_cli.py
+   ```
+
+   For app/GUI usage: Input and output are in the app
+   ```bash
+   cd examples/step2_outfit_with_switch
    python run_app.py
-   ```  
-    **Ensure the workflow engine is running before executing the script.**
+   ```
 
 
-### 🏗 Architecture
+## 🏗 Architecture
 The design architecture of OmAgent adheres to three fundamental principles:  
 1. Graph-based workflow orchestration;   
 2. Native multimodality;   

README_ZH.md

@@ -75,53 +75,49 @@ docker compose -f docker/conductor/docker-compose.yml up -d
 
 ## 🚀 快速开始 
 ### Hello World
-1. **调整Python路径**：该脚本修改Python路径，以确保可以定位必要的模块。请验证路径对于您的设置是否正确：
+### 1、配置
 
-   ```python
-   CURRENT_PATH = Path(__file__).parents[0]
-   sys.path.append(os.path.abspath(CURRENT_PATH.joinpath('../../')))
+`container.yaml` 文件是一个管理系统中不同组件的依赖和设置的配置文件。按以下步骤设置您的配置：
+
+1. 生成 `container.yaml` 文件：
+   ```bash
+   cd examples/step2_outfit_with_switch
+   python compile_container.py
    ```
-   - **CURRENT_PATH**：这是当前目录的路径。
-   - **sys.path.append**：这将当前目录的路径添加到Python路径中。这是为了允许稍后从示例目录导入包。
+   这将在 `examples/step2_outfit_with_switch` 下创建一个具有默认设置的 `container.yaml` 文件。
 
-2. **初始化日志记录**：该脚本设置日志记录以跟踪应用程序事件。您可以根据需要调整日志记录级别（`INFO`，`DEBUG`等）：
+2. 在 `configs/llms/gpt.yml` 和 `configs/llms/text_res.yml` 中配置您的 LLM 设置：
 
-   ```python
-   logging.init_logger("omagent", "omagent", level="INFO")
+   - 通过环境变量或直接修改 yml 文件来设置您的 OpenAI API 密钥或兼容的 endpoint
+   ```bash
+   export custom_openai_key="your_openai_api_key"
+   export custom_openai_endpoint="your_openai_endpoint"
    ```
 
-3. **创建和执行工作流**：该脚本创建一个工作流并向其中添加一个任务。然后启动代理客户端以执行工作流：
-
-   ```python
-    from examples.step1_simpleVQA.agent.simple_vqa.simple_vqa import SimpleVQA
-    from examples.step1_simpleVQA.agent.input_interface.input_interface import InputIterface
-
-    workflow = ConductorWorkflow(name='example1')
-    task1 = simple_task(task_def_name='InputIterface', task_reference_name='input_task')
-    task2 = simple_task(task_def_name='SimpleVQA', task_reference_name='simple_vqa', inputs={'user_instruction': task1.output('user_instruction')})
-    workflow >> task1 >> task2
-
-
-    workflow.register(True)
-
-    agent_client = DefaultClient(interactor=workflow, config_path='examples/step1_simpleVQA/configs', workers=[InputIterface()])
-    agent_client.start_interactor()
-   ```
+3. 更新生成的 `container.yaml` 中的设置：
+   - 配置 Redis 连接设置，主要是主机地址、端口、密码凭证，包括 `redis_stream_client` 和 `redis_stm_client` 部分都要进行设置。
+   - 在 `conductor_config` 下更新 Conductor 服务器的 URL
+   - 根据需要调整其他组件设置
+
+有关 `container.yaml` 配置的更多信息，请参阅 [container 模块](./docs/concepts/container.md)
 
-   - **Workflow**：定义任务序列。'name'是工作流的名称， 请保证唯一性。
-   - **Task**：表示工作单元，在本例中，我们使用来自示例的SimpleVQA。'task_def_name'表示对应的类名，'task_reference_name'表示在conductor中的名称。
-   - **AppClient**：启动代理客户端以执行工作流。这里我们使用AppClient，如果您想使用CLI，请使用DefaultClient。
-   - **agent_client.start_interactor()**：这将启动与注册任务对应的工作器，在本例中，它将启动SimpleVQA并等待conductor的调度。
+### 2、运行示例
+
+1. 运行 outfit with switch 示例：
+
+   对于终端/CLI 使用：输入和输出在终端窗口中
+   ```bash
+   cd examples/step2_outfit_with_switch
+   python run_cli.py
+   ```
 
-4. 配置参数  
-   TODO:修改配置文件或设置环境变量
-5. **运行脚本**  
-  使用Python执行脚本：  
+   对于app/GUI 使用：输入和输出在应用程序中
    ```bash
+   cd examples/step2_outfit_with_switch
    python run_app.py
    ```
-   **在执行脚本之前，请确保工作流引擎已经部署并正在运行。**
 
+## 🏗 架构
 OmAgent的设计架构遵循三项基本原则：  
 1. 基于图的工作流编排；  
 2. 本地多模态；  

examples/step2_outfit_with_switch/configs/tools/websearch.yml

@@ -1,5 +1,5 @@
 llm: ${sub| text_res}
 tools:
   - name: WebSearch
-    bing_api_key: ${env| bing_api_key, your_bing_api_key}
+    bing_api_key: ${env| bing_api_key, null}
     llm: ${sub|text_res}

examples/step3_outfit_with_loop/configs/tools/websearch.yml

@@ -1,5 +1,5 @@
 llm: ${sub| text_res}
 tools:
   - name: WebSearch
-    bing_api_key: ${env| bing_api_key, your_bing_api_key}
+    bing_api_key: ${env| bing_api_key, null}
     llm: ${sub|text_res}

examples/step4_outfit_with_ltm/outfit_from_storage/configs/tools/websearch.yml

@@ -1,5 +1,5 @@
 llm: ${sub| text_res}
 tools:
   - name: WebSearch
-    bing_api_key: ${env| bing_api_key, bing_api_key}
+    bing_api_key: ${env| bing_api_key, null}
     llm: ${sub| text_res}

omagent-core/src/omagent_core/utils/build.py

@@ -82,8 +82,11 @@ def prep_config(sub_config: dict, config: dict, forbid_keys: list):
                     sub_config[key] = env_value
                 elif not env_value and default_value:
                     sub_config[key] =default_value.strip()
+                    if sub_config[key] == "null" or sub_config[key] == "~":
+                        sub_config[key] = None
                 else:
                     raise ValueError(f'Environmental variable {env_key} need to be set.')
+
 
         elif isinstance(conf, dict):
             prep_config(sub_config[key], config, forbid_keys)