AgentDOC-W1：ChatRobot

docs/en/Cookbook/chatrobot.md

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+# Adaptive Chatbot Construction Tutorial
+This tutorial demonstrates how to build an interactive chatbot using LazyLLM, leveraging the SenseNova model for natural language processing.You will learn how to initialize the model, configure environment variables, and implement a simple user interaction loop.
+
+By completing this tutorial, you will learn the following:
+
+- How to initialize the SenseNova model using the [OnlineChatModule][lazyllm.OnlineChatModule].
+- How to set up the API Key and Secret Key to connect to SenseNova correctly.
+- How to build an interactive chatbot that responds to user input.
+# Environment Setup
+Before starting, ensure you have installed LazyLLM:
+
+```bash
+pip install lazyllm
+```
+# Code Implementation
+
+## Step 1: Configure Environment Variables
+To use the SenseNova online model, you need to set the API Key and Secret Key. These credentials can be configured as environment variables.
+
+```python
+import os
+
+# Configure LazyLLM with SenseNova API Key and Secret Key
+os.environ["LAZYLLM_SENSENOVA_API_KEY"] = "YOUR_API_KEY"
+os.environ["LAZYLLM_SENSENOVA_SECRET_KEY"] = "YOUR_SECRET_KEY"
+```
+Parameter Description:
+LAZYLLM_SENSENOVA_API_KEY: The API Key provided by SenseNova for authentication.
+LAZYLLM_SENSENOVA_SECRET_KEY: The Secret Key provided by SenseNova for secure access.
+Make sure to replace "YOUR_API_KEY" and "YOUR_SECRET_KEY" with the actual credentials you received from SenseNova.
+
+## Step 2: Initialize the OnlineChatModule
+LazyLLM provides the OnlineChatModule to connect to online models like SenseNova. You can initialize the module using the following code:
+
+```python
+
+import lazyllm
+
+# Initialize LazyLLM's online chat module (using SenseNova)
+model = lazyllm.OnlineChatModule(provider="sensenova")
+```
+Parameter Description:
+provider="sensenova": Specifies that the SenseNova model is being used as the chat service provider.
+## Step 3: Build a User Interaction Loop
+Using a simple while loop, you can create real-time interactions between the user and the model:
+
+```python
+
+if __name__ == "__main__":
+    print("Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit"]:
+            print("Exiting...")
+            break
+
+        # Generate a response using the LazyLLM model
+        response = model(user_input)
+        print(f"Model: {response}")
+```
+Features Implemented:
+User input is sent to the SenseNova model for processing.
+The model's response is printed to the console.
+Typing "exit" or "quit" ends the conversation.
+### Complete Code
+Below is the complete implementation:
+
+```python
+
+import os
+import lazyllm
+
+# Configure LazyLLM with SenseNova API Key and Secret Key
+os.environ["LAZYLLM_SENSENOVA_API_KEY"] = "YOUR_API_KEY"
+os.environ["LAZYLLM_SENSENOVA_SECRET_KEY"] = "YOUR_SECRET_KEY"
+
+# Initialize LazyLLM's online chat module (using SenseNova)
+model = lazyllm.OnlineChatModule(provider="sensenova")
+
+# Test the chatbot
+if __name__ == "__main__":
+    print("Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit"]:
+            print("Exiting...")
+            break
+
+        # Generate a response using the LazyLLM model
+        response = model(user_input)
+        print(f"Model: {response}")
+```
+### Example Run
+Start the Chatbot
+When you run the script, you will see the following prompt:
+
+```bash
+Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.
+You:
+```
+Sample Conversation
+```bash
+You: Hello!
+Model: Hello! How can I assist you today?
+
+You: What's the weather like today?
+Model:  I don't have real-time capabilities or access to current data, including weather updates. To find out the weather for today, I recommend checking a reliable weather website like the Weather Channel, BBC Weather, or using a weather app on your smartphone for the most accurate and up-to-date information.
+
+You: exit
+Exiting...
+```
+Tips
+Environment Variable Configuration
+If you prefer not to hardcode the API Key and Secret Key in your script, you can use a .env file or system configuration to manage environment variables.
+Error Handling
+If the model fails to return a response, ensure that the API Key and Secret Key are configured correctly.
+Confirm that your network connection is stable.
+Extending Functionality
+You can integrate additional LazyLLM features (e.g., tool functions or RAG reasoning) to enhance the chatbot's capabilities.
+
+## Conclusion
+By following this tutorial, you have learned how to build a simple chatbot using LazyLLM and SenseNova. This chatbot serves as a foundation that can be expanded into a more versatile AI assistant. Try adding more features or combining tool functions to improve the chatbot's interactivity!
+

docs/zh/Cookbook/chatrobot.md

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+# 自适应聊天机器人构建教程
+本教程展示了如何使用 LazyLLM 构建一个交互式聊天机器人，基于 SenseNova 模型实现自然语言处理。您将学习如何初始化模型、配置环境变量以及实现一个简单的用户交互循环。
+
+通过本教程您将学习以下内容
+
+- 如何通过 [OnlineChatModule][lazyllm.OnlineChatModule] 初始化 SenseNova 模型。
+- 如何设置 API Key 和 Secret Key 以正确连接 SenseNova。
+- 如何编写一个基于用户输入与模型响应的交互式聊天机器人。
+# 环境准备
+在开始之前，请确保已安装 LazyLLM：
+
+```bash
+pip install lazyllm
+```
+# 代码实现
+
+## Step 1: 配置环境变量
+为了使用 SenseNova 在线模型，需要设置 API Key 和 Secret Key。这些密钥可以通过环境变量的方式配置。
+
+```python
+import os
+
+# 设置 LazyLLM 的 SenseNova API Key 和 Secret Key
+os.environ["LAZYLLM_SENSENOVA_API_KEY"] = "YOUR_API_KEY"
+os.environ["LAZYLLM_SENSENOVA_SECRET_KEY"] = "YOUR_SECRET_KEY"
+```
+参数说明：
+LAZYLLM_SENSENOVA_API_KEY：SenseNova 提供的 API Key，用于身份验证。
+LAZYLLM_SENSENOVA_SECRET_KEY：SenseNova 提供的 Secret Key，用于安全验证。
+确保将 "YOUR_API_KEY" 和 "YOUR_SECRET_KEY" 替换为您从 SenseNova 获取的实际密钥。
+
+## Step 2: 初始化 OnlineChatModule
+LazyLLM 提供了 OnlineChatModule 用于连接在线模型（如 SenseNova）。我们可以通过以下代码初始化模块：
+
+```python
+
+import lazyllm
+
+# 初始化 LazyLLM 的在线聊天模块 (基于 SenseNova)
+model = lazyllm.OnlineChatModule(provider="sensenova")
+```
+参数说明：
+provider="sensenova"：指定使用 SenseNova 模型作为聊天服务提供方。
+## Step 3: 构建用户交互循环
+通过一个简单的 while 循环，可以实现用户与模型的实时交互：
+
+```python
+
+if __name__ == "__main__":
+    print("Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit"]:
+            print("Exiting...")
+            break
+
+        # 使用 LazyLLM 模型生成回复
+        response = model(user_input)
+        print(f"Model: {response}")
+```
+实现功能：
+用户输入会传递给 SenseNova 模型进行处理。
+模型返回的响应会打印到控制台。
+输入 "exit" 或 "quit" 可退出对话。
+### 完整代码
+以下是完整的代码实现：
+
+```python
+
+import os
+import lazyllm
+
+# 设置 LazyLLM 的 SenseNova API Key 和 Secret Key
+os.environ["LAZYLLM_SENSENOVA_API_KEY"] = "YOUR_API_KEY"
+os.environ["LAZYLLM_SENSENOVA_SECRET_KEY"] = "YOUR_SECRET_KEY"
+
+# 初始化 LazyLLM 的在线聊天模块 (基于 SenseNova)
+model = lazyllm.OnlineChatModule(provider="sensenova")
+
+# 测试模型交互
+if __name__ == "__main__":
+    print("Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit"]:
+            print("Exiting...")
+            break
+
+        # 使用 LazyLLM 模型生成回复
+        response = model(user_input)
+        print(f"Model: {response}")
+```
+### 示例运行结果
+启动聊天机器人
+运行脚本后，您会看到以下提示：
+
+```bash
+Welcome to LazyLLM Chatbot! Type 'exit' or 'quit' to end the chat.
+You:
+```
+示例对话
+```bash
+You: Hello!
+Model: Hello! How can I assist you today?
+
+You: What's the weather like today?
+Model:  I don't have real-time capabilities or access to current data, including weather updates. To find out the weather for today, I recommend checking a reliable weather website like the Weather Channel, BBC Weather, or using a weather app on your smartphone for the most accurate and up-to-date information.
+
+You: exit
+Exiting...
+```
+小贴士
+环境变量配置：
+
+如果不想在代码中硬编码 API Key 和 Secret Key，可以通过 .env 文件或系统配置管理环境变量。
+错误处理：
+
+如果模型未能返回响应，请确保 API Key 和 Secret Key 配置正确。
+检查网络连接是否稳定。
+扩展功能：
+
+您可以集成更多 LazyLLM 的功能（如工具函数或 RAG 推理）来增强聊天机器人的能力。
+
+## 结语
+通过本教程，您已经学会如何使用 LazyLLM 和 SenseNova 构建一个简单的聊天机器人。该机器人可以作为基础，进一步扩展为多功能的 AI 助手。尝试添加更多功能或结合工具函数来提高机器人的交互能力吧！
+