agentscope.init(
    model_configs=[
        {
            "model_type": "dashscope_chat",
            "config_name": "qwen_config",
            "model_name": "qwen-max",
            "api_key": f"{os.environ.get('DASHSCOPE_API_KEY')}",
        },
        {
            "model_type": "dashscope_text_embedding",
            "config_name": "qwen_emb_config",
            "model_name": "text-embedding-v2",
            "api_key": f"{os.environ.get('DASHSCOPE_API_KEY')}",
        },
    ],
)

{
    "class": "LlamaIndexAgent",
    "args": {
      "name": "AgentScope Framework Code Assistant",
      "sys_prompt": "You're a helpful assistant about coding. You can very familiar with the framework code of AgentScope.",
      "model_config_name": "qwen_config",
      "emb_model_config_name": "qwen_emb_config",
      "rag_config": {
            "load_data": {
              "loader": {
                "create_object": true,
                "module": "llama_index.core",
                "class": "SimpleDirectoryReader",
                "init_args": {
                  "input_dir": "../../src/agentscope",
                  "recursive": true,
                  "required_exts": [".py"]
                }
              }
            },
            "store_and_index": {
              "transformations": [
                {
                  "create_object": true,
                  "module": "llama_index.core.node_parser",
                  "class": "CodeSplitter",
                  "init_args": {
                    "language": "python",
                    "chunk_lines": 100
                  }
                }
              ]
            },
            "chunk_size": 2048,
            "chunk_overlap": 40,
            "similarity_top_k": 10,
            "log_retrieval": false,
            "recent_n_mem": 1
      }
    }
  }

with open("./agent_config.json", "r", encoding="utf-8") as f:
    agent_configs = json.load(f)
tutorial_agent = LlamaIndexAgent(**agent_configs[0]["args"])
code_explain_agent = LlamaIndexAgent(**agent_configs[1]["args"])
summarize_agent = DialogAgent(**agent_configs[2]["args"])
user_agent = UserAgent()

while True:
    x = user_agent()
    x.role = "user"  # to enforce dashscope requirement on roles
    if len(x["content"]) == 0 or str(x["content"]).startswith("exit"):
        break
    # 教程助理回答
    tutorial_response = tutorial_agent(x)
    # 代码讲解助理回答
    code_explain = code_explain_agent(x)
    # 汇总信息
    msg = Msg(
        name="user",
        role="user",
        content=tutorial_response["content"]
        + "\n"
        + code_explain["content"]
        + "\n"
        + x["content"],
    )
    #总结
    summarize_agent(msg)

User: what is the difference between DialogAgent and DictDialogAgent?

AgentScope Tutorial Assistant: `DialogAgent` and `DictDialogAgent` are both agents in the AgentScope framework, but they differ in how they handle conversations and the level of customization:
1. **DialogAgent**: This is a general-purpose agent class that manages dialogues by understanding context and generating coherent responses. （本文中省略...）
2. **DictDialogAgent**: This agent type seems to be specialized for scenarios where the agent's behavior needs to adhere to specific constraints or rules defined in a dictionary or through a structured prompt.（本文中省略...）
While both agents are capable of holding a conversation, the `DialogAgent` is more versatile and generally （本文中省略...）

AgentScope Framework Code Assistant: In the given context, `DictDialogAgent` and `DialogAgent` are both subclasses of `AgentBase`, designed for different use cases within a conversational AI system. Here's a brief comparison:
1. **DictDialogAgent**:
   - This agent specifically deals with responses returned by the model in dictionary format.
   - （本文中省略...）
2. **DialogAgent**:
   - This is a more general-purpose dialog agent without the assumption that responses are in dictionary format.
   - （本文中省略...）
Both agents utilize prompts to request specific formats from the model and have mechanisms to handle and process the model's responses. （本文中省略...）

Summarize Assistant: **Summary:** 
The `DialogAgent` and `DictDialogAgent` are both classes in the AgentScope framework, designed for managing conversational AI interactions. They differ primarily in their intended usage and response handling:
1. **DialogAgent**:
   - This is a generic agent class meant for a wide variety of conversation contexts.
   - （本文中省略...）
2. **DictDialogAgent**:
   - This agent is more specialized and suited for scenarios where the dialogue must adhere to specific rules or constraints encoded in a dictionary or structured prompt.
   - （本文中省略...）
In summary, `DialogAgent` is more versatile and adaptable to various conversational contexts, while `DictDialogAgent` is tailored for environments where the dialogue is more predictable and rule-bound,