87 lines
2.9 KiB
Markdown
87 lines
2.9 KiB
Markdown
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# Documentation
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- Class name: ConversableAgentCreatorAdvanced
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- Category: SALT/Shakers/Agents
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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ConversableAgentCreatorAdvanced节点专门用于创建具有可定制功能的高级会话代理，包括集成大型语言模型（LLM）和个性化系统消息。它允许创建能够处理复杂交互并基于特定配置提供定制响应的AI代理。
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# Input types
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## Required
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- name
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- name参数指定了所创建的会话代理的身份，作为唯一标识符和交互标签。
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- Comfy dtype: STRING
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- Python dtype: str
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- system_message
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- system_message参数定义了代理用于指导其交互的初始消息或指令集，为其操作设定了基调和上下文。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- llm_model
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- llm_model参数允许指定一个大型语言模型，以赋予代理先进的理解和响应生成能力。
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- Comfy dtype: LLM_MODEL
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- Python dtype: dict
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- default_auto_reply
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- 指定代理在无法生成特定回复时使用的默认响应，确保了一种备用通信方式。
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- Comfy dtype: STRING
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- Python dtype: str
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- description
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- 代理目的或能力的简短描述，用于其他代理的识别或参考。
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- Comfy dtype: STRING
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- Python dtype: str
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# Output types
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- agent
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- 输出是一个配置了提供参数的会话代理，准备好进行交互并能够处理复杂任务。
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- Comfy dtype: AGENT
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- Python dtype: ConversableAgent
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class ConversableAgentCreatorAdvanced:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"name": ("STRING", {"multiline": False, "placeholder": "Assistant"}),
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"system_message": ("STRING", {
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"multiline": True,
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"default": "You are a helpful AI assistant. You can help with document QA. Return 'TERMINATE' when the task is done."
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}),
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},
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"optional": {
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"llm_model": ("LLM_MODEL",),
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# default auto reply when no code execution or llm-based reply is generated.
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"default_auto_reply": ("STRING", {"multiline": True}),
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# a short description of the agent, this description is used by other agents.
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"description": ("STRING", {"multiline": True}),
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}
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}
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RETURN_TYPES = ("AGENT",)
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RETURN_NAMES = ("agent",)
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FUNCTION = "create_agent"
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CATEGORY = f"{MENU_NAME}/Shakers/Agents"
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def create_agent(self, name, system_message, llm_model=None, default_auto_reply="", description=None):
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agent = ConversableAgent(
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name=name,
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system_message=system_message,
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llm_config={"config_list": [{"model": llm_model["llm"].model, "api_key": llm_model["llm"].api_key}]} if llm_model is not None else False,
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human_input_mode="NEVER",
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default_auto_reply=default_auto_reply,
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description=description if description is not None else system_message,
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)
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return (agent,)
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```
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