2.1 KiB
2.1 KiB
∞ Message (Advanced)
Documentation
- Class name:
LLMChatMessagesAdv - Category:
SALT/Language Toolkit/Messages - Output node:
False
The LLMChatMessagesAdv node is designed to prepare chat messages by encapsulating system and user prompts into a structured format. This node facilitates the creation of chat interactions by organizing input prompts into a sequence of messages, thereby enabling a more structured and coherent dialogue flow.
Input types
Required
system_prompt- The system prompt represents the initial message or context provided by the system. It sets the stage for the chat interaction, guiding the user's response.
- Comfy dtype:
STRING - Python dtype:
str
user_prompt- The user prompt captures the user's input or question in response to the system prompt. It plays a crucial role in driving the conversation forward.
- Comfy dtype:
STRING - Python dtype:
str
Output types
llm_message- Comfy dtype:
LIST - A list of chat messages that includes both the system and user prompts, structured for further processing or interaction.
- Python dtype:
List[ChatMessage]
- Comfy dtype:
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class LLMChatMessagesAdv:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"system_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "You are a dog, you cannot speak, only woof, and react as a dog would."}),
"user_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "plaeholder": "What is your name?"}),
},
}
RETURN_TYPES = ("LIST", )
RETURN_NAMES = ("llm_message", )
FUNCTION = "prepare_messages"
CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Messages"
def prepare_messages(self, system_prompt, user_prompt):
messages = [
ChatMessage(role=MessageRole.SYSTEM, content=system_prompt ),
ChatMessage(role=MessageRole.USER, content=user_prompt ),
]
return (messages,)