> ## Documentation Index
> Fetch the complete documentation index at: https://developer.watson-orchestrate.ibm.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Generative prompt node
Use a generative prompt node to generate AI-driven content using large language models (LLMs). To configure a generative prompt node in your agentic workflow, call the `prompt()` method. In this method, define the following parameters:
| Parameter | Type | Required | Description |
| ---------------------- | ----------------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| name | string | Yes | Unique identifier for the node. |
| display\_name | string | No | Display name for the node. |
| system\_prompt | string or list\[string] | No | Initial instructions that guide the LLM’s behavior.
Starting from version 1.14.0, it also supports expressions. For example: `flow.input.variable_1`. |
| user\_prompt | string or list\[string] | No | The specific request or task you want the LLM to perform.
Starting from version 1.14.0, it also supports expressions. For example: `flow.input.variable_1`. |
| prompt\_examples | list\[PromptExample] | No | Examples of user prompts. You can configure: - **input:** The example input prompt.
- **expected\_output:** The expected output for the given input.
- **enabled:** A boolean value to enable or disable the example.
|
| llm | string | No | LLM used for content generation. |
| llm\_parameters | PromptLLMParameters | No | Parameters for the LLM. You can configure: - **temperature:** Controls randomness. Higher values produce more diverse outputs.
- **min\_new\_tokens:** Sets the minimum number of tokens to generate.
- **max\_new\_tokens:** Sets the maximum number of tokens to generate.
- **top\_k:** Limits token selection to the top k most likely options.
- **top\_p:** Uses nucleus sampling to select from the top p cumulative probability.
- **stop\_sequences:** Defines sequences that stop generation when encountered.
|
| description | string | No | Description of the node. |
| input\_schema | type\[BaseModel] | No | Input schema for the LLM. |
| output\_schema | type\[BaseModel] | No | Output schema for the LLM. |
| input\_map | DataMap | No | Define input mappings using a structured collection of Assignment objects. |
| error\_handler\_config | NodeErrorHandlerConfi | No | Defines the configuration for the retry option using a JSON structure. In this JSON, set the following fields: - `error_message`: an optional string that describes the retry error.
- `max_retries`: an optional integer that limits how many times the node retries.
- `retry_interval`: an optional integer that sets the interval between retries in milliseconds.
|
The following example shows how to configure a generative prompt node in a Python function and instantiate it in a agentic workflow:
```py Python [expandable] theme={null}
'''
Build a simple hello world agentic workflow that will combine the result of two tools.
'''
from datetime import datetime
from typing import Optional
from pydantic import BaseModel, Field
from ibm_watsonx_orchestrate.flow_builder.flows import END, Flow, flow, START, PromptNode
from .email_helpdesk import email_helpdesk
class Message(BaseModel):
"""
This class represents the content of a support request message.
Attributes:
message (str): support request message
"""
message: str
requester_name: Optional[str] = Field(default=None, description="Name of the support requestor.")
requester_email: Optional[str] = Field(default=None, description="Email address of the support requestor.")
received_on: Optional[str|datetime] = Field(default=None, description="The date when the support message was received.")
class SupportInformation(BaseModel):
requester_name: str | None = Field(description="Name of the support requestor.")
requester_email: str | None = Field(description="Email address of the support requestor.")
summary: str = Field(description="A high level summary of the support issue.")
details: str = Field(description="Original text of the support request.")
order_number: str | None = Field(description="The order number.")
received_on: datetime | None = Field(description="The date when the support message was received.")
def build_prompt_node(aflow: Flow) -> PromptNode:
prompt_node = aflow.prompt(
name="extract_support_info",
display_name="Extract information from a support request message.",
description="Extract information from a support request message.",
system_prompt=[
"You are a customer support processing assistant, your job take the supplied support request received from email,",
"and extract the information in the output as specified in the schema."
],
user_prompt=[
"Here is the {message}"
],
llm="meta-llama/llama-3-3-70b-instruct",
llm_parameters={
"temperature": 0,
"min_new_tokens": 5,
"max_new_tokens": 400,
"top_k": 1,
"stop_sequences": ["Human:", "AI:"]
},
error_handler_config={
"error_message": "An error has occurred while invoking the LLM",
"max_retries": 1,
"retry_interval": 1000
},
input_schema=Message,
output_schema=SupportInformation
)
return prompt_node
@flow(
name = "extract_support_info",
input_schema=Message,
output_schema=SupportInformation
)
def build_extract_support_info(aflow: Flow = None) -> Flow:
"""
Creates a agentic workflow that will use the Prompt node to extract information from a support
message, and forward the summary to the helpdesk.
This agentic workflow will rely on the agentic workflow engine to perform automatic data mapping at runtime.
Args:
flow (Flow, optional): During deployment of the agentic workflow model, it will be passed a agentic workflow instance.
Returns:
Flow: The created agentic workflow.
"""
email_helpdesk_node = aflow.tool(email_helpdesk)
prompt_node = build_prompt_node(aflow)
aflow.sequence(START, prompt_node, email_helpdesk_node, END)
return aflow
```