This topic describes how to configure the Natural Language Generate component. This component allows you to use large language models for multi-round conversations, knowledge retrieval, and content generation.
Component information
AI large model-generated content may contain issues. Please carefully evaluate and verify before use with caution.
Icon
Name
Natural Language Generate
Preparations
Go to the canvas page of an existing flow or a new flow.
Go to the canvas page of an existing flow.
Log on to Chat App Message Service Console. Choose Chat Flow > Flow Management. Click the name of the flow that you want to edit. The canvas page of the flow appears.
Create a new flow to go to the canvas page. For more information, see Create a flow.
Procedure
Click the Natural Language Generate icon on the canvas to view the configurations on the right.
Configure the component based on your needs. For more information, see Parameters.
Click Save in the upper-right corner. In the message that appears, click Save.
Parameters
You can set Implementation Type to Model or Application. Different implementation types have different parameters. The following tables describe the specific parameters.
Implementation Type - Model
Parameter | Description |
Protocol | The protocol of the model service. Valid value: OpenAI. |
baseUrl | The endpoint for the model service. Example: https://api.openai.com/v1 or another OpenAI-compatible endpoint. |
apiKey | The key of the model service. |
Model Name | The model name. Example: gpt-3.5-turbo or qwen-plus. |
Initial Prompt | The initial prompt input for the model session, used to guide its output. Example: You are a witty comedian, please use humorous language in the following Q&A. |
Model Input | The current round of model conversation input can directly reference or embed multiple variables within a text. Example: {{incomingMessage}} or “Please help me find information about {{topic}}.” |
Model Output Variable Name | The variable name for output of this round in the model conversation can be reused in subsequent processes and used as the content of a message reply. |
Fallback Text | This content will be used as the output when the model service is unavailable. Example: Sorry, I am temporarily unable to answer your question. |
Implementation Type - Application
Parameter | Description |
Protocol | The protocol of the application service. Valid value: DashScope. Note For more information about applications, see Application building. |
apiKey | The key of the application service. Note For more information, see Obtain an API key. |
workspaceId | The workspace ID where the agent, workflow, or agent orchestration application resides. It needs to be passed when calling an application in a sub-workspace, but not when calling an application in the default workspace. Note For information about workspaces, see Authorize a sub-workspace to use models. |
appId | The application ID. |
Application Input | The current round of application conversation input can directly reference or embed multiple variables within a text. Example: {{incomingMessage}} or Please help me find information about {{topic}}. |
Custom Pass-through Parameters | Custom pass-through parameters. Example: {"city": "Hangzhou"}. |
Application Output Variable Name | The variable name for output of this round in the application conversation can be reused in subsequent processes and used as the content of a message reply. |
Fallback Text | This content will be used as the output when the application service is unavailable. Example: Sorry, I am temporarily unable to answer your question. |