Chat completion trigger

Defines the entry point for a Flow that executes an AI chat completion request.

You'll typically use this trigger when you want to implement a Flow that serves as the backend for an interactive chat, allowing users to have a conversation with an AI by asking questions and follow-up questions. The trigger also provides a context property, allowing arbitrary data to be passed to the Flow to use for filtering or other types of business logic, supporting workloads such as RAG to help the LLM generate more relevant responses.

Example
The example above shows a Flow that performs a chat completion. It supports follow-up questions by loading the chat history for the session, feeding it into the chat model, and then saving the updated chat history (including the response) back to the database before returning the response to the client.

Properties

Name	Type	Description
Title	Optional	A description of the action.
Test data	Optional	A JSON string that can be in dev mode to test the Flow before it's called from the outside (for example InVision). The JSON must have format as described in Test data below.
Output variable name	Required	The name of the variable containing the ChatCompletionRequest object. (See [Returns](#returns below))

Test data

The Test data property must have the following format:

{
    "sessionId": "some-id",
    "prompt": "some question",
    "context": "some context"
}

Returns

The trigger outputs a ChatCompletionRequest object with the following properties.

Property name	Type	Description
SessionId	String	The id of the chat session provided by the client. Use this value to store and retrive the chat history between requests, enabling users to ask follow-up questions.
Prompt	String	The question asked by the user.
Context	String	A value provided by the client (for example a product id, file name or JSON string) that can be used to provide context to the chat completion process (for example as additional parameters to vector search)

Description

To perform a chat completion, you need at least a user prompt (the question asked by the user). This is provided by the Prompt property of the ChatCompletionRequest object returned from the trigger (described above), and can be mapped to the User prompt property of an AI chat completion action, such as the Azure OpenAI chat completion action. You should also enable the user to have a conversation (asking follow-up questions) with the chat model by managing the chat history. For this purpose, use the SessionId to store and retrive the chat history between requests.

Note that a Flow (and consequently a chat completion) is stateless, so to enable users to have a conversation with an AI by asking follow-up questions, you need to provide the chat history to the chat model along with the user prompt and any other data for every request. For this purpose, you can use the SessionId provided by the ChatCompletionRequest object to store and retrive the chat history from a database between requests. The SessionId is provided by the chat client, allowing users to start new sessions when they want.