Intent

In this example, we will create a function that is able to tell the intent of the user's most recent message in the conversation.

This can be useful as:

• First step in a pipeline to filter options

This takes advantage of a custom output parser to not only ensure formatting, but slightly transform the output.

Step 1 - Prepare Types & Intents

@code{8-35} ts:no-line-numbers

Step 2 - Prepare Prompt

@code{37-76} ts:no-line-numbers

Step 3 - Create Custom Output Parser

While we tell the LLM to format its response as JSON, it's a string. We still need to parse and validate the response so it can be used in our code. A custom output parser will reformat the output from the LLM, and provide typings for our LLM executor. @code{77-118} ts:no-line-numbers

Step 4 - Create LLM Executor

Combine the prompt, LLM, and parser into a single function. @code{120-127} ts:no-line-numbers

Step 4 - Use it!

import { identifyIntent } from "./somewhere"

// a chat history, loaded from somewhere
const chatHistory = [];

const response = await identifyIntent().execute({
    // the input you get from somewhere
    input: "Hey I'll be in town next week and need a car",
    intents,
    chatHistory
});

/**
 * 
 * console.log(response)
 * {
 *   "intent": "rent_car",
 *   "confidence": 90,
 * }
 **/

// the intent is focused on the most recent message
chatHistory.push({ 
    role: "user",
    content: "Hey I'll be in town next week and need a car"
});

const response2 = await identifyIntent().execute({
    input: "Actually I need a place to sleep more",
    intents,
    chatHistory
});

/**
 * 
 * console.log(response)
 * {
 *   "intent": "book_hotel",
 *   "confidence": 90,
 * }
 **/

Complete File

@code{8-127} ts