Learn OpenAI Assistant APIs by Forking This Web Chat App

Table of Contents

1. Introduction
2. Building an Assistance Wrapper
   • Setting Up the Chat Interface
   • Creating an Assistant
   • Creating a Thread
   • Creating a Message
3. The Three Layers of an Assistant
   • Assistant
   • Thread
   • Message
4. Frontend Code
   • Communicating with the OpenAI API
   • Storing Data in the Local Database
   • Managing Tenancy
5. Backend Code
   • Creating Assistants, Threads, and Messages
   • Managing Runs and Polling for Message Completion
   • Updating Descriptions
6. Enforcing User Sign up Restrictions
7. Deploying to Production

Building an Assistance Wrapper

In this article, we will learn how to use the OpenAI Assistance API to build an assistance wrapper - a chat interface that allows users to Interact with an AI-powered assistant. We will cover the steps involved in setting up the chat interface, creating assistants, threads, and messages, and communicating with the OpenAI API using Node.js.

Introduction

The OpenAI Assistance API allows developers to build powerful AI assistants that can provide conversational responses to user queries. In this tutorial, we will walk through the process of building an assistance wrapper using the OpenAI API and Node.js. We will provide step-by-step instructions for setting up the chat interface, creating assistants, threads, and messages, and managing the communication between the frontend and backend.

Setting Up the Chat Interface

To get started, we need to set up a chat interface that users can interact with. The chat interface will allow users to enter their queries and receive responses from the AI assistant. We will Create a simple web-Based chat interface using React and Node.js. The chat interface will have features such as user authentication, the ability to create assistants, threads, and messages, and display the responses from the AI assistant.

Creating an Assistant

Once the chat interface is set up, we can start creating AI assistants. An assistant is the top-level entity in our assistance wrapper. It represents a specific AI assistant that users can interact with. We will provide a name and instructions for the assistant, and also specify whether the assistant should have a code interpreter or retrieval capabilities. We will use the OpenAI API to create the assistant and store its details in our local database.

Creating a Thread

After creating an assistant, we can proceed to create threads. A thread represents a conversation or interaction between a user and the AI assistant. Each thread can have multiple messages. We will provide a message as the initial input for the thread and send it to the OpenAI API for processing. We will also update the user interface to display the responses from the AI assistant.

Creating a Message

A message is a user query or response within a thread. We can create messages by entering text inputs in the chat interface and sending them to the OpenAI API for processing. The API will generate a response based on the message and we will display it in the chat interface. We will also store the messages in our local database for future reference and display the message history to the users.

The Three Layers of an Assistant

An assistance wrapper consists of three main layers: the assistant, the thread, and the message. Each layer has its own functionality and allows for more complex interactions with the AI assistant.

Assistant

The assistant is the top-level entity in our assistance wrapper. It represents a specific AI assistant that users can interact with. We can create multiple assistants, each with its own set of instructions and capabilities. The assistant is responsible for managing the overall conversation flow and handling user queries and responses.

Thread

A thread represents a conversation or interaction between a user and the AI assistant. Each thread can have multiple messages, representing the back-and-forth communication between the user and the assistant. Threads allow us to keep track of multiple conversations and provide Context for the AI assistant's responses.

Message

A message is a user query or response within a thread. Messages provide the input for the AI assistant and trigger the generation of appropriate responses. Each message is sent to the OpenAI API for processing and the generated response is displayed in the chat interface. Messages can also be stored in the local database for future reference and analysis.

Frontend Code

In the frontend code, we will handle the user interface and interaction with the OpenAI API. The frontend code will be responsible for creating new assistants, threads, and messages, as well as displaying the responses from the AI assistant.

Communicating with the OpenAI API

To interact with the OpenAI API, we will use the OpenAI JavaScript Package. This package provides convenient methods for making API calls and handling the responses. We will import the package and set up a connection to the OpenAI API using the API keys provided by OpenAI. We will also define functions to create assistants, threads, and messages, and send requests to the OpenAI API to retrieve the responses.

Storing Data in the Local Database

To keep track of the created assistants, threads, and messages, we will store them in our local database. We will use the database models provided by Gadget, which include the necessary fields to store the required information. When creating a new assistant, thread, or message, we will make API calls to the respective data models and store the data in our database. This will allow us to access and display the data in the chat interface and retrieve it in the future.

Managing Tenancy

To ensure that each user can only access and interact with their own data, we will implement tenancy in our application. Tenancy refers to the separation of data and resources for different users. We will use filters in our API endpoints to ensure that each user can only access their own assistants, threads, and messages. This will prevent users from viewing or modifying data that belongs to other users.

Backend Code

In the backend code, we will handle the server-side logic and interactions with the OpenAI API. The backend code will be responsible for creating assistants, threads, and messages, as well as managing the runs and polling for message completion.

Creating Assistants, Threads, and Messages

To create assistants, threads, and messages, we will define global actions in our backend code. These global actions will handle the creation of AI assistants, threads, and messages, and interact with the OpenAI API to generate responses. We will use the OpenAI JavaScript package to make API calls, pass the required parameters, and retrieve the responses. The created assistants, threads, and messages will be stored in the local database for future reference.

Managing Runs and Polling for Message Completion

Creating a message involves generating a response from the OpenAI API and waiting for the response to be completed. We will implement logic to handle the runs and polling for message completion. After creating a message, we will initiate a run and periodically check the status of the run until it is completed. This polling mechanism will allow us to retrieve the response from the OpenAI API and display it in the chat interface.

Updating Descriptions

To provide descriptive information for threads, we will update the descriptions based on the user queries. After a message is processed, we will check if the description for the thread is null. If it is null, we will make an API call to the OpenAI API with a user query to generate a description. The generated description will then be stored in the local database and displayed in the chat interface.

Enforcing User Sign up Restrictions

To restrict user sign up to specific email addresses, we will implement logic to validate the email addresses during the sign-up process. We will check if the user's email matches the allowed email address specified in the environment variables. If the email does not match, we will prevent the user from signing up and display an error message. This restriction ensures that only authorized users can create accounts and access the assistance wrapper.

Deploying to Production

To deploy the assistance wrapper to a production environment, we will use the deployment functionality provided by Gadget. We will click the deploy button and select the production environment to deploy our application. Gadget will copy the development environment to the production environment, ensuring that the application is live and accessible to users. The production environment will provide a faster and more responsive user experience without the development-specific features.

Highlights

• Building an assistance wrapper using the OpenAI Assistance API and Node.js
• Setting up a chat interface and allowing users to interact with an AI-powered assistant
• Creating assistants, threads, and messages to manage conversations
• Using the OpenAI API to generate responses and display them in the chat interface
• Managing runs and polling for message completion
• Enforcing user sign up restrictions to ensure only authorized users can access the application
• Deploying the assistance wrapper to a production environment for faster and more responsive user experience