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Integrating Context in OpenAI-Based Chat Applications

Integrating Context in OpenAI-Based Chat Applications Integrating Context in OpenAI-Based Chat Applications Introduction In modern conversational AI applications, context plays a crucial role in maintaining coherent and meaningful interactions. This article delves into the intricacies of integrating context in an OpenAI-based chat application. We will explore the necessity of context, the process of setting up context-aware conversations, and the technical implementation to manage and utilize context effectively. The Importance of Context Context in conversational AI refers to the ability of the system to remember previous interactions and use this information to provide more accurate and relevant responses. Without context, a chat application can struggle to maintain a logical flow in conversations, leading to disjointed and unsatisfactory user experiences. Example Scenario Consider the following interaction:

Integrating OpenAI API into Your Project

Integrating OpenAI API into Your Project Integrating OpenAI API into Your Project In this tutorial, we will guide you through the process of integrating the OpenAI API into your project. We will cover everything from installing the necessary package to making your first API call. Let's get started! Step 1: Install the OpenAI Package First, ensure that you have already installed the OpenAI package in your project. This is typically done at the beginning of your project setup. If you haven't installed it yet, you can do so using npm: npm install openai Step 2: Retrieve Your API Key Next, you need to obtain your OpenAI API key. To do this, navigate to the OpenAI dashboard and locate the API keys section. Please note the following important points: When you generate a new API key, you must copy it immediately. You won't be able to view

Using OpenAI API: Methods and Properties

Using OpenAI API: Methods and Properties Using OpenAI API: Methods and Properties In this article, we'll explore the methods and properties used to make API calls to OpenAI. We'll start by looking at the API reference in the dashboard, specifically focusing on the chat and chat completion object. API Reference In the OpenAI dashboard, navigate to the API Reference section. More specifically, look for the chat and then chat completion object . This object will be the response we receive from the API call. Response Object The response object includes a usage property that shows the tokens used in both the question and the answer, along with the total tokens. The actual response will be found in the choices array, specifically under the message property with the role assistant . Setting Up NodeJS If you don't have NodeJS, make sure

OpenAI API Playground

OpenAI API Playground OpenAI API Playground Once you sign up for an account, login. You want to choose the API, not ChatGPT. We're looking for API. This is going to be our dashboard. Here we have documentation. But for now, I want to go to the playground. This is where we can test the API. If you have used ChatGPT before, this window probably looks familiar. Essentially, yes, we can use this as ChatGPT. This is more of a joke, but if you have signed up for ChatGPT and use it once a month, this probably is a cheaper option because here you get charged only for the tokens, not a monthly fee. The idea is the same: provide questions and get back answers. On the left-hand side, you see the system. The system sets guidelines and expectations for how the AI is supposed to interact within the session. The default is "you are a helpful assistant," but you can change it to "you are a famous chef,&

Adding React Query to a Next.js Application

Adding React Query to a Next.js Application Adding React Query to a Next.js Application In this guide, we will integrate React Query into a Next.js application. Before we begin, it's important to note that the setup might seem tedious, but it is essentially a boilerplate setup taken directly from the React Query documentation. You only need to do it once, and you can copy and paste the code where needed. Setting Up the Query Client First, we need to provide our query client to each page. While this might seem like a downside, remember that it can be copied and pasted across pages. The setup is a bit more complex than in a traditional React application because we are using server components, but the benefits far outweigh the boilerplate. One significant benefit is that we can use server actions directly in our React Query code. When setting up the query or mutation function, we can use server a

Using React Query in a Next.js Project

Using React Query in a Next.js Project Using React Query in a Next.js Project As a big fan of React Query, I find it extremely useful in various projects. Despite its extensive capabilities, React Query's primary attraction for me is its simplicity and efficiency in handling data fetching and caching. Why Use React Query? React Query stands out for several reasons: Simple setup process. Cached requests make the application feel fast and responsive. For those new to React Query, let's cover some general concepts first. Installation Start by installing React Query and its DevTools: npm install react-query react-query/devtools Setup In a typical React project, you will need to set up a QueryClient and a QueryClientProvider : import { QueryClient, QueryClientProvider } from 'react-query'; import