OpenAI in React: Integrating GPT-4 with Your React Application

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In this talk, attendees will learn how to integrate OpenAI's GPT-4 language model into their React applications, exploring practical use cases and implementation strategies to enhance user experience and create intelligent, interactive applications.

This talk has been presented at React Day Berlin 2023, check out the latest edition of this React Conference.

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FAQ

Artificial intelligence is a revolutionary change that helps businesses solve real problems and makes employees and individuals more productive. It is important for building intelligent and context-aware applications, which are in high demand for creating highly engaging and differentiating user experiences.

Jesse Hall is a senior developer advocate at MongoDB and is also known for his YouTube channel, Code Stacker.

Generative Pretrained Transformers (GPT) are large language models that perform various tasks such as natural language processing, content generation, and common sense reasoning. They are used to make applications smarter by enabling them to understand and generate human-like text.

GPT models have a static knowledge base, meaning they only know what they've been trained on. They can search the Internet for newer information, but there is no guarantee of accuracy. Additionally, they cannot access or learn from real-time proprietary data, which limits their effectiveness in certain applications.

Retrieval Augmented Generation (RAG) is a technique that involves creating vector embeddings for private or custom data and storing them in a vector database. User queries are then vectorized and searched against this database to find semantically related information, which is used to generate accurate and contextually relevant responses.

Vectors are numerical representations of data in a multidimensional space. They allow for the manipulation and understanding of complex data and are used in semantic search to find contextually relevant information. Vectors can represent text, images, video, or audio data.

Vectors are created through an encoder that organizes information in a virtual space. These vectors can be stored in a vector database and used in AI applications for semantic search. For example, vector embeddings can be used to find relevant information based on user queries.

MongoDB provides a vector database that can store vector embeddings for custom data. It also offers tools like MongoDB Atlas Vector Search, which can be integrated into React applications to enable smarter and context-aware functionalities.

To build an AI-powered documentation site, you need to create embeddings for your custom data, set up a Next.js app, and integrate MongoDB Atlas Vector Search. You can use tools like the LangChain text splitter, OpenAI embeddings, and MongoDB client to process and store your data. Detailed steps and configurations are provided in the tutorial by Jesse Hall.

For more detailed information, you can attend workshops conducted by Jesse Hall or visit the conference website where these workshops are listed. Additionally, Jesse Hall's YouTube channel, Code Stacker, provides valuable insights and tutorials on this topic.

Jesse Hall
Jesse Hall
11 min
12 Dec, 2023

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Video Summary and Transcription

AI is a revolutionary change that helps businesses solve real problems and make applications smarter. Vectors enable semantic search, allowing us to find contextually relevant information. We'll build an AI-powered documentation site that answers questions, provides contextually relevant information, and offers links for further exploration. To enable vector search with MongoDB, we use the LingChain method to connect to MongoDB, create vector embeddings for user queries, and find related documents using maximal marginal reference. Join the workshop for a complete start-to-finish guide and integrate MongoDB Vector Search into your next React-based AI application.

1. Introduction to AI and React

Short description:

AI is a revolutionary change that helps businesses solve real problems and make applications smarter. We'll explore the demand for intelligent apps, limitations of LLMs, and how to overcome them. Using GPT and RAG, we can augment React apps with smarter capabilities using vectors as building blocks for representing complex data.

Artificial intelligence. It's just a fad, right? It's gonna blow over like a blockchain. Well, actually I don't think so. In fact, AI is far from a fad. It's a revolutionary change. It's helping businesses solve real problems and making employees and individuals more productive. So let's talk about why AI matters now more than ever and how AI can take your react applications to the next level.

I'm Jesse Hall, a senior developer advocate at MongoDB. You might also know me from my YouTube channel, Code Stacker. So throughout this talk, we're going to explore the demand for intelligent apps, practical use cases, limitations of LLMs, how to overcome these limitations, the tech stack that we're going to use to build a smart react app and how to integrate GPT, make it smart and optimize the user experience.

There is a huge demand for building intelligence into our applications in order to make these modern, highly engaging applications and to make differentiating experiences for each of our users. We have something called Generative Pretrained Transformers or GPT. These large language models perform a variety of tasks from natural language processing to content generation, and even some elements of common sense reasoning, and they are the brains that are making our applications smarter. But there is a catch. GPTs are incredible, but they aren't perfect. One of their key limitations is their static knowledge base. They only know what they've been trained on. There are integrations with some models now that can search the Internet for newer information, but how do we know that that information that they're finding on the Internet is accurate? They can hallucinate. Very confidently, I might add. So how can we minimize this? Now they can't access or learn from real-time proprietary data, your data. And that's a big limitation, don't you think? The need for real-time, proprietary and domain specific data is why we can't rely on the LLMs as they are.

Well, this brings us to the focus of our talk today. It's not merely about leveraging the power of GPT in React, it's about taking your React applications to the next level by making them intelligent and context aware. We're going to explore how to augment React apps with smarter capabilities using large language models and boost those capabilities even further with retrieval augmented generation or RAG. Now, what's involved in retrieval augmented generation? First up, vectors. What are vectors? These are the building blocks that allow us to represent complex multidimensional data in a format that's easy to manipulate and understand. Now the simplest explanation is a vector is a numerical representation of data. An array of numbers and these numbers are coordinates in an in dimensional space where in is the array length. So, however, many numbers we have in the array is how many dimensions we have. Now, you'll also hear vectors referred to as vector embeddings or just embeddings.

2. Vectors and Retrieval Augmented Generation

Short description:

Vectors enable semantic search, allowing us to find contextually relevant information. They can be created through an encoder and used in retrieval augmented generation. Private data is converted into embeddings and stored in a vector database. User queries are vectorized and used for vector search to find related information.

So here's a real life example of vectors in use. When you go to a store and you ask a worker where to find something, many times they're going to say go to aisle 30, bay 15. And so that is a two dimensional vector. And we also notice at stores that similar items will be placed near each other for ease of searching and finding.

The light bulbs aren't just scattered all over the store, they're strategically placed to be found easily. And so, again, what makes vectors so special? They enable semantic search. In simpler terms, they let us find information that is contextually relevant, not just a keyword search. And the data source is not just limited to text, it can also be images, video or audio. These can all be converted to vectors.

So how do we go about creating these vectors? Well, this is done through an encoder. The encoder defines how the information is organized in the virtual space. So now let's tie all this back to retrieval augmented generation. So first we take our private data or custom data, whatever it may be, and generate our embeddings using an embedding model and then store those embeddings in a vector database. And once we have our embeddings for our custom data, we can now accept user queries to find relevant information within our custom data. Now to do this, we send the user's natural language query to an LLM, which vectorizes the query, and then we use vector search to find information that is closely related, semantically related to the user's query, and then we return those results.

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