In this article, we’ll develop an AI-powered research tool using JavaScript, focusing on leveraging the latest artificial intelligence (AI) advancements to sift through tons of data faster. We’ll start by explaining basic AI concepts to help you understand how the research tool will work. We’ll also explore the limitations of the research tool and some available tools that will help us enhance our AI research tool’s capabilities in a way that allows it to access tailored information more efficiently. At the end of the article, you’ll have created an advanced AI research assistant tool to help you gain insights quicker and make more informed research-backed decisions.
Table of Contents
- Background and Basics
- Vector embeddings
- Retrieval methods
- JavaScript’s role in AI development
- Introducing OpenAI’s Node SDK
Background and Basics
Before we start building, it’s important we discuss some fundamental concepts that will help you better understand how popular AI-powered applications like Bard and ChatGPT work. Let’s begin with vector embeddings.
Vector embeddings
Vector embeddings are numerical representations of text-based data. They are essential because they allow AI models to understand the context of the text provided by the user and find the semantic relationship between the provided text and the tons of knowledge they’ve been trained on. These vector embeddings can then be stored in vector databases like Pinecone, allowing optimal search and retrieval of stored vectors.
Retrieval methods
AI models have been fine-tuned to provide satisfactory answers. To do that efficiently, they’ve been trained on vast amounts of data. They’ve also been built to rely on efficient retrieval techniques — like semantic similarity search — to quickly find the most relevant data chunks (vector embeddings) to the query provided. When we supply the model with external data, as we’ll do in subsequent steps, this process becomes retrieval-augmented generation. This method combines all we’ve learned so far, allowing us to enhance a model’s performance with external data and synthesize it with similar vector embeddings to provide more accurate and reliable data.
JavaScript’s role in AI development
JavaScript has been the most popular programming language for the past 11 years, according to the 2023 Stack Overflow survey. It powers most of the world’s web interfaces, has a robust developer ecosystem, and enjoys versatile cross-platform compatibility with other key web components like browsers. In the early stages of the AI revolution, Python was the primary language used by AI researchers to train novel AI models. However, as these models become consumer-ready, there’s a growing need to create full-stack, dynamic, and interactive web applications to showcase the latest AI advancements to end-users. This is where JavaScript shines. Combined with HTML and CSS, JavaScript is the best choice for web and (to some extent) mobile development. This is why AI companies like OpenAI and Mistral have been building developer kits that JavaScript developers can use to create AI-powered development accessible to a broader audience.
Introducing OpenAI’s Node SDK
The OpenAI’s Node SDK provides a toolkit that exposes a suite of APIs that JavaScript developers can use to interact with their AI models’ capabilities. The GPT 3.5 and GPT 4 model series, Dall-E, TTS (text to speech), and Whisper (speech-to-text models) are available via the SDK. In the next section, we’ll use the latest GPT 4 model to build a simple example of our research assistant.
