Tutorials: Chat

Note: This article assumes you have basic Node.js knowledge and are comfortable with environment variables. The code snippets in this article were run on a Windows OS. This GitHub repo contains all the files used in this article. Recently, applications or websites have been built with some form of chat. Whether it is through social

Chat applications have moved well beyond the basic IRC messaging they started as. With chat platforms such as Stream, you get access to a whole array of rich features: reactions, presence notifications, file uploads, and slash commands. These features change straightforward chat into meaningful real-time applications. But how do these work under the hood? By

Our aim today is straightforward. We learn how to integrate an AI assistant with Stream's Chat APIs and make it blazing fast, like Le Chat by Mistral AI, using the LLM inference platforms. After the February 2025 updates and improvements to Le Chat by Mistral, many developers and power users have compared its response speed

Deepseek is the latest LLM to hit the digital shelves. It boasts high-quality reasoning at a fraction of the cost of current state-of-the-art models, OpenAI o1 and o3-mini, and Gemini 2.0 Flash Thinking. DeepSeek R1 is open-source, which means two things. First, developers can examine the model's architecture, training process, and weights directly, enabling a

Chat has become a necessary feature in most products. Businesses want to integrate quick, real-time communication to connect with their users. It can be for HR team management, hiring, onboarding, or within existing teams – but chat is almost everywhere. If you are a developer or product manager looking to integrate a chat API into

JavaScript is the foundation of the modern web. Without it, you don’t have the rich interaction you expect from websites, like real-time updates, video, or chat. But JS brings a problem: bloat. Modern web apps often ship megabytes of JavaScript to the browser. This slows initial page load and hurts performance, especially for mobile devices.

AI agent chats are mostly a 1:1 experience. But that misses a clear opportunity--having an AI member of your team. If every team member was participating in a chat with AI, you could collaborate as a group, create shared knowledge bases, or solve problems together more efficiently. So, let's build that. We're going to extend

Integrating AI into enterprise applications often challenges getting accurate and efficient results from LLMs. The main reason is that LLMs are trained on large datasets rather than specifically on your enterprise's data. These challenges may usually include hallucinations, outdated information presentation, and more. This article explores the integration of AI agents, or, Agentic Retrieval Augmented