Tutorials: Chat

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 These challenges may usually include hallucinations, outdated information presentation, and more.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

In the previous article, we extended our API with endpoints to create and retrieve notes and integrated the new endpoints into the application. In this article, we will extend our application with full-stack functionality for editing and deleting notes. Editing Notes Adding the API Request Before creating the API, let's set up a dependency to

In the previous article, we extended our application with authentication and integrated it with the API. Now, will start building a note-taking application. We will focus on adding full-stack functionality for creating and viewing notes. Adding Dependencies Before building, let's add a few dependencies on the client side. Run the following command in a terminal

In this post, we will see how to build a Python server allowing frontend chat SDKs to start and stop an AI agent for a channel in Stream Chat. Building polished AI assistants can be challenging. Features like streaming responses, table components, and code generation require complex implementation across SDKs and the backend. To ease

Have you ever wanted to chat with the characters in your favorite book? Talk to Heathcliff about his origins, Harry Potter about his first impressions of Hogwarts, Jane Eyre about Lowood, or Lizzie Bennet about Mr. Darcy's proposal. Or, maybe, like us, you can’t wait to interrogate WebRTC For The Curious to learn more about

Earlier this year, payment service Klarna launched an AI customer service assistant. Within the first month, this AI had 2.3 million conversations, the equivalent of 700 agents, and led to a 25% drop in repeated inquiries. Klarna estimated it would “drive $40 million USD in profit improvement” in 2024. Customer success is an ideal opportunity

In this post, we will see how we can build a NodeJS server that will allow frontend chat SDKs to start and stop an assistant for a given channel in Stream Chat. Building polished AI assistants can be challenging. Features like streaming responses, table components, and code generation require complex implementation across SDKs and the