Build AI Chat with Convex Vector Search

The post describes building an AI-powered chat interface using Convex, a full-stack development platform with a built-in vector search feature. This implementation is part of a series comparing different AI chatbot setups, emphasizing the use of Convex for message storage and context retrieval, while leveraging OpenAI for embeddings and LLM chat completion. The process involves embedding data from the Convex documentation site, handling user queries, and retrieving relevant contextual information for generating responses. The backend uses a structured schema to manage messages, documents, chunks, and embeddings, ensuring efficient data handling and retrieval. The frontend implementation involves a React and Tailwind CSS setup, providing real-time updates, session management, and a user-friendly interface. The approach offers full control over the AI system, type safety, and consistency, with the ability to log and inspect data for debugging purposes. The post concludes by highlighting Convex's capabilities as a comprehensive backend platform for scalable full-stack AI projects.

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