Build a RAG App With Descope, Supabase & pgvector: Part 1

Kevin Kimani's tutorial outlines the process of building a retrieval-augmented generation (RAG) application, focusing on integrating Supabase as a backend and pgvector for managing embeddings. The tutorial aims to enhance AI-generated responses by incorporating external knowledge and ensuring robust security. In the first part, readers set up Supabase, configure pgvector for storing and querying embeddings, and learn to preprocess data from the Descope website. The application distinguishes between developer and marketer roles, facilitating targeted queries for product information and documentation. By generating embeddings for user queries and performing similarity searches, the RAG app retrieves relevant documents, which are then used to enhance responses generated by the OpenAI API. The upcoming second part promises to cover integrating Descope for authentication and implementing granular permissions using Supabase Row-Level Security.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.