Openai

If you are exposing AI-enabled capabilities in your product and supporting external integrations, there is a good chance you will implement an MCP (Model Context Protocol) server to handle tool calls from LLMs. When you do, you will need to manage authentication, input validation, multi-tenant isolation, and more. Instead of starting from scratch, I have put together a starter-kit that gives you all this out of the box: JWT-based tenant authentication, input validation, per-function metadata, cloud-native & container-ready with Docker, and standard endpoints as per the MCP spec.

RAG is one of the most common use cases that has been implemented in the past couple of years. Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by combining them with external knowledge sources. It involves retrieving relevant information from a knowledge base, incorporating it into the LLM's context, and then generating a response that leverages both the LLM's internal knowledge and the retrieved information. Building RAG applications requires integrating various components like vector databases and search algorithms, which can be quite involved. In this blog we'll briefly talk about RAG basics and leveraging OpenAI's assistants to build simple RAG applications.