MongoDB's Generative AI Showcase is a collection of example applications and tutorials demonstrating how to build generative AI systems with MongoDB.
The showcase addresses the practical challenge of integrating MongoDB with large language models and AI frameworks. It provides working examples across three main patterns: Retrieval-Augmented Generation (RAG) systems that ground LLM responses in stored data, AI agents that can take actions and reason over information, and domain-specific applications built for particular industries. By offering concrete, runnable code samples rather than abstract documentation, the project helps developers understand how to structure MongoDB queries, vector storage, and LLM interactions together.
Developers building generative AI applications with MongoDB should use this showcase to learn implementation patterns and accelerate their own projects. The collection is particularly suited for teams evaluating how to add AI capabilities to existing MongoDB deployments or designing new systems that combine document storage with LLM reasoning. The examples span multiple use cases, so developers can find samples closest to their problem domain rather than starting from scratch.
The project maintains a steady stream of new examples and applications added to the collection. Documentation and code samples receive regular updates to reflect changes in MongoDB's APIs and generative AI frameworks. The showcase remains actively curated with fresh material demonstrating emerging patterns in the generative AI space.
