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GenAI Showcase

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Basic Information

MongoDB's GenAI Showcase is a curated collection of examples, sample applications, and learning materials intended to help developers explore and build generative AI solutions that integrate with MongoDB. The repository focuses on practical patterns such as Retrieval-Augmented Generation and AI Agents and highlights how MongoDB can be used as a vector database, an operational database, and a memory provider in GenAI pipelines. It aggregates Jupyter notebooks, ready-to-run JavaScript and Python apps, self-paced workshops, and partner-contributed examples so users can experiment with agentic applications and evaluation scenarios. The README also explains the basic setup requirement of connecting to a MongoDB Atlas cluster and points to contribution guidelines and license information for anyone who wants to reuse or extend the examples.

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App URL
https://github.com/mongodb-developer/GenAI-Showcase
Github Repository
https://github.com/mongodb-developer/GenAI-Showcase/blob/main/README.md

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App Details

Features
Organized content folders including notebooks for Jupyter examples covering RAG, agentic applications, and evaluations. A set of apps and demos implemented in JavaScript and Python that showcase end-to-end patterns and integration with MongoDB. Self-paced workshops designed for hands-on learning and step-by-step exercises. A partners directory containing contributed examples from third-party collaborators. Clear getting-started instructions that note the need for a MongoDB Atlas cluster and a connection string to run examples. Contribution guidelines and an MIT license are provided to encourage community participation and reuse. Support is directed through the repository issue tracker for troubleshooting and feedback.
Use Cases
This repository serves as a practical learning and prototyping resource for developers and teams building generative AI applications that rely on persistent storage and retrieval. It helps users understand concrete ways to use MongoDB as a vector store and long-term memory, and it supplies runnable notebooks and app demos to accelerate experimentation with RAG pipelines and agentic workflows. The workshops provide structured, hands-on exercises for skill development while partner examples illustrate industry-specific use cases. By offering contribution guidance, a permissive license, and an issues channel for support, the project lowers the barrier to adopt MongoDB in GenAI projects and to iterate on real implementations.

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