GenAI-Showcase is MongoDB's repository with examples and notebooks for RAG, AI agents, and use cases, integrating MongoDB as vector DB, memory, and operational store. It includes notebooks, apps, workshops, and partner contributions.
Collecting history — the radar snapshots this repo daily. The trend line appears after 3 days of data (1 so far).
What it is
MongoDB's GenAI Showcase repository provides examples and sample applications focused on Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific GenAI use cases. It describes integrating MongoDB into RAG pipelines as a vector database, operational database, and memory provider. The repository contains folders for notebooks, apps, workshops, and partners.
How it works
The README indicates that MongoDB can function as a vector database, an operational database, and memory provider within GenAI workflows. The repo includes Jupyter notebooks for RAG and agentic applications, as well as JavaScript and Python apps/demos in the apps folder. Contributing guidance exists via a CONTRIBUTING.md, and support workflows encourage opening issues for problems.
Getting started
To run the examples, you need to connect to a MongoDB cluster. Steps provided:
- Register for a free MongoDB Atlas account
- Create a new database cluster
- Obtain the connection string for your database cluster These steps imply configuring a MongoDB Atlas connection string to use the notebooks and apps.
Recent releases
No releases are listed (latest 0). The repository shows no named releases in the provided data.
Traction
- stars: 4258
- forks: 743
- open_issues: 16 (The repository metrics are provided as raw counts; no trend data is included in the snippet.)
License
Licensed under the MIT License.
Getting support
If problems arise, open a new issue on the repository.
Additional resources
Links to AI Learning Hub, GenAI Community Forum, and tutorials/code examples from MongoDB docs are provided for further reference.