# mongodb-developer/GenAI-Showcase

MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.

Repository: https://github.com/mongodb-developer/GenAI-Showcase
Canonical: https://ross.abutalabs.com/products/genai-showcase
Homepage: https://www.mongodb.com/cloud/atlas/register
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: agents, artificial-intelligence, generative-ai, llms, rag, atlas, jupyter-notebook, mongodb, python
Last push: 2026-08-21T16:42:58+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 67
- inputs: {"age_days": 946, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4260, forks 745 (observed 2026-08-28T04:08:40.600250+00:00)

## What it is
MongoDB's official showcase repository of Jupyter notebooks, sample apps, and workshops demonstrating Retrieval-Augmented Generation (RAG), AI agents, and industry-specific GenAI use cases. It shows how MongoDB Atlas serves as a vector database, operational store, and memory provider in GenAI pipelines.

## Use cases
- build a RAG pipeline with MongoDB Atlas vector search
- learn how to add memory to AI agents
- find example code for chatbots over my own documents
- evaluate LLM applications with notebooks
- get started with generative AI using MongoDB
- see industry-specific GenAI application examples
- run self-paced workshops on AI agents and RAG

## When to choose
- you use or plan to use MongoDB Atlas and want GenAI examples
- you want hands-on notebooks and workshops for RAG and agents
- you need reference sample apps combining vector search with LLMs

## When to avoid
- you need a production-ready framework rather than examples
- you don't want a MongoDB dependency
- you need a self-contained vector database solution

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, agent-framework, vector-database, llm-inference, machine-learning
- domain: artificial-intelligence, large-language-models, databases, tutorials
- platform: python, jvm
- tags: jupyter-notebooks, sample-applications, mongodb-atlas, generative-ai, workshops, vector-search, retrieval-augmented-generation, ai-agents, nodejs

## Member repositories
- mongodb-developer/GenAI-Showcase (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:40.600250+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:05.946541+00:00, confidence not recorded.
  - readme: https://github.com/mongodb-developer/GenAI-Showcase (fetched 2026-08-28T04:08:40.600250+00:00, sha 06e9518bdd9f)
  - homepage: https://www.mongodb.com/cloud/atlas/register (fetched 2026-08-29T09:11:39.342080+00:00, sha ab272ab542a3)
- Data as of 2026-08-30T08:39:29.467469+00:00.