mongodb-ai-playground

MongoDB AI Playground

MongoDB AI Playground is a set of interactive widgets to explore, test, and visualize MongoDB-powered AI capabilities, including Retrieval-Augmented Generation (RAG) and GraphRAG workflows, using modern LLM and vector search integrations.

Overview

The MongoDB AI Playground provides interactive widgets for experimenting with advanced AI capabilities on MongoDB, including:

• RAG (Retrieval-Augmented Generation): Chunk, embed, store, and query documents using MongoDB Atlas Vector Search and LLMs.

Built as a set of AnyWidget Jupyter widgets, this playground is designed for rapid prototyping, learning, and demonstration of GenAI + MongoDB workflows.

Installation

• Pre-requisites

• An Atlas account with a cluster running MongoDB version 6.0.11, 7.0.2, or later (including release candidates). Ensure your IP address is included in your Atlas project's access list. To learn more, see Create a Cluster.
• An environment to run interactive Python notebooks such as JupyterLab, Jupyter Notebook, Google Colab and VSCode. Make sure you have widget support enabled.
• Create a database and collection (:warning: if you choose to use an existing collection, usage with the playground will erase the collection's data. Providing a new collection is recommended)
• Create an Atlas Vector Search index with the correct dimension associated with the embedding model you will use. The field containing the embedding must be named embedding.

• Install dependencies:

pip install mongodb-ai-playground

The mongodb-ai-playground depends on the following Python libraries: anywidget, ipywidgets, langchain, langchain-mongodb, and pymongo.

Full examples

Usage

RAG Playground

from mongodb_ai_playground import MongoDBRAGPlayground

# Example: Pass your own loader, embedding model, LLM, and MongoDB collection
widget = MongoDBRAGPlayground(
    loader=...,              # LangChain loader
    embedding_model=...,     # LangChain embedding model 
    llm=...,                 # LangChain Chat Model (LLM) for answering questions (OpenAI, Claude, DeepSeek, etc.)
    mongo_collection=...,    # PyMongo collection for storing vectors
    index_name=...           # Name of your Atlas Vector Search index, you need to create if you don't have one, with the correct dimension (field containing the embedding is 'embedding')
)
widget # Display the playground widget

• All interactions are performed via the interactive UI in Jupyter-compatible environments.
• Visualize chunking, embeddings, vector search results, and knowledge graphs.

Features

• 📄 Document Chunking: Flexible strategies (fixed, recursive, markdown) for splitting documents.
• 🧠 Embeddings & Vector Search: Store and search embeddings in MongoDB Atlas using langchain-mongodb.
• 🔎 RAG Playground UI: Step-by-step interface for chunking, embedding, and querying.
• 🕸️ Knowledge Graph RAG: Build and visualize entity/relation graphs from docs and run graph-based QA.
• 🧩 Extensible: Built for experimentation with LangChain so that you can use different loaders, embeddings models, LLMs and more.

Project Structure

mongodb_ai_playground/
├── rag_widget.py         # RAG playground widget (chunking, embedding, RAG)
├── graphrag_widget.py    # Graph RAG playground widget (graph ingest, QA)
├── index.js              # JS frontend for RAG widget
├── graphrag.js           # JS frontend for Graph RAG widget
├── index.css             # Shared widget styles
├── __init__.py           # Exports widgets
...

Requirements

• Python 3.7+
• Jupyter widget compatible environments (JupyterLab, Jupyter Notebook, Colab, Marimo, etc.)
• MongoDB Atlas deployment (for vector/graph storage)
• Any LLM and embedding model using LangChain components (OpenAI, HuggingFace, etc.)

License

This project is licensed under the MIT License.

Acknowledgments

• AnyWidget
• LangChain
• langchain-mongodb
• PyMongo
• MongoDB Atlas

Additional Resources

• GenAI Showcase
• AI Learning Hub
• GenAI Community Forum
• Tutorials and code examples from our official docs