---
title: MongoDB AI Community
emoji: 📚
colorFrom: green
colorTo: blue
sdk: static
pinned: false
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/692d46a01dcd4562191b1346/qwlPWphJfKXdCE5qhUbSf.png
---

# 🍃 MongoDB AI Community

Welcome to the MongoDB AI Community on Hugging Face! We're a community of developers, researchers, and AI practitioners building production-grade intelligent applications by combining MongoDB's flexible data platform with cutting-edge machine learning models from Hugging Face.

## 🎯 Our Mission

We make it easier to deploy AI models in real-world applications by bridging the gap between state-of-the-art models on Hugging Face and scalable data infrastructure with MongoDB Atlas.

## 🚀 What We Build

### Vector Search Applications
Semantic search engines, recommendation systems, and similarity-based retrieval using Hugging Face transformer models for embeddings and MongoDB Atlas Vector Search for scalable storage and retrieval.

### RAG Systems
Retrieval-augmented generation pipelines combining Hugging Face large language models with MongoDB as the knowledge base for accurate, context-aware responses.

### Multimodal Applications
Image search, audio processing, and cross-modal retrieval systems leveraging Hugging Face's diverse model ecosystem with MongoDB for data management.

### Production ML Workflows
End-to-end pipelines from data ingestion, embedding generation with Hugging Face models, to model serving and result ranking at scale with MongoDB Atlas.

## 📦 What You'll Find Here

### Models
- Fine-tuned sentence transformers optimized for specific domains
- Embedding models configured for MongoDB Atlas Vector Search
- Custom architectures for specialized use cases
- Model checkpoints with performance benchmarks

### Datasets
- Pre-processed datasets with generated embeddings
- Benchmark datasets for vector search evaluation
- Domain-specific corpora ready for MongoDB ingestion
- Training data for fine-tuning embedding models

### Spaces
- **Interactive Demos**: Try live applications powered by MongoDB and Hugging Face
- **Tutorials**: Step-by-step guides using Gradio and Streamlit
- **Benchmarks**: Performance comparisons of different embedding models
- **Tools**: Utilities for data processing, embedding generation, and deployment

### Articles
- Architecture patterns and best practices
- Performance optimization techniques
- Integration guides and tutorials
- Real-world case studies and implementations

## 🛠️ Technology Stack

We work with the full Hugging Face ecosystem and MongoDB tools:

**Hugging Face Libraries:**
- `transformers` - Pre-trained models and fine-tuning
- `sentence-transformers` - Specialized embedding models
- `datasets` - Dataset management and processing
- `tokenizers` - Fast text processing
- `accelerate` - Distributed training and inference
- `gradio` - Interactive demos and interfaces

**MongoDB Stack:**
- `pymongo` - Python MongoDB driver
- `motor` - Async Python driver
- MongoDB Atlas Vector Search - Vector similarity at scale
- MongoDB Atlas - Managed cloud database
- Change Streams - Real-time data sync

## 📚 Featured Projects

### 🎬 Mood-Based Movie Recommendation Engine
A semantic search application that matches user mood descriptions with relevant films using Voyage-4-nano embeddings and MongoDB Atlas Vector Search. Built on a dataset of 5,000+ movies with rich metadata including genres, descriptions, and user ratings.

**Key Features:**
- Natural language mood queries
- Real-time semantic matching
- Scalable vector search with MongoDB Atlas
- Interactive Gradio interface

## 🤝 Community & Contributing

We welcome contributions from developers, researchers, and ML practitioners!

### How to Contribute
- **Share Models**: Upload your fine-tuned models with benchmarks
- **Contribute Datasets**: Share pre-processed datasets with embeddings
- **Build Demos**: Create Spaces showcasing novel applications
- **Write Content**: Author tutorials, guides, and case studies
- **Join Discussions**: Help others in the Community tab
- **Report Issues**: Improve existing resources and documentation

### Community Guidelines
- Be respectful and inclusive
- Share working code and reproducible examples
- Document your work clearly
- Credit sources and collaborators
- Focus on practical, production-ready solutions

## 🔗 Connect With Us

### Hugging Face
- [Our Organization](https://huggingface.co/mongodb-community)
- [Models](https://huggingface.co/mongodb-community/models)
- [Datasets](https://huggingface.co/mongodb-community/datasets)
- [Spaces](https://huggingface.co/mongodb-community/spaces)
- [Discussions](https://huggingface.co/mongodb-community/discussions)

### MongoDB Resources
- [MongoDB Developer Hub](https://www.mongodb.com/company/blog/channel/developer-blog)
- [MongoDB Atlas](https://www.mongodb.com/atlas)
- [Vector Search Documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/)
- [Community Forums](https://www.mongodb.com/community/forums)

### Social
- Hugging Face: [@mongodb-community](https://huggingface.co/mongodb-community)
- GitHub (HF): [Hugging Face](https://github.com/huggingface)
- GitHub (MongoDB): [MongoDB](https://github.com/mongodb)
- Twitter (HF): [@huggingface](https://twitter.com/huggingface)
- Twitter (MongoDB): [@MongoDB](https://twitter.com/MongoDB)
- LinkedIn (HF): [Hugging Face](https://www.linkedin.com/company/huggingface)
- LinkedIn (MongoDB): [MongoDB](https://www.linkedin.com/company/mongodb)

## 📄 License

Unless otherwise specified, our open-source projects use permissive licenses (Apache 2.0, MIT) to encourage adoption and contribution.

---

<div align="center">

**Building the Future of AI Applications**

*Where cutting-edge models meet production-grade infrastructure* 🚀

</div>