Introduction
Welcome to the MongoDB GenAI Hackathon! Below are all the resources you will need to prepare for the hackathon.
Discord
Join our Discord channel to introduce yourself, ask questions, and meet other participants.
Tools and Technologies
MongoDB is renowned in the industry as a top-tier operational database. With MongoDB Atlas, we have unified MongoDB’s operational and vector database capabilities into a single unified platform. With Atlas, you can combine the benefits of a flexible document model and an extensive aggregation framework with vector search to build powerful AI applications for various use cases. As enterprises go from RAG to agents, MongoDB Atlas can also serve as long and short-term memory for agents.
Quickstarts- Sign-up for or login to your MongoDB Atlas account
- Click Billing at the top of your MongoDB Atlas account page.
- Enter the code provided on-site into the “Apply Code” box at the bottom of the MongoDB Atlas billing page.
- The GenAI-Showcase GitHub repo has several examples of using MongoDB Atlas with frameworks such as LangChain, and LlamaIndex as well as model providers such as OpenAI, Cohere, Hugging Face, etc.
- MongoDB Chatbot Framework is a set of libraries for building full-stack intelligent chatbot applications using MongoDB Atlas Vector Search.
- Embeddings Generator is a simple tool to add embeddings to a dataset of your choice and ingest them into MongoDB.
- Launch a Fully Managed RAG Workflow With MongoDB Atlas and Amazon Bedrock
- How to Choose the Right Embedding Model for RAG
- How to Evaluate Your RAG Application
- Building a RAG System using LlamaIndex, OpenAI, and MongoDB AtlasMongoDB Atlas Vector Search and AWS Bedrock modules RAG tutorial
- Tutorial: MongoDB Atlas Vector Search and AWS Bedrock modules RAG tutorial
- Building a PDF chatbot using Mistral AI and MongoDB Atlas
- Building an Interactive RAG Agent with MongoDB Atlas and Function Calling
- Adding Semantic Caching and Memory to your RAG Application using MongoDB Atlas and LangChain
- Adding Memory to your JavaScript RAG application using MongoDB Atlas and LangChain
AWS
- Building Generative AI Applications Modernization on AWS and MongoDB Atlas Workshop
- Building Generative AI Applications with MongoDB Atlas on AWS with Igor Alekseev
- Build RAG applications with MongoDB Atlas in Knowledge Bases for Amazon Bedrock
- MongoDB Atlas on AWS Marketplace
- AWS and Generative AI Services Deployments and RAG with Amazon Bedrock
Unstructured
- Documentation: https://docs.unstructured.io/welcome
- GitHub: https://github.com/unstructured-IO/unstructured
- Quick Start notebook
Fireworks AI:
Fireworks.ai provides a fast, reliable platform for developers to run generative artificial intelligence (AI) models at scale. Use Fireworks to provide your users with delightful, responsive experiences by delivering up to 4x lower latency than alternative solutions with zero compromise on model quality.
- Start with the model playground and API quickstart
- Getting started RAG with MongoDB
- Additional cookbooks here