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Generative AI Use Cases Repository

Introduction

Generative AI Use Cases Repository Welcome to the Generative AI Use Cases Repository! This comprehensive resource showcases cutting-edge applications in generative AI, including Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. Discover how MongoDB integrates with RAG pipelines and AI Agents, serving as a vector database, operational database, and memory provider.

Key Features:

  • RAG pipelines and applications leveraging MongoDB for efficient data retrieval and management
  • AI Agents utilizing MongoDB as a scalable memory provider
  • Practical notebooks and guidance on frameworks like LlamaIndex, Haystack and LangChain
  • Integration with state-of-the-art models from Anthropic and OpenAI
  • Industry-specific use cases across healthcare, finance, e-commerce, and more

Table of Contents

Use Cases

This section contains examples of use cases that are commonly seen in industry-focused scenarios and generic applications. Each entry in the table includes a description and links to production-level examples and relevant code.

Use Case Stack Link Description
Customer Support Chatbot JavaScript, OpenAI, MongoDB GitHub The MongoDB Chatbot Framework provides libraries that enable the creation of sophisticated chatbot
HR Support Chatbot LangGraph.JS, Anthropic, OpenAI, MongoDB GitHub Create an AI-powered HR assistant using LangGraph.js and MongoDB
Trip Advisor - Laravel, OpenAI and Atlas PHP (Laravel), OpenAI, MongoDB GitHub Leverage PHP, Laravel and OpenAI to build suphisticated recommendation engines
MongoDB AI Framework Key AI Stack components GitHub The MAAP framework is a set of libraries that you can use to build your RAG Application using MongoDB and Atlas Vector Search and associated MAAP partners
MongoDB and BuildShip Agents MongoDB Atlas, BuildShip low-code, Anthropic GitHub This guide demonstrates how to create an AI agent for handling rental booking workflows using BuildShip's no-code platform with MongoDB Aggregation and Insert integrations.
Realtime Voice OpenAI API and Atlas Vector Search Rental Agent OpenAI, Atlas , Python , React GitHub Sample application that use OpenAI Realtime API and MongoDB Atlas vector/CRUD as tools for a full voice based rental assistant.

Evaluations

RAG

Title Stack Colab Article
RAG with Llama3, Hugging Face and MongoDB Hugging Face, Llama3, MongoDB Open In Colab
How to Build a RAG System Using Claude 3 Opus and MongoDB MongoDB, Anthropic, Python Open In Colab View Article
How to Build a RAG System with the POLM AI Stack POLM (Python, OpenAI, LlamaIndex, MongoDB) Open In Colab View Article
MongoDB LangChain Cache Memory Python Example POLM (Python, OpenAI, LangChain, MongoDB) Open In Colab View Article
MongoDB LangChain Cache Memory JavaScript Example JavaScript, OpenAI, LangChain, MongoDB Open In Colab View Article
Naive RAG Implementation Example POLM (Python, OpenAI, LlamaIndex, MongoDB) Open In Colab View Article
OpenAI Text Embedding Example Python, MongoDB, OpenAI Open In Colab View Article
RAG with Hugging Face and MongoDB Example Hugging Face, Gemma, MongoDB Open In Colab View Article
Chat With PDF Example Python, MongoDB, OpenAI, LangChain Open In Colab
RAG Pipeline Python, MongoDB, Gemma2, KeraNLP Open In Colab
RAG Pipeline with Open Models Python, MongoDB, Gemma2, Hugging Face Open In Colab
MongoDB and Haystack cooking advisor Python, Haystack , OpenAI Open In Colab View Article
MongoDB and Semantic Kernel Movie Recommendation Bot C# Console App, MongoDB, Semantic Kernel, Azure OpenAI or OpenAI GitHub Repo View Article
Build an Asset Manager RAG Chatbot Cohere, MongoDB, Python Open In Colab Coming soon
Asset Manager Chatbot with LLM Evals and Moderation Gemma 2B, ShieldGemma, MongoDB, Python Open In Colab View Article
Lyric Semantic Search with MongoDB and Spring AI Java, Spring AI, OpenAI, MongoDB Github Repo View Article
Terraforming AI Workflows: RAG With MongoDB Atlas and Spring AI Java, Spring AI, OpenAI, MongoDB, Terraform Github Repo View Article

Agents

An agent is an artificial computational entity with an awareness of its environment. It is equipped with faculties that enable perception through input, action through tool use, and cognitive abilities through foundation models backed by long-term and short-term memory. Within AI, agents are artificial entities that can make intelligent decisions followed by actions based on environmental perception, enabled by large language models.

Title Stack Colab Link Article Link
Agentic Factory Safety Assistant LangGraph, Open AI, MongoDB, LangChain Open In Colab
AI Research Assistant FireWorks AI, MongoDB, LangChain Open In Colab View Article
AI Investment Researcher MongoDB, CrewAI and LangChain Open In Colab View Article
Agentic RAG: Recommmendation System Claude 3.5, LlamaIndex, MongoDB Open In Colab View Article
Agentic HR Chatbot Claude 3.5, LangGraph, MongoDB Open In Colab Coming Soon
AWS Bedrock Agent Claude 3, AWS Bedrock, Python, MongoDB Open In Colab View Article
Asset Manager Assistant LangGraph, OpenAI, Anthropic, MongoDB Open In Colab
Implementing Working Memory with Tavily and MongoDB Python, Tavily, MongoDB Open In Colab
AI Food Assistant Semantic Kernel, C#, OpenAI, MongoDB GitHub Repo View Article

ML

This folder will contain all traditional machine learning tutorials. They include important explanations, step-by-step instructions, and everything a reader needs in order to be successful following the tutorial from beginning to end.

Title Colab Link
Written in the Stars: Predict Your Future With Tensorflow and MongoDB Charts Open In Colab

MongoDB Specific

These MongoDB specific tutorials are meant to showcase a specific MongoDB platform integrated with artificial intelligence or machine learning. These step-by-step tutorials will allow the reader to truly understand not only the platform, but also the AI use-case.

Title Colab Link
Aperol Spritz Summer With MongoDB Geospatial Queries & Vector Search Open In Colab
Sip, Swig, and Search With Playwright, OpenAI, and MongoDB Atlas Search Open In Colab
Ingesting Quantized vectors with Cohere and MongoDB Open In Colab
Evaluating quantized vectors vs Non-Quantized Vectors with MongoDB Open In Colab

Workshops

Workshops are designed to take learners through the step-by-step process of developing LLM applications. These workshops include essential explanations, definitions, and resources provided within the notebooks and projects. Each workshop is structured to build foundational knowledge and progressively advance to more complex topics. Practical exercises and real-world examples ensure that learners can apply the concepts effectively, making it easier to understand the integration and deployment of generative AI applications.

Title Colab Link
Pragmatic LLM Application Development: From RAG Pipelines to AI Agent Open In Colab
Building chatbots with NextJS and Atlas Vector search View Article

Tools

Useful tools and utilities for working with generative AI models:

Datasets

Below are various datasets with embeddings for use in LLM application POCs and demos. All datasets can be accessed and downloaded from their respective Hugging Face pages.

Dataset Name Description Link
Cosmopedia Chunked version of a subset of the data Cosmopedia dataset View Dataset
Movies Western, Action, and Fantasy movies, including title, release year, cast, and OpenAI embeddings for vector search. View Dataset
Airbnb AirBnB listings dataset with property descriptions, reviews, metadata and embeddings. View Dataset
Tech News Tech news articles from 2022 and 2023 on valuable tech companies. View Dataset
Restaurant Restaurant dataset with location, cuisine, ratings, attributes for industry analysis, recommendations, and geographical studies. View Dataset
Subset Arxiv papers This arXiv subset has 256-dimensional OpenAI embeddings for each entry, created by combining title, author(s), and abstract. View Dataset

General Knowledge

Thought leadership in AI is not an option, we take it seriously. That's why we've curated articles and pieces created by our team to get you conversation-ready and equipped with the right information to make key decisions when building AI products.

Title Link
What is an AI Stack? View Article
How to Optimize LLM Applications With Prompt Compression Using LLMLingua and LangChain View Article
What is Atlas Vector Search View Article
How to Choose the Right Chunking Strategy for Your LLM Application View Article
How to Choose the Right Embedding Model for Your LLM Application View Article
How to Evaluate Your LLM Application View Article

Contributing

We welcome contributions! Please read our Contribution Guidelines for more information on how to participate.

License

This project is licensed under the MIT License.

Contact

Feel free to reach out for any queries or suggestions: