Welcome to the Generative AI Use Cases Repository! This repository is dedicated to showcasing a wide range of applications in generative AI, including Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. It also provides practical notebooks and guidance on utilizing frameworks such as LlamaIndex and LangChain, and demonstrates how to integrate models from leading AI research companies like Anthropic and OpenAI.
- Introduction
- Getting Started
- Use Cases
- Notebooks
- Tools
- Frameworks and Models
- Contributing
- License
- Contact
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 | View Repository | The MongoDB Chatbot Framework provides libraries that enable the creation of sophisticated chatbot |
- Evaluating your LLM applications
- Angle Embeddings Evaluation
- OpenAI Embeddings Evaluation
- VoyageAI Embeddings Evaluation
| Title | Stack | Colab | Article |
|---|---|---|---|
| RAG with Llama3, Hugging Face and MongoDB | Hugging Face, Llama3, MongoDB |  |
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| How to Build a RAG System Using Claude 3 Opus and MongoDB | MongoDB, Anthropic, Python | |
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| How to Build a RAG System with the POLM AI Stack | POLM (Python, OpenAI, LlamaIndex, MongoDB) | |
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| MongoDB LangChain Cache Memory Python Example | POLM (Python, OpenAI, LangChain, MongoDB) | |
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| MongoDB LangChain Cache Memory JavaScript Example | JavaScript, OpenAI, LangChain, MongoDB | |
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| Naive RAG Implementation Example | POLM (Python, OpenAI, LlamaIndex, MongoDB) | |
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| OpenAI Text Embedding Example | Python, MongoDB, OpenAI | |
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| RAG with Hugging Face and MongoDB Example | Hugging Face, Gemma, MongoDB | |
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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 |
|---|---|---|---|
| AI Research Assistant | FireWorks AI, MongoDB, LangChain | |
Useful tools and utilities for working with generative AI models:
- Embeddings Generator: A set of scripts for generating and manipulating embeddings.
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 |  |
| Movies | Western, Action, and Fantasy movies, including title, release year, cast, and OpenAI embeddings for vector search. | |
| Airbnb | AirBnB listings dataset with property descriptions, reviews, metadata and embeddings. | |
| Tech News | Tech news articles from 2022 and 2023 on valuable tech companies. | |
| Restaurant | Restaurant dataset with location, cuisine, ratings, attributes for industry analysis, recommendations, geographical studies. | |
Explore the utilization of various AI models and frameworks across different notebooks provided.
We welcome contributions! Please read our Contribution Guidelines for more information on how to participate.
This project is licensed under the MIT License.
Feel free to reach out for any queries or suggestions:
- Email: richmond.alake@mongodb.com