To construct native AI applications, such as a realistic, end-stage optimized chatbot, you require significantly more than just a vector store. The knowledge chunks within should support transactional operations, managed through a CRUD application interface layered above. Here are some resources and examples to help you along the way.
This repository contains a collection of Jupyter Notebooks designed to demonstrate various MongoDB functionalities, focusing on Atlas Search, VectorSearch, RAG (Retrieval-Augmented Generation), Langchain, and basic MongoDB CRUD operations. These notebooks are intended to provide a practical, hands-on approach to learning and implementing MongoDB's powerful features in your projects.
- Atlas Search: Notebooks covering the integration and utilization of MongoDB Atlas's full-text search capabilities.
- VectorSearch: Demonstrations of vector search functionalities within MongoDB, enabling semantic search capabilities.
- RAG (Retrieval-Augmented Generation): Examples of leveraging retrieval-augmented generation for enhancing language model predictions with database searches.
- Langchain: Tutorials on using the Langchain library for building language applications with MongoDB.
- Basic MongoDB CRUD: Notebooks that go through the basics of creating, reading, updating, and deleting documents in MongoDB.
To get started with these notebooks, clone this repository to your local machine or Jupyter environment:
git clone https://github.com/voxic/MongoDB_Notebooks
Ensure you have MongoDB set up in your environment, and if you're working with Atlas, ensure your cluster is configured correctly.
- Jupyter or JupyterLab
- MongoDB server or MongoDB Atlas account
- Python 3.6 or newer
Install the required Python libraries using pip:
pip install -r requirements.txt
python -m jupyter notebook
- MongoDB AI Resource Hub
- MongoDB AI Cookbook
- MongoDB Aggregation framework Ebook
- MongoDB Solutions Library
- MongoDB Chatbot Framework
- RAG to Riches Developer Quest
- External Brain - Live Chatbot example
Contributions to this collection are welcome! Please feel free to fork the repository, add your notebooks, and submit a pull request.
This collection is distributed under the MIT License. See LICENSE for more information.
- MongoDB, Inc. for providing the powerful database platform and the Atlas Search feature.
- The developers of Langchain and the OpenAI API for enabling advanced language model integrations.