This repository contains Jupyter Notebooks that follow tutorials and code examples in MongoDB's official Atlas Vector Search documentation. You can run, download, and modify these notebooks as you learn how to use MongoDB Atlas Vector Search for your use case.
Each notebook corresponds to a page or example in our documentation. Refer to the docs page linked in each notebook for prerequisites, set-up instructions, and detailed explanations of the code.
The following table summarizes the contents of the notebooks in each directory:
| Directory | Description |
|---|---|
| /create-embeddings | Learn how to generate embeddings for vector search |
| /get-started | Complete our quick start tutorial |
| /integrations | Implement RAG with popular frameworks that integrate with MongoDB |
| /manage-indexes | Create, view, edit, and delete vector search indexes |
| /quantization | Quantize your vector embeddings for efficient processing |
| /run-queries | Learn how to run vector search queries (ANN and ENN) |
| /use-cases | Implement RAG using a MongoDB-native retrieval system |
This project is licensed under the Apache 2.0 License.
To report an issue with any of these notebooks, please leave feedback through
the corresponding documentation page linked at the top of the file. Using the
Rate This Page button, you can add a comment about the issue after leaving
a star rating.
We are not currently accepting public contributions to this repository at this time.