MongoDB + SuperAGI

[ranfysvalle02]

Description

MongoDB has been ranked as the best vector database(https://www.mongodb.com/blog/post/atlas-vector-search-commands-highest-developer-nps-retool-state-ai-2023-survey) in the Retool AI report, so it is quite important to add MongoDB vector search as an option for Autogen RAG.

You can easily start the MongoDB vector search on a free tier M0 MongoDB Atlas cluster. Free tier cluster provides the full functionality of the MongoDB vector search. https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/
But why is MongoDB such a standout? Well, there are a few key reasons.

1. MongoDB Atlas integrates smoothly with existing databases. For organizations already using MongoDB, this means a seamless expansion into the vector storage—no major system overhauls required!
2. MongoDB Atlas is built to handle operational heavy-lifting. It excels when serving large-scale, mission-critical applications, offering robustness and reliability where it counts.
3. MongoDB's flexibility in handling a variety of data types and structures makes it perfectly suited to the complexity of vector embeddings.

Related Issues

Solution and Design

Test Plan

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Docs update

Checklist

• My pull request is atomic and focuses on a single change.
• I have read the contributing guide and my code conforms to the guidelines.
• I have documented my changes clearly and comprehensively.
• I have added the required tests.

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