In step-by-step tutorial we will show how to leverage MongoDB Atlas Vector Search with SuperDuperDB, including the generation of vector embeddings. Learn how to connect embedding APIs such as OpenAI or use embedding models for example from HuggingFace with MongoDB Atlas with simple Python commands.

Install superduperdb Python package

Using vector-search with SuperDuperDB on MongoDB requires only one simple python package install:

MongoDB now supports vector-search on Atlas enabling developers to build next-gen AI applications directly on their favourite database. SuperDuperDB now make this process painless by allowing to integrate, train and manage any AI models and APIs directly with your database with simple Python.

Build next-gen AI applications - without the need of complex MLOps pipelines and infrastructure nor data duplication and migration to specialized vector databases:

• (RAG) chat applications on documents hosted in MongoDB Atlas
• semantic-text-search & similiarity-search, using vector embeddings of your data stored in Atlas
• image similarity & image-search on images hosted in or referred to on MongoDB Atlas
• video search including search within videos for key content
• content based recommendation based on content hosted in MongoDB Atlas
• ...and much, much more!

We're happy to announce the integration of two more AI APIs, Cohere and Anthropic, into SuperDuperDB.

Cohere and Anthropic provides AI developers with sorely needed alternatives to OpenAI for key AI tasks, including:

• text-embeddings as a service
• chat-completions as as service.

Imagine effortlessly infusing AI into your data repositories—databases, data warehouses, or data lakes—without breaking a sweat. With SuperDuperDB, we aim to make this dream a reality. We want to provide everyone with the tools to build AI applications directly on top of their data stores, with just a pinch of Python magic sprinkled on top! 🐍✨

In this latest blog post we take a dive into one such example - a Retrieval Augmented Generation (RAG) app we built directly on top of our MongoDB store.

In this blog-post we show you how to easily operate vector-search in MongoDB Atlas using SuperDuperDB, leading to many savings and efficiencies in your AI development.

In 2023 vector-databases are hugely popular; they provide the opportunity for developers to connect LLMs, such as OpenAI’s GPT models, with their data, as well as providing the key to deploying “search-by-meaning” on troves of documents.

It's 2023, and unless you've been in cryo-sleep since mid-2022, you'll have heard about the explosion of powerful AI and LLMs. Leveraging these LLMs, developers are now able to connect existing data with AI using vector search.