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4 posts tagged with "Mongodb"

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· 6 min read
Anita Okoh

Integrating Nomic API with MongoDB using SuperDuperDB


One of the major components of building an RAG system is being able to perform a vector search or a semantic search. This potentially includes having an embedding model and a database of choice.

For this demo, we will be using Nomic’s embedding model and MongoDB in order to accomplish this

Nomic AI builds tools to enable anyone to interact with AI scale datasets and models. Nomic Atlas enables anyone to instantly visualize, structure, and derive insights from millions of unstructured data points. The text embedder, known as Nomic Embed, is the backbone of Nomic Atlas, allowing users to search and explore their data in new ways.

· 4 min read
Duncan Blythe

Despite the huge surge in popularity in building AI applications with LLMs and vector search, we haven't seen any walkthroughs boil this down to a super-simple, few-command process. With SuperDuperDB together with MongoDB Atlas, it's easier and more flexible than ever before.


We have built and deployed an AI chatbot for questioning technical documentation to showcase how efficiently and flexibly you can build end-to-end Gen-AI applications on top of MongoDB with SuperDuperDB:

Implementing a (RAG) chat application like a question-your-documents service can be a tedious and complex process. There are several steps involved in doing this:

· 4 min read
Duncan Blythe
Timo Hagenow

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.


SuperDuperDB makes it very easy to set up multimodal vector search with different file types (text, image, audio, video, and more).

Install superduperdb Python package

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

· 3 min read
Duncan Blythe

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!