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!
Using SuperDuperDB to get started with Atlas vector-search
There is great content on the MongoDB website on how to get started with vector-search on Atlas. You'll see that there are several steps involved:
- Preparing documents for vector-search
- Converting text into vectors with an AI "model" and storing these vectors in MongoDB
- Setting up a vector-search index on Atlas vector-search
- Preparing a production API endpoint to convert searches in real time to vectors
Each of these steps contains several sub-steps, and can become quite a headache for developers wanting to get started with vector-search.
With SuperDuperDB, this preparation process can be boiled down to one simple command:
from superduperdb.ext.openai.model import OpenAIEmbedding
from superduperdb.container.vector_index import VectorIndex
from superduperdb.container.listener import Listener
from superduperdb.db.mongodb.query import Collection
db.add(
VectorIndex(
identifier='my-index',
indexing_listener=Listener(
model=OpenAIEmbedding(model='text-embedding-ada-002'),
key='key', # path of documents
select=Collection('documents').find(),
predict_kwargs={'max_chunk_size': 1000},
),
)
)
Under the hood SuperDuperDB does these things:
- Sets up an Atlas vector-search index in the
"documents"collection - Converts all documents into vectors
- Creates a function allow users to directly search using vectors, without needing to handle the conversion to vectors themselves:
Collection('documents').like({'key': 'This is the text to search with'}).find(). This function can easily be served using, for example, FastAPI. (See here for an example.)
Take AI even further with SuperDuperDB on MongoDB
AI is not just vector-search over text-documents -- there are countless additional ways in which AI can be leveraged with data. This is where SuperDuperDB excels and other solutions come up short in leveraging data in MongoDB.
SuperDuperDB also allows developers to:
- Search the content of images, videos and voice memos in MongoDB
- Create talk-to-your documents style chat applications.
- Use classical machine learning models together with state-of-the-art computer vision models.
Useful Links
Contributors are welcome!
SuperDuperDB is open-source and permissively licensed under the Apache 2.0 license. We would like to encourage developers interested in open-source development to contribute in our discussion forums, issue boards and by making their own pull requests. We'll see you on GitHub!
Become a Design Partner!
We are looking for visionary organizations which we can help to identify and implement transformative AI applications for their business and products. We're offering this absolutely for free. If you would like to learn more about this opportunity please reach out to us via email: partnerships@superduperdb.com