Docs | Blog | Showcases | Live Jupyter Demo
🔮 SuperDuperDB is open-source: Leave a star ⭐️ to support the project!
SuperDuperDB is not another database. It is a framework that transforms your favorite database into an AI powerhouse:
- A single scalable AI deployment of all your models and AI APIs, including output computation (inference) — always up-to-date as changing data is handled automatically and immediately.
- A model trainer that allows to easily train and fine-tune models simply by querying the database.
- A feature store in which the model outputs are stored alongside the inputs in any data format.
- A fully functional vector database that allows to easily generate vector embeddings and vector indexes of the data with preferred models and APIs.
Easily implement AI without the need to copy and move your data to complex MLOps pipelines and specialized vector databases. Integrate, train, and manage your AI models and APIs directly with your chosen database, using a simple Python interface.
Unlock the power of SuperDuperDB to connect and manage various types of data sources effortlessly!
|
|
|
|
|
|
|
|
Leverage SuperDuperDB to discover insights from your data using a variety of AI models!
|
|
|
Let SuperDuperDB make your applications smarter using a suite of ready-to-use AI models!
|
|
|
Try our ready-to-use notebooks live on your browser.
- Generative AI & chatbots
- Vector Search
- Standard Use-Cases (classification, regression, clustering, recommendation etc)
- Highly custom AI use-cases and workflows with specialized models.
|
|
|
| Text-To-Image Search | Text-To-Video Search | Question the Docs |
|---|---|---|
|
|
|
| Semantic Search Engine | Classical Machine Learning | Cross-Framework Transfer Learning |
pip install superduperdb
- You need to install Docker? See the docs here.
make run_demo
In this tutorial, you will learn how to Integrate, train and manage any AI models and APIs directly with your database with your data. You can visit the docs to learn more.
Automatically compute outputs (inference) with your database in a single environment.
import pymongo
from sklearn.svm import SVC
from superduperdb import superduper
# Make your db superduper!
db = superduper(pymongo.MongoClient().my_db)
# Models client can be converted to SuperDuperDB objects with a simple wrapper.
model = superduper(SVC())
# Add the model into the database
db.add(model)
# Predict on the selected data.
model.predict(X='input_col', db=db, select=Collection(name='test_documents').find({'_fold': 'valid'}))Simply by querying your database, without additional ingestion and pre-processing:
import pymongo
from sklearn.svm import SVC
from superduperdb import superduper
# Make your db superduper!
db = superduper(pymongo.MongoClient().my_db)
# Models client can be converted to SuperDuperDB objects with a simple wrapper.
model = superduper(SVC())
# Predict on the selected data.
model.train(X='input_col', y='target_col', db=db, select=Collection(name='test_documents').find({'_fold': 'valid'}))Use your existing favorite database as a vector search database, including model management and serving.
# First a "Listener" makes sure vectors stay up-to-date
indexing_listener = Listener(model=OpenAIEmbedding(), key='text', select=collection.find())
# This "Listener" is linked with a "VectorIndex"
db.add(VectorIndex('my-index', indexing_listener=indexing_listener))
# The "VectorIndex" may be used to search data. Items to be searched against are passed
# to the registered model and vectorized. No additional app layer is required.
db.execute(collection.like({'text': 'clothing item'}, 'my-index').find({'brand': 'Nike'}))Use OpenAI, PyTorch or Hugging face model as an embedding model for vector search.
# Create a ``VectorIndex`` instance with indexing listener as OpenAIEmbedding and add it to the database.
db.add(
VectorIndex(
identifier='my-index',
indexing_listener=Listener(
model=OpenAIEmbedding(identifier='text-embedding-ada-002'),
key='abstract',
select=Collection(name='wikipedia').find(),
),
)
)
# The above also executes the embedding model (openai) with the select query on the key.
# Now we can use the vector-index to search via meaning through the wikipedia abstracts
cur = db.execute(
Collection(name='wikipedia')
.like({'abstract': 'philosophers'}, n=10, vector_index='my-index')
)model_id = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.float16,
device_map="auto",
)
model = Pipeline(
identifier='my-sentiment-analysis',
task='text-generation',
preprocess=tokenizer,
object=pipeline,
torch_dtype=torch.float16,
device_map="auto",
)
# You can easily predict on your collection documents.
model.predict(
X=Collection(name='test_documents').find(),
db=db,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200
)model.predict(
X='input_col',
db=db,
select=coll.find().featurize({'X': '<upstream-model-id>'}), # already registered upstream model-id
listen=True,
)- Join our Slack (we look forward to seeing you there).
- Search through our GitHub Discussions, or add a new question.
- Comment an existing issue or create a new one.
- Help us to improve SuperDuperDB by providing your valuable feedback here!
- Email us to
gethelp@superduperdb.com. - Feel free to contact a maintainer or community volunteer directly!
There are many ways to contribute, and they are not limited to writing code. We welcome all contributions such as:
- Bug reports
- Documentation improvements
- Enhancement suggestions
- Feature requests
- Expanding the tutorials and use case examples
Please see our Contributing Guide for details.
SuperDuperDB is open-source and intended to be a community effort, and it won't be possible without your support and enthusiasm. It is distributed under the terms of the Apache 2.0 license. Any contribution made to this project will be subject to the same provisions.
We are looking for nice people who are invested in the problem we are trying to solve to join us full-time. Find roles that we are trying to fill here!