Docs | Blog | Use-Cases | Live Notebooks | Community Apps | Slack | Youtube
- Support of major SQL databases: PostgreSQL, MySQL, SQLite, DuckDB, Snowflake, BigQuery, ClickHouse, DataFusion, Druid, Impala, MSSQL, Oracle, pandas, Polars, PySpark, and Trino (next to MongoDB of course)
- Improved docs
- A modularized testing suite
SuperDuperDB is open-source. Please support us:
- Leave a star on the repo! ⭐
- Upvote on ProductHunt! 🗳️
- Help us spread the word and win swag by reposting this post on LinkedIn, and tag anyone in the comments who could be interested in SuperDuperDB 📣
To get started:
- Check the uses cases that we have already implemented here in the docs as well as apps built by the community in the dedicated superduper-community-apps repo and try all of them with Jupyter right in your browser!
For more information about SuperDuperDB and why we believe it is much needed, read this blog post.
SuperDuperDB is an open-source framework for integrating AI directly with your existing databases, including streaming inference, scalable model training, and vector search.
SuperDuperDB is not a database. It transforms your favorite database into an AI development and deployment environment (making it super-duper).
SuperDuperDB eliminates complex MLOps pipelines, specialized vector databases - and the need to migrate and duplicate data by integrating AI at the data's source, directly on top of your existing data infrastructure. This massively simplifies building and managing AI applications:
- Generative AI & LLM-Chat
- Vector Search
- Standard Machine Learning Use-Cases (Classification, Segmentation, Recommendation etc.)
- Highly custom AI use-cases involving ultra specialized models
- Integration of AI with your existing data infrastructure: Integrate any AI models and APIs with your databases in a single scalable deployment, without the need for additional pre-processing steps, ETL or boilerplate code.
- Streaming Inference: Have your models compute outputs automatically and immediately as new data arrives, keeping your deployment always up-to-date.
- Scalable Model Training: Train AI models on large, diverse datasets simply by querying your training data. Ensured optimal performance via in-build computational optimizations.
- Model Chaining: Easily setup complex workflows by connecting models and APIs to work together in an interdependent and sequential manner.
- Simple, but Extendable Interface: Add and leverage any function, program, script or algorithm from the Python ecosystem to enhance your workflows and applications. Drill down to any layer of implementation, including to the inner workings of your models while operating SuperDuperDB with simple Python commands.
- Difficult Data-Types: Work directly with images, video, audio in your database, and any type which can be encoded as
bytesin Python. - Feature Storing: Turn your database into a centralized repository for storing and managing inputs and outputs of AI models of arbitrary data-types, making them available in a structured format and known environment.
- Vector Search: No need to duplicate and migrate your data to additional specialized vector databases - turn your existing battle-tested database into a fully-fledged multi-modal vector-search database, including easy generation of vector embeddings and vector indexes of your data with preferred models and APIs.
| With SuperDuperDB | Without | |
|---|---|---|
| Data Management & Security | Data stays in the database, with AI outputs stored alongside inputs available to downstream applications. Data access and security to be externally controlled via database access management. | Data duplication and migration to different environments, and specialized vector databases, imposing data management overhead. |
| Infrastructure | A single environment to build, ship, and manage your AI applications, facilitating scalability and optimal compute efficiency. | Complex fragmented infrastructure, with multiple pipelines, coming with high adoption and maintenance costs and increasing security risks. |
| Code | Minimal learning curve due to a simple and declarative API, requiring simple Python commands. | Hundreds of lines of codes and settings in different environemts and tools. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Transform your existing database into a Python-only AI development and deployment stack with one command:
db = superduper('mongodb|postgres|sqlite|duckdb|snowflake://<your-db-uri>')
|
|
|
Integrate, train and manage any AI model (whether from open-source, commercial models or self-developed) directly with your datastore to automatically compute outputs with a single Python command:
- Install and deploy model:
m = db.add(
<sklearn_model>|<torch_module>|<transformers_pipeline>|<arbitrary_callable>,
preprocess=<your_preprocess_callable>,
postprocess=<your_postprocess_callable>,
encoder=<your_datatype>
)
- Predict:
m.predict(X='<input_column>', db=db, select=<mongodb_query>, listen=False|True, create_vector_index=False|True)
- Train model:
m.fit(X='<input_column_or_key>', y='<target_column_or_key>', db=db, select=<mongodb_query>|<ibis_query>)
|
|
|
Integrate externally hosted models accessible via API to work together with your other models with a simple Python command:
m = db.add(
OpenAI<Task>|Cohere<Task>|Anthropic<Task>(*args, **kwargs), # <Task> - Embedding,ChatCompletion,...
)
Try our ready-to-use notebooks live on your browser.
Also find use-cases and apps built by the community in the superduper-community-apps repository.
|
|
|
| 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.
docker run -p 8888:8888 superduperdb/demo:latest
Here are snippets which give you a sense of how superduperdb works and how simple it is to use. 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 at
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 wouldn'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!