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📣 On May 1st we will release v0.2 including proper versioning of the docs (the docs are currently outdated). Find all major updates and fixes here in the Changelog!

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What is SuperDuperDB? 🔮

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning.

Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source:

• Generative AI, LLMs, RAG, vector search
• Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.)
• Custom AI use-cases involving specialized models
• Even the most complex applications/workflows in which different models work together

SuperDuperDB is not a database. Think db = superduper(db): SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

Key Features:

• 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 Python Interface: Replace writing thousand of lines of glue code with simple Python commands, while being able to drill down to any layer of implementation detail, like the inner workings of your models or your training details.
• Python-First: Bring and leverage any function, program, script or algorithm from the Python ecosystem to enhance your workflows and applications.
• Difficult Data-Types: Work directly with images, video, audio in your database, and any type which can be encoded as bytes in 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.

Example use-cases and apps (notebooks)

The notebooks below are examples how to make use of different frameworks, model providers, vector databases, retrieval techniques and so on.

To learn more about how to use SuperDuperDB with your database, please check our Docs and official Tutorials.

Also find use-cases and apps built by the community in the superduper-community-apps repository.

Name	Link
Use ChatGPT to chat with Snowflake
Streaming Inference using Mnist and MongoDB
Multimodal Vector Search with your SQL database
Connecting text and images using CLIP model
Question your docs using ChatGTP
Question your docs using Vllm
High-throughput Embeddings using Dask and MiniLM model
Transfer Learning between Transformers and Scikit
Declarative Model Chaining
Search your videos using CLIP model
Voice Assistant using LibriSpeech and Chat-Completion

Why opt for SuperDuperDB?

	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 environments and tools.

For more information about SuperDuperDB and why we believe it is much needed, read this blog post.

Supported Datastores (more coming soon):

Transform your existing database into a Python-only AI development and deployment stack with one command:

db = superduper('mongodb|postgres|mysql|sqlite|duckdb|snowflake://<your-db-uri>')

Supported AI Frameworks and Models (more coming soon):

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>)

Pre-Integrated AI APIs (more coming soon):

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>|JinaAI<Task>(*args, **kwargs),   # <Task> - Embedding,ChatCompletion,...
)

Infrastructure Diagram

Installation

# Option 1. SuperDuperDB Library

Ideal for building new AI applications.

pip install superduperdb

# Option 2. SuperDuperDB Container

Ideal for learning basic SuperDuperDB functionalities and testing notebooks.

docker pull superduperdb/superduperdb
docker run -p 8888:8888 superduperdb/superduperdb

# Option 3. SuperDuperDB Testenv

Ideal for learning advanced SuperDuperDB functionalities and testing whole AI stacks.

make testenv_image
make testenv_init

Preview

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.

- Deploy ML/AI models to your database:

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'}))

- Train models directly from your database.

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())

# Fit model on the training data.
model.fit(X='input_col', y='target_col', db=db, select=Collection(name='test_documents').find({}))

- Vector-Search your data:

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'}))

- Integrate AI APIs to work together with other models.

Use OpenAI, Jina AI, 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')
)

- Add a Llama 2 model to SuperDuperDB!:

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
)

- Use models outputs as inputs to downstream models:

model.predict(
    X='input_col',
    db=db,
    select=coll.find().featurize({'X': '<upstream-model-id>'}),  # already registered upstream model-id
    listen=True,
)

Community & Getting Help

If you have any problems, questions, comments, or ideas:

• 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!

Contributing

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.

Contributors

Thanks goes to these wonderful people:

License

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.

Join Us

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!