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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. Aa single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.
- 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.
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.
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.
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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>')
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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>)
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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,...
)
Ideal for building new AI applications.
pip install superduperdb
Ideal for learning basic SuperDuperDB functionalities and testing notebooks.
docker pull superduperdb/superduperdb
docker run -p 8888:8888 superduperdb/superduperdb
Ideal for learning advanced SuperDuperDB functionalities and testing whole AI stacks.
make testenv_image
make testenv_init
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())
# Fit model on the training data.
model.fit(X='input_col', y='target_col', db=db, select=Collection(name='test_documents').find({}))
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, 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')
)
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!