Databases
Superduper (formerly SuperDuperDB) is a Python framework for integrating AI models and workflows with major databases. Implement custom AI solutions without moving your data through complex pipelines and specialized vector databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning.
Transform your existing database into an AI development and deployment stack with one command, streamlining your AI workflows in one environment instead of being spread across systems and environments:
db = superduper('mongodb|postgres|mysql|sqlite|duckdb|snowflake://<your-db-uri>')
Run Superduper anywhere, or contact us to learn more about the enterprise platform for bringing your apps to production at scale.
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in Python.Browse the re-usable snippets to understand how to accomplish difficult AI end-functionality with few lines of code using Superduper.
The notebooks below are examples how to make use of different frameworks, model providers, databases, retrieval techniques and more. To learn more about how to use Superduper with your database, please check our Docs.
Name | Link |
---|---|
Multimodal vector-search with a range of models and datatypes | |
RAG with self-hosted LLM | |
Fine-tune an LLM on your database | |
Featurization and transfer learning |
Superduper your: MongoDB, MongoDB Atlas, Snowflake, PostgreSQL, MySQL, SQLite, DuckDB, Google BigQuery, Amazon S3, Microsoft SQL Server (MSSQL), ClickHouse, Oracle, Trino, PySpark, Pandas, Apache Druid, Apache Impala, Polars, Apache Arrow DataFusion,
Integrate and self-hosted your own models (whether from open-source, commercial or self-developed) with a simple Python command from: PyTorch, Scikit-learn, HuggingFace
Integrate externally hosted models accessible via API to work side-by-side or together with your other models a simple Python command: OpenAI, Cohere, Anthropic, Jina AI
Ideal for building new AI applications.
pip install superduper-framework
Ideal for learning basic Superduper functionalities and testing notebooks.
docker pull superduperio/superduper
docker run -p 8888:8888 superduperio/superduper
Ideal for learning advanced Superduper functionalities and testing whole AI stacks.
make build_sandbox
make testenv_init
If you have any problems, questions, comments, or ideas:
gethelp@superduper.io
.There are many ways to contribute, and they are not limited to writing code. We welcome all contributions such as:
Please see our Contributing Guide for details.
Thanks goes to these wonderful people:
Superduper 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!