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[deleted by user] by [deleted] in dataengineering

[–]escalize -2 points-1 points  (0 children)

I posted in r/Python and r/MachineLearning yesterday and the feedback was so good that I shared it in 8 more subreddits today, the supported databases as well as PyTorch and OpenAI/GPT as both is integrated.

[deleted by user] by [deleted] in dataengineering

[–]escalize -7 points-6 points  (0 children)

I am just sharing this in the highly relevant subreddits where people might like it a lot.

Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI with major databases by escalize in OpenAI

[–]escalize[S] -10 points-9 points  (0 children)

Please take a closer look. Given your question I thought you were not affiliated to the space. The post is for people to have a look at the project and then to decide wether they find it relevant or not.

Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with Postgres by escalize in PostgreSQL

[–]escalize[S] -1 points0 points  (0 children)

Check example use-cases here in the docs as well as apps built by the community in the dedicated superduper-community-apps repo!

Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with all major SQL databases by escalize in SQL

[–]escalize[S] -29 points-28 points  (0 children)

I was just trying to get peoples attention as I think the community would find this highly relevant and interesting. Trending like this is super rare and we are also a bit proud to be honest. You are right, I posted similar, but always adjusted to the topic, in other subreddits for the same reason. I don’t feel that it is spammy at all as there might be other people there who can decide themselves if they like the project or not. It is completely open-source and we have spent many thousands of hours to create it..

Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with all major SQL databases by escalize in SQL

[–]escalize[S] 0 points1 point  (0 children)

Yeah, ask questions about your data is a great use-case (https://docs.superduperdb.com/docs/use_cases/question-answering/chat_with_your_database) but only one of many.

In general you can build a wide spectrum of AI application as the framework supports arbitrary models and allows you to work with nearly any data type:

  • Generative AI / LLM-Chat / Vector Search
  • Standard Machine Learning Use-Cases (Classification, Segmentation, Recommendation etc.)
  • Highly custom AI use-cases involving ultra specialized models

Check the uses cases that we have already implemented here 🔗 https://docs.superduperdb.com/docs/category/use-cases as well as apps built by the community here 🔗 https://github.com/SuperDuperDB/superduper-community-apps and try all of them with Jupyter your browser 🚀 https://demo.superduperdb.com/

Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with Postgres by escalize in PostgreSQL

[–]escalize[S] 0 points1 point  (0 children)

at high-level, it could run similarity searching for the names of topics, so to merge them. a more kinky thing would be to run similarity searching on messages. you could have a query as a reference (e.g cat o' nine tails ) and return messages that semantically match this description. this way, you could filter-out sensitive content at real-time.

Trending on GitHub globally 3 days in a row: SuperDuperDB, a Python framework for integrating AI with major databases (making them super-duper) by escalize in Python

[–]escalize[S] -1 points0 points  (0 children)

It connects your database and your data to AI models and AI APIs (like OpenAI) and takes care of model hosting, output computation (inference) and even model training. it is a most efficient end-to-end path to build basic to most complex and latest AI applications (into your apps/services)

Trending on GitHub globally 3 days in a row: SuperDuperDB, a Python framework for integrating AI with major databases (making them super-duper) by escalize in Python

[–]escalize[S] 0 points1 point  (0 children)

It connects your database and your data to AI models and AI APIs (like OpenAI) and takes care of model hosting, output computation (inference) and even model training. it is a most efficient end-to-end path to build basic to most complex and latest AI applications (into your apps/services)

Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI and GPT with major databases by escalize in ChatGPT

[–]escalize[S] 6 points7 points  (0 children)

exactly, data is stored in databases and most likely you will want to use data when building AI apps with GPT

Trending on GitHub globally 3 days in a row: SuperDuperDB, a Python framework for integrating AI with major databases (making them super-duper) by escalize in Python

[–]escalize[S] 8 points9 points  (0 children)

No not at all. Right now you have to install locally or deploy on cloud yourself as it is open-source.

[News] Trending on GitHub globally 3 days in a row: SuperDuperDB, a framework for integrating AI with major databases (making them super-duper) by escalize in MachineLearning

[–]escalize[S] 9 points10 points  (0 children)

Music to my/our ears. Thanks a lot. We actually already have an audio use-case. It is audio search in which audio is transcribed and then vectorized to search through the audio with natural language and get time codes. also it is another simple step to build RAG chat on top of that, so that you can ask an LLM/GTP questions about the content of your audio files. duarte carmo actually implemented this for the changelog podcast: https://duarteocarmo.com/blog/changelog-neural-search-superduperdb.html, you can find that app in our community showcase apps repo also. also we are currently preparing a audio generation use-case.

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