Row 5664

Row ID: 5664 | Dataset Entry | Axioma AXP Content Repository

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Hey everyone, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech.

\[spRAG\](https://github.com/SuperpoweredAI/spRAG) is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, **spRAG gets 83% of questions correct, compared to 19% for the vanilla RAG baseline** (which uses Chroma + OpenAI Ada embeddings + LangChain).

You can find more info about how it works and how to use it in the project’s README. We’re also very open to contributions. We especially need contributions around integrations (i.e. adding support for more vector DBs, embedding models, etc.) and around evaluation.

Happy to answer any questions!

\[GitHub repo\](https://github.com/SuperpoweredAI/spRAG)

FieldValue
text Hey everyone, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech. \[spRAG\](https://github.com/SuperpoweredAI/spRAG) is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For …
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-02
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Raw Record

{
  "text": "Hey everyone, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech.\n\n\\[spRAG\\](https://github.com/SuperpoweredAI/spRAG) is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, **spRAG gets 83% of questions correct, compared to 19% for the vanilla RAG baseline** (which uses Chroma + OpenAI Ada embeddings + LangChain).\n\nYou can find more info about how it works and how to use it in the project’s README. We’re also very open to contributions. We especially need contributions around integrations (i.e. adding support for more vector DBs, embedding models, etc.) and around evaluation.\n\nHappy to answer any questions!\n\n\\[GitHub repo\\](https://github.com/SuperpoweredAI/spRAG)",
  "label": "r/machinelearning",
  "dataType": "post",
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  "datetime": "2024-05-02",
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}
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