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For further queries, please write to us at support@hasgeek.com or call us at +91 7676 33 2020.Transforming Document Curation: LMs and vector databases at scale
Submitted Jun 30, 2023
Abstract
Auquan is an AI startup that serves institutional investors and investment managers with curated news and documents to help them make better investment decisions.
In this presentation, I will discuss our approach for using a vector database and a tuned language model to curate news items at scale.
I will walk through the process and pitfalls for using these technologies in production, and provide best practices for achieving high performance. Specifically, I will discuss the metrics that we used for selecting a vector database and tune our language model.
Audience
ML Engineers, early stage Data Scientists
Takeaways
- How to use a vector database and a language model to curate news items at scale
- Best practices for using vector databases in production
- Pitfalls to avoid when using vector databases
Presentation Outline:
- Introduction
- About Auquan
- Problem description
- Vector databases for news curation
- Choosing a vector database
- Using embeddings with vector databases for different tasks
- Offline population and real time inference
- Tuning a language model for news curation
- Tuning an LM
- Using tuned model for embedding
- Stack architecture
- Conclusion/QA
Link to the Presentation
https://drive.google.com/file/d/1FubDUOYBJhzgaW-H6qxrlkEO49d3abDr/view?usp=sharing
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Nischal HP
@nischalhp Editor
Hello Samik Raychaudhuri,
If possible, I would include the vector databases as a section in the previous talk, thereby connecting the various dots with a good storyline.
Samik Raychaudhuri
@samikr Submitter
Hello Nischal,
Thanks. Actually, this talk is organized in a way that the model tuning part is a subsection. Given your suggestion, I think this talk would probably present a complete/end-to-end storyline. Please let me know what you think - open to suggestions.