In this tutorial, viewers are guided through the cutting-edge process of creating a powerful app by leveraging the capabilities of a vector searches (AKA semantic searches or similarity searches) using vector database, specifically Milvus, in conjunction with the DistilBERT machine learning model. The video meticulously explains how to harness Milvus for efficiently storing and querying large volumes of vector data, which are generated using the DistilBERT, an LLM for natural language processing (NLP). Additionally, the tutorial covers the critical steps of integrating these technologies into a cohesive application architecture and exposes this functionality through a well-designed API. By the end of the video, viewers will gain a comprehensive understanding of working with vector databases and machine learning models, enabling them to create applications that can handle complex queries and provide insightful responses with unprecedented speed and accuracy.
00:00 -- Introduciton
00:43 -- Waht is a Vector Database?
04:34 -- Getting Data into a Vector Database
08:17 -- How Searches Work on a Vector Database with an LLM
13:07 -- DistilBERT and Milvus: Your LLM and Vector Database
15:31 -- Install and Starting Milvus
16:50 -- Installing Dependencies
18:18 -- The Import Process Code
23:43 -- The Search API Code
26:31 -- Seing It In Action!
30:08 -- Conclusion
Github Repo: github.com/theonemule/vector-...
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Blaize’s Website: www.blaize.net
Blaize’s Twitter: / theonemule