A practical application leveraging Langchain and BigQuery Vector Search

Today, I'm thrilled to share insights into the integration of Langchain and BigQuery Vector search through a practical application that I've developed. This video presentation goes beyond theoretical discussion, offering a hands-on look at leveraging these cutting-edge technologies.

I cover critical topics like Large Language Models (LLM), Embeddings, and Vector Search, which are fundamental to the application's architecture. You'll see how these components work together to provide highly accurate and context-specific information, a key aspect of delivering real value in any business setting.

Furthermore, I discuss the broader implications of adopting this technology pattern within the industry, highlighting straightforward opportunities, areas requiring careful consideration for success, and high-risk challenges that necessitate diligent attention to ensure optimal solution performance.

I hope you find the content of this video useful :)

* 01:20 - What is Langchain
* 02:17 - Technical terms used in this presentation
* 03:51 - Use case explained
* 06:35 - Architecture
* 12:08 - Demo
* 18:53 - Code walkthrough
* 25:33 - Code walkthrough. Schema of the table on BigQUery
* 32:49 - Opportunities
* 40:01 - Summary

Code base: https://github.com/rocketechgroup/langchain_bigquery_vector/tree/main
Slides: https://docs.google.com/presentation/d/1MzE31hR01WIS8YZXd9wwQmhQyzxno9Tzv5VPENrdol0/edit?usp=drive_link

Видео A practical application leveraging Langchain and BigQuery Vector Search канала PracticalGCP