Building Search and RAG for OpenAPI Specifications
Qdrant - Vector Database & Search Engine via YouTube
Future-Proof Your Career: AI Manager Masterclass
Google, IBM & Microsoft Certificates — All in One Plan
Overview
Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Watch a 33-minute Vector Space Talk featuring Nick Khami, Founder-Engineer at Trieve, demonstrating practical techniques for implementing vector search and Retrieval-Augmented Generation (RAG) capabilities over OpenAPI specifications. Learn how to leverage Large Language Models for generating improved code embeddings through summarization, and discover methods for efficiently storing UI-relevant data within point metadata for streamlined interface development. Gain insights from Nick's extensive experience building vector search and RAG applications since Qdrant v0.11.0, and explore the journey that led to founding Trieve, a company focused on helping businesses implement cutting-edge vector search and RAG solutions. Delve into topics including OpenAPI specifications, search UI implementation, analytics integration, and receive valuable recommendations for working with vector databases.
Syllabus
Intro
OpenAPI spec
Search UI
Treeve
Why Treeve
Analytics
Journey of using Quadrant
Recommendations
Taught by
Qdrant - Vector Database & Search Engine