Vector Search with Relational Databases using PostgreSQL
Master vector search implementation in PostgreSQL for advanced similarity matching and data retrieval.
Master vector search implementation in PostgreSQL for advanced similarity matching and data retrieval.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Vector Database Fundamentals Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Create and manage vector embeddings in PostgreSQL
Implement efficient vector search operations
Optimize database performance with indexing techniques
Perform bulk data operations with Python and Node.js
Develop similarity search applications
Skills you'll gain
This course includes:
5.5 Hours PreRecorded video
3 assignments
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FullTime access
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There is 1 module in this course
This micro course focuses on implementing vector search capabilities in relational databases using PostgreSQL. Students learn to model vector data, perform efficient storage and retrieval operations, and optimize search performance. The curriculum covers practical aspects such as creating embeddings, managing collections, bulk data insertion, and implementing similarity search using techniques like cosine similarity. The course features hands-on labs using both Python and JavaScript for real-world applications.
Vector Search Practices for SQL Databases
Module 1 · 6 Hours to complete
Fee Structure
Instructors
Two Decades of Innovation in Technology and Education
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