Vector Search with Relational Databases using PostgreSQL

Skill-Up EdTech Team and Lavanya Thiruvali Sunderarajan

With vector databases now powering business competitiveness through super-fast applications such as recommendation engines, it’s no surprise that the vector database market is set to grow 23% CAGR by 2032 (Markets and Markets).

This micro course gives aspiring data scientists, ML engineers, gen-AI engineers, software developers, and other data-oriented roles the in-demand skills for performing vector searches in relational databases.

What's inside

Syllabus

Vector Search Practices for SQL Databases

Welcome to this module, where you’ll learn how to implement vector searches using relational databases. You’ll begin with a recap of RDBMS and then dive into the structures RDBMS uses to support vector data types and queries. You’ll apply what you know to perform similarity search tasks using special operators available in PostgreSQL. And, with a focus on PostgreSQL, you’ll learn how to create tsvector data, perform tsquery tasks, and perform bulk inserts using pg-vector for Node.js and psycopg2 for Python.

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Search with Relational Databases using PostgreSQL with these activities:

SQL querying fundamentals recap

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Refreshes basic querying skills, which are foundational to this course

Browse courses on SQL

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Review the syntax of basic SQL queries
Write sample queries to retrieve data
Practice using aggregate functions
Try using subqueries
Explore using joins

Create a comprehensive course notebook

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Organize and synthesize course materials to enhance retention.

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Gather lecture notes, slides, assignments, and other course materials
Organize materials into logical sections and subsections
Annotate materials with your own notes and summaries
Create a table of contents and index for easy navigation

Create a comprehensive study guide

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Consolidate and summarize key concepts from the course for easy reference.

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Gather all course materials (notes, slides, assignments).
Identify and organize main topics and subtopics.
Summarize key concepts and examples in a concise manner.

Three other activities

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Work through vector similarity examples

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Improve your understanding of vector similarity measures by practicing with examples.

Browse courses on Cosine Similarity

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Review the different vector similarity measures
Calculate the similarity between two vectors using each measure
Interpret the results and identify the most appropriate measure for your application

Build a vector-powered recommendation system

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Apply vector search concepts to create a real-world recommendation engine.

Browse courses on Recommendation Systems

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Design the data model for storing vectorized product data.
Implement vector search functionality using a relational database.
Develop a user interface for browsing and searching products.
Evaluate the performance and accuracy of the recommendation system.

Write a blog post or article on a vector database topic

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Deepen your understanding and showcase your knowledge by creating content on vector databases.

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Choose a specific topic related to vector databases that you are passionate about
Conduct thorough research to gather insights and information
Organize your content into a logical structure with an introduction, body, and conclusion
Write clear and engaging prose that effectively conveys your knowledge