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Storing and Managing Embeddings in PostgreSQL with pgvector

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Overview

Learn how embeddings are generated, stored and queried using pgvector, starting from setup to practical similarity search queries.

Syllabus

  • Unit 1: Generating Embeddings and Setting Up pgvector in PostgreSQL
    • Activating and Checking Database Extensions
    • Inspecting the Products Table Structure
  • Unit 2: Exploring the Data and Stored Embeddings in PostgreSQL
    • Exploring Product Data in SQL
    • Filtering Product Data with Embeddings
    • Exploring Data Order and Range
  • Unit 3: Running Nearest Neighbor Queries with Different Distances
    • Finding Similar Products with Embeddings
    • Exploring Similarity with Inner Product
    • Cosine Similarity in Product Search
    • Exploring Product Similarity with L1 Distance
    • Displaying Distance Values in Results
  • Unit 4: Inspecting Distances and Similarity Scores in pgvector
    • Displaying Distance Scores in Results
    • Exploring Cosine Similarity in Results
    • Comparing Distance and Similarity Scores
    • Filtering Results by Similarity Score
    • Refactoring for Clearer Similarity Results

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