Artificial Intelligence
Intermediate
Vector Databases in Practice
Understand vector indexing, payloads, and reliable similarity search.
Course Overview
Work through collection design, point identifiers, metadata filtering, and synchronization concerns using realistic catalog data. You will also learn why vector state needs an operational source of truth.
What You Will Learn
Design vector collection schemas with optimized distance metrics (Cosine, Dot, Euclidean)
Structure vector payloads with typed metadata for efficient pre-filtering
Manage outbox synchronization between SQL source of truth and Qdrant index
Perform vector snapshot backups, index rebuilding, and orphan detection
Diagnose retrieval latency and recall performance bottlenecks
Tools & Technologies Used
Qdrant
Python
SQLAlchemy
FastAPI
Docker
Structured Curriculum
2 Modules · 6 Lessons · 165 Minutes Total
Module 1
Module 1: Vector Indexing & Collection Design
3 lessons
Module 1: Vector Indexing & Collection Design
Master point creation, embedding dimensions, and distance metrics.
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Embedding Spaces & Distance Metrics
Compare Cosine, Dot product, and Euclidean distances for semantic similarity.
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Qdrant Collection & Payload Schema Setup
Initialize collections, set point vector sizes, and attach JSON payload fields.
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Payload Indexing & Metadata Pre-Filtering
Build boolean, keyword, and range payload indexes to execute fast filtered queries.
Module 2
Module 2: Transactional Sync & Operational Health
3 lessons
Module 2: Transactional Sync & Operational Health
Maintain vector index integrity with SQL outbox tables and reconciliation.
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The Transactional Vector Outbox Pattern
Prevent database-vector drift using atomic SQL outbox queues.
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Reconciliation & Orphan Point Deletion
Write operational reconciliation scripts to detect and clean orphaned vector points.
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Performance Tuning & Benchmark Analysis
Measure query QPS, indexing memory footprint, and recall accuracy.
Practical Project & Capstone Outcome
🚀 Capstone Project
Resilient Catalog Vector Search Service
Implement a complete vector index pipeline featuring an outbox synchronization background worker, metadata-filtered semantic search endpoint, and vector reconciliation audit script.
Prerequisites
- Python intermediate proficiency
- Basic knowledge of embeddings
Intended Audience
- Data Engineers implementing production vector search infrastructure
- Backend Engineers adding semantic search capabilities
Instructor Information
N
Nikhil Rao
Course Author & Industry Expert
Nikhil Rao is a Lead Data Infrastructure Engineer who has engineered large-scale vector search engines for enterprise clients.
Frequently Asked Questions
Can I run Qdrant locally without cloud accounts?
Yes! The course demonstrates both embedded Qdrant local storage and containerized local Docker setups.