Building a Vector Search Engine with Pgvector | CodeSignal Learn
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intermediate
intermediate
Building a Vector Search Engine with Pgvector
Artificial Intelligence
4 courses
44 practices
6 hours
This learning path introduces the fundamentals and practical implementation of vector-based search systems, from generating text embeddings to building scalable semantic search with pgvector. Learners will be able to create and manage efficient vector search engines.
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Earn a shareable
Certificate of Achievement
Verified skills you'll gain
Badge for Feature Engineering and Text Representation, Developing
DEVELOPING
Feature Engineering and Text Representation
Badge for NLP Model Evaluation and Optimization, Intermediate
INTERMEDIATE
NLP Model Evaluation and Optimization
Badge for Programming and Text Processing Algorithms, Intermediate
INTERMEDIATE
Programming and Text Processing Algorithms
Badge for Text Data Collection and Preparation, Intermediate
INTERMEDIATE
Text Data Collection and Preparation
Tools you'll use
OpenAI
PGVector
Python
Trusted by learners working at top companies
Uber
Meta
Instacart
Google
Netflix
Zoom
Course 1
Understanding Embeddings and Vector Representations
4 lessons
12 practices
This course introduces vector embeddings, why they are useful for search, and how to generate them using different models like OpenAI and Hugging Face.
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Course 2
Storing and Managing Embeddings in PostgreSQL with pgvector
4 lessons
Course 3
Advanced Querying with pgvector
4 lessons
Course 4
Indexing, Optimization and Scaling pgvector
3 lessons
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I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!
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Francisco Aguilar Meléndez
Data Scientist
Badge for General Programming, AdvancedBadge for Coding and Data Algorithms, AdvancedBadge for Deep Learning and Neural Networks, Expert
+11
15 practices
Learn how embeddings are generated, stored and queried using pgvector, starting from setup to practical similarity search queries.
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10 practices
Learn how to combine filtering, full-text search, distance thresholds, and hybrid techniques to build more advanced vector search queries.
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7 practices
Learn how to scale and optimize pgvector queries using indexing, tuning search parameters, monitoring database performance, and running queries using these indexes.
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