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.
Overview
Syllabus
- Course 1: Understanding Embeddings and Vector Representations
- Course 2: Storing and Managing Embeddings in PostgreSQL with pgvector
- Course 3: Advanced Querying with pgvector
- Course 4: Indexing, Optimization and Scaling pgvector
Courses
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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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Learn how embeddings are generated, stored and queried using pgvector, starting from setup to practical similarity search queries.
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Learn how to combine filtering, full-text search, distance thresholds, and hybrid techniques to build more advanced vector search queries.
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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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