Vector Similarity Search for Semantic Retrieval
The lesson shows how to run vector similarity searches combined with metadata filters and hybrid keyword search, explains why Postgres lacks a built-in reciprocal rank fusion function, and emphasizes setting distance thresholds to discard irrelevant results and prevent LLM hallucinations.
Previous Module
Introducing Azure Database for PostgreSQL
This Module
AI Vector Search on PostgreSQL
Introduction to pgvector
Store and Query Embeddings with pgvector
Vector Similarity Search Indexes
Vector Similarity Search for Semantic Retrieval
Retrieval Patterns for RAG Pipelines
Optimize Vector Search in Azure Database for PostgreSQL
Tune PostgreSQL for pgvector
Choose and Configure Vector Indexes
Optimize Data Layout for Filtered Search
Scale PostgreSQL for High-Volume Workloads