PostgreSQL Hybrid Search Course – Keyword (BM25) + Vector Retrieval

By freeCodeCamp.org
Published: Oct 21, 2026

“
Learn how to build a complete Wikipedia search engine entirely within PostgreSQL using BM25 keyword matching, vector embeddings, and reciprocal rank fusion. This step-by-step course covers setting up `pg_textsearch` and `pgvector-scale`, analyzing query plans, tuning hybrid weights, and serving results through a FastAPI backend. By keeping your data, keyword indexes, and vectors inside a single database, you eliminate complex synchronization pipelines and unlock fast, transactional search.

Code: https://github.com/beaucarnes/wikisearch

Course created by @beau

Try Tiger Data Cloud: https://tsdb.co/fcc-postgres-search

Chapters
– 00:00 Introduction & Project Overview
– 04:06 Matching vs. Ranking & PostgreSQL Text Search
– 07:20 Understanding BM25 (IDF, Term Frequency, & Document Length)
– 11:39 Setting Up Tiger Data Cloud & PostgreSQL
– 14:06 Environment Setup & Enabling Database Extensions
– 15:43 Loading Wikipedia Dataset & Schema Setup
– 20:30 Native PostgreSQL Full-Text Search Baseline
– 24:38 Analyzing Query Plans with EXPLAIN ANALYZE
– 27:40 Implementing BM25 Keyword Search with pg_textsearch
– 32:32 Building the Keyword Search Query & Highlighting (TS_HEADLINE)
– 36:18 Boolean Conditions & Phrase Search
– 37:32 Semantic Search Fundamentals & Generating Embeddings
– 41:33 Vector Indexing with pgvector-scale
– 43:28 Reciprocal Rank Fusion (RRF) for Hybrid Search
– 46:58 Query Plan Analysis for Hybrid Search
– 49:12 Comparing Keyword, Semantic, and Hybrid Results
– 52:13 Connecting the FastAPI Backend & Connection Pooling
– 54:06 Testing the Web Interface & Weight Tuning
– 55:23 Adding New Records via Transactional Writes
– 57:24 Scale Benchmarks & Next Steps for Optimization

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