Building a Wikipedia Search App with PostgreSQL, BM25, and Semantic Search

This tutorial demonstrates building a Wikipedia search application that combines keyword and semantic retrieval inside PostgreSQL. It uses real Simple English Wikipedia articles, SQL examples, and a simple web interface to compare PostgreSQL full-text search, PG Text Search BM25 ranking, pgvector semantic search, and hybrid rank fusion.

■ Setup and data loading
- Creating a Tiger Cloud PostgreSQL service and enabling PG Text Search, pgvector, and pgvector scale
- Loading about 242,000 Simple English Wikipedia articles and preparing a roughly 25,000-article embedding subset

■ Search methods
- PostgreSQL built-in full-text search with tsvector/tsquery, GIN indexes, ts_rank, and query plans
- PG Text Search BM25 scoring, index configuration, and ranked retrieval
- Semantic search with local embeddings stored in pgvector and approximate vector indexes
- Hybrid search using reciprocal rank fusion and adjustable semantic weighting

■ Application integration
- FastAPI endpoints, parameterized SQL, connection pooling, highlighted previews, and demo article insertion
- Operational considerations such as index building, performance measurement, and coverage limits

It is aimed at developers with SQL basics and enough Python to run scripts who want a practical comparison of PostgreSQL-native search techniques. Viewers gain a working mental model of keyword, semantic, and hybrid retrieval and can repeat the examples with their own data; the repository and README provide next steps.