Most application search setups force a trade-off: settle for simple text filters, or manage a separate search cluster like Elasticsearch with complex synchronization pipelines.

In a new video course on the freeCodeCamp.org YouTube channel, I demonstrate how to run an entire production-grade search pipeline inside PostgreSQL. By pairing lexical keyword matching with dense vector embeddings in a single database, you eliminate external dependencies while preserving ACID guarantees for your search index.

Search queries usually demand two different retrieval behaviors. Exact queries like "Apollo 13" require precise keyword matching, which keyword engines handle best. Conceptual queries like "animals that make their own light" describe an idea without naming bioluminescence , which is where semantic vector search excels.…