Find 'Abbey Road when type 'Beatles abbey rd': Fuzzy/Semantic search in Postgres

3 months ago

The article discusses the challenge of matching messy user input to clean database entries, using music albums as an example.
Two PostgreSQL extensions are highlighted for solving this problem: pg_trgm for fuzzy text matching and pgvector for semantic similarity search.
The Spotify Tracks Dataset from Hugging Face is used as a real-world dataset to demonstrate the approaches.
pg_trgm breaks text into 3-character sequences (trigrams) to measure overlap, useful for typos, abbreviations, and word order variations.
pgvector uses vector embeddings to compare meaning, useful for synonyms, paraphrasing, and conceptual similarity.
A normalization pipeline is introduced to clean input text, improving matching accuracy for both approaches.
The article suggests a hybrid approach: start with fuzzy matching and fall back to semantic search if needed.
Performance considerations and when to use each approach are discussed, with pg_trgm recommended for most cases and pgvector for semantic understanding.
Different embedding models are compared, with all-mpnet-base-v2 recommended for general-purpose use.
The conclusion emphasizes that matching messy input to clean catalogs is achievable with PostgreSQL and the right tools.

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