Postgres as a Search Engine
Postgres can function as a search engine by integrating full-text, semantic, and fuzzy search techniques, enhancing retrieval quality and allowing for effective ranking and relevance tuning within existing databases.
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Postgres can be effectively utilized as a search engine by integrating semantic, full-text, and fuzzy search techniques, making it suitable for retrieval-augmented generation (RAG) pipelines. The article outlines a method to build a robust search system using Postgres, emphasizing the importance of combining traditional lexical search with modern semantic approaches. Key components include full-text search using `tsvector`, semantic search with `pgvector`, and fuzzy matching through the `pg_trgm` extension. The implementation involves creating a structured table for documents and utilizing various SQL queries to rank search results based on relevance. The article also discusses the significance of tuning search parameters, such as adjusting weights for different text fields and normalizing document lengths to enhance search accuracy. By leveraging these techniques, developers can create a scalable search solution within their existing Postgres database, avoiding the need for separate search services.
- Postgres can serve as a comprehensive search engine by integrating multiple search techniques.
- The combination of full-text, semantic, and fuzzy search enhances retrieval quality in applications.
- Tuning search parameters, such as weights and normalization, is crucial for improving search relevance.
- The use of SQL queries allows for effective ranking and retrieval of documents based on user queries.
- Implementing these techniques can streamline search functionalities within existing Postgres databases.
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8 commentsIt was a really, really hard task 20 years ago, but I'd imagine that now there must be a drop-in grep/ag replacement for natural languages that you run once to build an index and it takes care of all this stemming, semantic embeddings and all other clever specialized things for you. Isn't there one?
And if no, what tools/libraries do exist in this area? To make something more sophisticated than in this post?
https://austingwalters.com/fast-full-text-search-in-postgres...
Imo custom indexes are the real key to more accuracy and speed. That said, if you have <100m documents the built in search functions are great and really depends on your speed requirements.
FTS and trigram can perform quite poorly unless the data and indices are tuned properly.
2. Postgre is not serverless, so it is not easy to separate read and write, and it is not easy to auto scaling
1. Full-text search with FTS5
2. Semantic search with sqlite-vec
3. Fuzzy matching with FTS5 trigram tokenizer
4. Bonus: FTS5 bm25() function
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