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Search Functionality

🎓 For a practical walkthrough, see the: Amazon S3 Vectors with Spice engineering blog post.

Spice provides robust search capabilities enabling developers to query datasets beyond traditional SQL, including semantic (vector-based) search, full-text keyword search, and hybrid search methods.

Search Methods Overview​

Spice supports three primary search methods:

  • Vector Search: Semantic search using embeddings to retrieve data by meaning and similarity.
  • Full-Text Search: Keyword-driven search optimized for text data retrieval.
  • SQL Search: Traditional SQL queries for precise and structured searches.

Vector search uses embeddings—numerical representations of data—to identify similar or related content based on semantic meaning.

Requirements:

  • Configured data connectors or accelerators
  • Defined embeddings for datasets

Getting Started:

Example SQL Vector Search:

SELECT id, extra_column, score
FROM vector_search(my_table, 'search query')
WHERE date_published > '2021-01-01'
ORDER BY score DESC
LIMIT 5

For complete SQL UDTF specifications, see Vector-Based Search SQL UDTF.

Full-Text Search​

Full-text search efficiently retrieves records matching specific keywords.

Requirements:

  • Indexed columns within datasets

Getting Started:

Example SQL Full-Text Search:

SELECT id, extra_column, score
FROM text_search(my_table, 'search terms')
WHERE date_published > '2021-01-01'
ORDER BY score DESC
LIMIT 5

For detailed SQL UDTF instructions, see Full-Text Search SQL UDTF.