---

title: 'Search Functionality' sidebar_label: 'Search' description: 'Learn how Spice can search across datasets using database-native and vector-search methods.' sidebar_position: 10 pagination_prev: null pagination_next: null tags:

• search
• features
• models

---

import DocCardList from '@theme/DocCardList';

🎓 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 multiple 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.
• Hybrid Search: Combine multiple search methods using Reciprocal Rank Fusion (RRF) for improved relevance.
• SQL Search: Traditional SQL queries for precise and structured searches.

Vector Search

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:

• Configure Embeddings
• Performing Vector Search

Example SQL Vector Search:

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:

• Full-Text Search Docs

Example SQL Full-Text Search:

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

Hybrid Search with RRF

Reciprocal Rank Fusion (RRF) combines results by merging rankings from multiple search methods to improve relevance.

Requirements:

• Multiple search methods configured (vector, full-text, etc.)

Example SQL Hybrid Search:

For complete RRF syntax and parameters, see Search SQL Reference.