examples

Tantiny Examples

This directory contains practical examples demonstrating how to use Tantiny for various search scenarios.

Quick Start

For a simple demonstration of title vs description ranking:

ruby examples/simple_ranking.rb

For a comprehensive ecommerce example:

ruby examples/ecommerce.rb

Simple Ranking Example

The simple_ranking.rb example provides a minimal demonstration of field-based ranking. It shows:

• Basic indexing - Creating a simple in-memory index
• Field weighting - Using boost values to rank title matches higher than description matches
• Side-by-side comparison - See the difference between equal weights and boosted fields

This is perfect for understanding the core concept of ranking in just a few lines of code.

Ecommerce Example

The ecommerce.rb example demonstrates building an in-memory search index for an ecommerce catalog. It showcases:

• In-memory indexing - Perfect for small to medium datasets without needing persistent storage
• Product search - Indexing 5 sample products with various attributes
• Basic search - Simple keyword searches
• Fuzzy search - Handling typos and misspellings (e.g., "loptop" → "laptop")
• Field-based ranking - Boosting title matches to rank higher than description matches
• Complex queries - Combining multiple conditions with AND/OR operators
• Category filtering - Filtering products by exact category match
• Price range queries - Finding products within a specific price range

Running the Example

cd examples
ruby ecommerce.rb

Or from the project root:

ruby examples/ecommerce.rb

Key Features Demonstrated

1. Creating an In-Memory Index

index = Tantiny::Index.new do
  id :product_id
  text :title
  text :description
  string :category
  double :price
  integer :stock
end

2. Fuzzy Search for Typos

# Handles "loptop" typo and finds "laptop"
fuzzy_query = index.smart_query(:title, "loptop", fuzzy_distance: 1)

3. Field Ranking with Boost

# Title matches ranked 3x higher than description matches
title_query = index.smart_query(:title, "laptop", boost: 3.0)
description_query = index.smart_query(:description, "laptop", boost: 1.0)
ranked_query = title_query | description_query

4. Complex Queries

# Find gaming products under $2000
gaming_query = index.smart_query(%i[title description], "gaming")
price_query = index.range_query(:price, 0.0..2000.0)
complex_query = gaming_query & price_query

Sample Output

The script will show 7 different search scenarios with results, demonstrating:

• Which products match
• How ranking affects result order
• How fuzzy search handles typos
• How to combine different query types

Understanding Boost Values

The boost parameter is a multiplier for the relevance score:

• boost: 1.0 - Default/normal relevance
• boost: 2.0 - Double the relevance (2x weight)
• boost: 3.0 - Triple the relevance (3x weight)

In the ecommerce example, we use boost: 3.0 for title matches and boost: 1.0 for description matches, ensuring that products with the search term in their title appear before products that only mention it in the description.