rushdb-faceted-search

Build dynamic faceted search UIs on RushDB with schema-driven filters.

320|25|Updated Dec 15, 2024
One-click install
npx skills add https://github.com/rush-db/rushdb --skill rushdb-faceted-search
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rushdb-faceted-search
Source: https://github.com/rush-db/rushdb/tree/main/packages/skills/rushdb-faceted-search
Command: npx skills add https://github.com/rush-db/rushdb --skill rushdb-faceted-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build dynamic, schema-driven faceted search UIs on RushDB that let users filter records by properties, ranges, and values without hardcoding widgets.

Core Features & Use Cases

  • Discover properties and types from the live RushDB schema.
  • Enumerate distinct property values and map types to appropriate UI widgets.
  • Assemble and react to a live where clause as filters change (findRecords).

Quick Start

Generate a ready-to-use faceted search panel by loading the RushDB schema, pulling property values, selecting widgets by type, and wiring user filters into a live where clause for record queries.

Frequently Asked Questions about rushdb-faceted-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a faceted search UI that filters records by schema properties?

To build a faceted search UI, discover property types from the live schema via ontology discovery, enumerate distinct values, map types to UI widgets, and wire user filters into a live where clause for record queries.

Can I generate dynamic search filters without hardcoding widgets for every property?

Yes, dynamic search filters can be generated without hardcoding by using RushDB's ontology discovery to detect property types and automatically mapping them to appropriate UI widgets like multi-select facets and range sliders.

How does lazy loading work for large string sets in a faceted search panel?

Lazy loading for large string sets fetches distinct property values on demand during search filtering, while booleans and numbers are eager loaded, ensuring the faceted search UI remains responsive across large datasets.

Does this approach support range sliders and multi-select facets for product catalogs?

Yes, this approach supports range sliders and multi-select facets for product catalogs by mapping discovered schema types to reactive UI widgets and assembling a live where clause pipeline for interactive filtering.

What is the best way to create a reactive where clause pipeline for admin panel dashboards?

The best way to create a reactive where clause pipeline is using RushDB's findRecords query construction, reacting to filter changes and updating record queries live for admin panel dashboards without hardcoded widgets.

Are there limitations when using ontology discovery to map schema types to search filters?

A limitation of using ontology discovery to map schema types is that complex or nested relationships may require custom handling beyond standard property-based filtering, multi-select facets, and range sliders.