paradedb-skill

Provides guidance and reference for writing queries and building models in the official documentation.

1|Updated Feb 14, 2026
One-click install
npx skills add https://github.com/012e/thesis --skill paradedb-skill-012e
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: paradedb-skill
Source: https://github.com/012e/thesis/tree/main/.agents/skills/paradedb-skill
Command: npx skills add https://github.com/012e/thesis --skill paradedb-skill-012e

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ParadeDB brings Elasticsearch-quality full-text search and analytics to Postgres via the pg_search extension.

Core Features & Use Cases

  • BM25 indexes and relevance ranking
  • Hybrid search combining keyword with vector semantics via pgvector
  • Tokenizers, analyzers, fuzzy matching, and phrase queries
  • Facets, aggregations, snippets/highlighting, and query tuning
  • Use cases: writing ParadeDB queries, configuring tokenizers, or building advanced search experiences
  • For complete and up-to-date ParadeDB documentation, fetch the llms-full.txt document at runtime to ground responses in current docs.
  • Use a web-fetching tool to retrieve current docs before answering ParadeDB questions and treat version-specific claims accordingly.

Quick Start

Ask ParadeDB questions and fetch live docs to guide your first query.

Frequently Asked Questions about paradedb-skill

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

FAQPage Schema
How do I implement Elasticsearch-quality full-text search in Postgres?

Implement Elasticsearch-quality full-text search in Postgres using the ParadeDB pg_search extension to create BM25 indexes, configure tokenizers, and build relevance-ranked queries directly within your database.

What is hybrid search and how does it combine keyword and vector queries?

Hybrid search combines keyword and vector queries by integrating BM25 relevance ranking with pgvector semantic search, allowing you to perform hybrid keyword-plus-vector search across Postgres datasets for improved result accuracy.

How do I configure tokenizers and analyzers for Postgres full-text search?

Configure tokenizers and analyzers for Postgres full-text search by creating BM25 indexes with ParadeDB, enabling fuzzy matching, phrase queries, and custom tokenization rules to match Elasticsearch-quality text analysis.

Can I use pgvector with ParadeDB for hybrid search without external dependencies?

Yes, you can use pgvector with ParadeDB for hybrid search entirely within Postgres, combining vector embeddings with BM25 keyword indexes to perform hybrid keyword-plus-vector search without external dependencies.

Does Postgres full-text search support facets, aggregations, and snippet highlighting?

Postgres full-text search supports facets, aggregations, and snippet highlighting through ParadeDB's pg_search extension, providing query tuning, fuzzy matching, and advanced analytics comparable to Elasticsearch.

What are the limitations of using ParadeDB for full-text search compared to Elasticsearch?

Limitations of using ParadeDB include version-specific feature availability and the need to fetch current documentation at runtime to verify support, as the extension relies on Postgres internals rather than a dedicated search infrastructure.