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fastapi-pgsearch-patterns

Official

Build AI-powered APIs with hybrid retrieval patterns.

Software Engineering#fastapi#retrieval#async#sqlalchemy#pgvector#hybrid#pg_search
AuthorMercurium-Analytics
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This skill provides reusable patterns to assemble FastAPI with pg_search and pgvector to enable hybrid lexical + semantic retrieval in API endpoints, accelerating development of AI-powered search services.

Core Features & Use Cases

  • Async FastAPI integration with SQLAlchemy Async, pgvector, and pg_search to support high-throughput search workloads.
  • Patterns for lexical (bm25) and vector (embedding-based) retrieval, plus a simple hybrid ranking workflow for end-to-end APIs.
  • Use cases include building RAG APIs, semantic search endpoints, and streaming results from a retrieval-augmented generation pipeline.

Quick Start

Run a FastAPI app configured with pg_search and pgvector patterns to expose a hybrid retrieval endpoint.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: fastapi-pgsearch-patterns
Download link: https://github.com/Mercurium-Analytics/pg-search-vector/archive/main.zip#fastapi-pgsearch-patterns

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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