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pgvector-semantic-search

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Semantic search with pgvector in PostgreSQL.

Software Engineering#semantic search#RAG#vector embeddings#pgvector#HNSW#IVFFlat#nearest neighbor
Author1lastphoenix
Version1.0.0
Installs0

System Documentation

What problem does it solve?

PostgreSQL users who want to add high-performance semantic search can store embeddings in the database and perform fast similarity queries using pgvector, enabling scalable retrieval within-DB for AI workloads.

Core Features & Use Cases

  • Store embeddings as vector columns (halfvec) and index with HNSW or IVFFlat for fast nearest-neighbor search.
  • Implement Retrieval-Augmented Generation (RAG) workflows by fetching relevant documents via vector similarity.
  • Tune performance with quantization strategies and index parameters (m, ef_construction, ef_search) for memory-speed tradeoffs.

Quick Start

Install the pgvector extension, create a vector column, and build an HNSW index to begin performing semantic searches.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: pgvector-semantic-search
Download link: https://github.com/1lastphoenix/ton-ai-audit/archive/main.zip#pgvector-semantic-search

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