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vector-search

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Unlock semantic search with pgvector.

Software Engineering#semantic search#embeddings#rag#vector search#similarity search#pgvector
AuthorCleanExpo
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill enables powerful semantic search capabilities by leveraging pgvector for embedding queries, similarity search, and hybrid search, streamlining data retrieval and agent knowledge management.

Core Features & Use Cases

  • Multi-Provider Embeddings: Generates embeddings using various providers (OpenAI, Ollama) for flexibility.
  • Hybrid Search: Combines vector similarity with keyword matching for more relevant results.
  • RAG Pipeline Integration: Codifies the infrastructure for document retrieval and agent memory.
  • Use Case: Implement a Q&A system over your company's documentation where users can ask natural language questions and receive precise answers derived from the most relevant document chunks.

Quick Start

Use the vector-search skill to find documents semantically similar to the query "what are the latest disaster recovery protocols?".

Dependency Matrix

Required Modules

None required

Components

references

💻 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: vector-search
Download link: https://github.com/CleanExpo/DR-NRPG/archive/main.zip#vector-search

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