Embedding Search

Name: Embedding Search
Availability: InStock
Author: winsorllc

Provides semantic search across documents with Tunrsity and Tunrsity-like features.

1|Updated Feb 25, 2026

Search Tags:#semantic search #embeddings #rag #knowledge base #vector search #hybrid search

Ready to run

npx skills add https://github.com/winsorllc/upgraded-carnival --skill embedding-search

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Embedding Search
Source: https://github.com/winsorllc/upgraded-carnival/tree/main/.pi/skills/embedding-search
Command: npx skills add https://github.com/winsorllc/upgraded-carnival --skill embedding-search

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill enables efficient searching through large collections of documents by understanding the semantic meaning of queries, not just keywords.

Core Features & Use Cases

Semantic Document Search: Find documents based on conceptual similarity.
Hybrid Search: Combines keyword matching with semantic similarity for robust results.
Use Case: Quickly find information about "cloud deployment strategies" across a vast codebase or documentation library, even if the exact phrase isn't present.

Quick Start

Use the embedding-search skill to search for "machine learning concepts" within your indexed documents.

Frequently Asked Questions about Embedding Search

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

FAQPage Schema

How do I perform semantic search on my documents?▼

Semantic search on your documents is performed using simulated embeddings to understand conceptual similarity. It enables efficient searching through large collections by matching query meaning rather than exact keywords.

What is hybrid search and how does it improve document retrieval?▼

Hybrid search improves document retrieval by combining keyword matching with vector similarity. This approach ensures robust results by leveraging both exact term matching and semantic understanding.

Can I index and add new documents to a persistent knowledge base?▼

Yes, you can add documents to a persistent knowledge base. The system supports indexing, searching, and continuously adding new documents to maintain an updatable vector search index.

What is the best way to find information across a documentation library without exact phrases?▼

The best way to find information without exact phrases is using vector-based semantic search. It locates documents based on conceptual similarity, finding relevant cloud deployment strategies even if the exact phrase is absent.

How does document chunking work with vector similarity ranking?▼

Document chunking splits text into manageable segments before applying vector similarity ranking. This process ensures precise relevance ranking by evaluating simulated embeddings for each specific chunk.

Does semantic search require external dependencies or APIs?▼

No external dependencies or APIs are required for semantic search. The system performs vector-based semantic search using simulated embeddings entirely within its own scripts.