vector-notes-indexer

Index local notes with vector embeddings for offline semantic search.

17|45|Updated Jan 23, 2026

Search Tags:#semantic-search #vector-embeddings #local-storage #content-retrieval #offline-processing #note-indexing

Ready to run

npx skills add https://github.com/XSpoonAi/spoon-awesome-skill --skill vector-notes-indexer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vector-notes-indexer
Source: https://github.com/XSpoonAi/spoon-awesome-skill/tree/main/ai-productivity/vector-notes-indexer
Command: npx skills add https://github.com/XSpoonAi/spoon-awesome-skill --skill vector-notes-indexer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sentence-transformers, chromadb, and includes scripts (resource) components.

What problem does it solve?

Index local notes with vector embeddings to enable semantic search across personal knowledge bases and documentation without relying on external services.

Core Features & Use Cases

Vector Embeddings: Generate embeddings for text content to capture meaning.
Local Storage: Store indexes on-device with no required cloud access.
Semantic Search: Retrieve content by meaning, not just keywords.
Batch Indexing: Index many notes efficiently.
Incremental Updates: Add or update notes without full reindexing.
Multiple Formats: Supports Markdown, plain text, and PDFs.
Customization: Configure embedding models and index parameters for different domains.

Quick Start

Index notes locally by running python3 scripts/main.py to build a semantic index and enable offline search.

Frequently Asked Questions about vector-notes-indexer

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

FAQPage Schema

How do I build a local semantic search index for my notes and documentation?▼

To build a local semantic search index, run python3 scripts/main.py to generate vector embeddings from your notes and store them on-device using ChromaDB, enabling offline content retrieval without external services.

Can I index Markdown, plain text, and PDF files for offline semantic retrieval?▼

Yes, offline semantic retrieval supports indexing multiple formats including Markdown, plain text, and PDF files. Vector embeddings are generated for the text content to capture meaning and enable search across your local knowledge base.

How do incremental updates work when adding new notes to a vector embedding index?▼

Incremental updates let you add or update notes without requiring a full reindexing of the entire collection. The indexer processes only the new or modified content, generating vector embeddings and appending them to your local storage.

Do I need sentence-transformers and ChromaDB to run the local note indexing process?▼

Yes, local note indexing requires sentence-transformers to generate vector embeddings and ChromaDB to store the indexes on-device. These dependencies enable offline batch indexing and semantic search capabilities without cloud access.

Can I configure different embedding models for my personal knowledge base?▼

Yes, you can configure embedding models and index parameters to suit different domains. This customization allows the semantic search to better capture meaning specific to your personal knowledge bases or documentation collections.

What is the best way to perform batch indexing on a large documentation collection offline?▼

The best way to batch index a large documentation collection offline is processing the files locally to generate vector embeddings efficiently. This approach supports multiple formats and stores indexes on-device for offline semantic retrieval.