vector-notes-indexer
OfficialCreate local semantic indexes for notes and docs
Data & Analytics#semantic-search#vector-embeddings#local-storage#content-retrieval#offline-processing#note-indexing
AuthorXSpoonAi
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
sentence-transformerschromadb
Components
scripts
💻 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-notes-indexer Download link: https://github.com/XSpoonAi/spoon-awesome-skill/archive/main.zip#vector-notes-indexer Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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