vecsearch

Name: vecsearch
Availability: InStock
Author: adriftnote

Search a basic-memory knowledge base for similar notes using vector semantics.

Updated Jan 23, 2026

Search Tags:#retrieval #memory #deduplication #semantic #knowledge-base #vector-search

Ready to run

npx skills add https://github.com/Adriftnote/zettelkasten-for-claude --skill vecsearch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vecsearch
Source: https://github.com/Adriftnote/zettelkasten-for-claude/tree/main/.claude/skills/vecsearch
Command: npx skills add https://github.com/Adriftnote/zettelkasten-for-claude --skill vecsearch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vector semantic search over a basic-memory knowledge base to locate similar notes and explore related knowledge quickly.

Core Features & Use Cases

Vector-based semantic search to discover similar notes and related knowledge within a memory base.
Supports distance-based ranking, filtering by type or project, and optional deduplication.
Use Case: Surface topic-related notes to triangulate information or avoid duplication.

Quick Start

Run the vecsearch command with your query to retrieve top matching notes.

Frequently Asked Questions about vecsearch

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

FAQPage Schema

How do I find similar notes in a memory knowledge base?▼

Vector semantic search identifies similar notes in a memory knowledge base by comparing vector embeddings. It returns a structured table of matching results ranked by distance metrics to help you quickly locate related information.

Can I deduplicate notes using vector search?▼

Yes, vector search supports optional deduplication to identify and surface similar notes within memory collections. This allows you to avoid content duplication and triangulate overlapping information across projects.

How does semantic search filter results by project or type?▼

Semantic search filters results by applying distance-based ranking while restricting the query scope to specific note types or projects. This ensures context-aware retrieval across your memory knowledge base.

What do I need to set up before running vector search on my notes?▼

You need a pre-built vector index within your basic-memory knowledge base before running the search. The command parses your query arguments to retrieve the top matching notes from this index.

Are there limitations to using vector search for memory retrieval?▼

Vector search requires an existing vector index and is limited to retrieving notes within the basic-memory knowledge base. It returns distance metrics for ranking but does not generate new content or modify the stored notes.