semantic_search
Find notes by conceptual meaning using vector embeddings. Supports filtering by folder, tags, and frontmatter for targeted searches.
Instructions
Vector similarity search using bge-m3 embeddings. Use this for conceptual or paraphrased queries — anywhere exact word matching would miss the point.
For exact identifiers, code symbols, proper nouns, or known phrases, use keyword_search instead.
Each result is one note (deduped) with its best-matching chunk as a ~500-char preview.
Call read_note on a result's path to get the full note content.
Args: query: Natural language description of what you're looking for. limit: Maximum number of distinct notes to return (default 15). folder: Optional folder prefix (e.g. "Projects/"). tags: Optional list of tag names; only notes carrying ALL listed tags match (e.g. ["product"]). frontmatter: Optional dict of frontmatter key/value pairs; strict type matching — string "0" does not match integer 0 (e.g. {"status": "active"}).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No | ||
| folder | No | ||
| tags | No | ||
| frontmatter | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |