smolbren similar — semantic similarity search over your vault

The similar command embeds your query with the same local model used by embed and returns the notes whose chunks are nearest in embedding space (cosine similarity, exact KNN). Unlike search, which matches keywords with BM25, similar finds notes that are about the same thing even when they share no words with the query. Requires smolbren embed to have run first (exit code 6 otherwise).

Synopsis

smolbren similar <query> [--type <type>] [--limit <n>]

Arguments and flags

string

required

A natural-language query. Full questions and descriptions work better than single keywords — the model embeds meaning, not terms.

string

Restrict results to notes whose frontmatter type exactly matches this value. Case-sensitive; discover values with smolbren types.

integer

default:"10"

Maximum number of notes to return, best match first.

Output

A JSON array of note objects, sorted by descending score. Chunk hits are deduplicated: each note appears once, represented by its best-matching chunk.

Field	Type	Description
`id`	string	Vault-relative note ID, e.g. `blogs/context-engineering`
`path`	string	Vault-relative file path including `.md`
`type`	string \| null	The note’s frontmatter `type`, or `null`
`title`	string	The note’s title
`score`	number	Cosine similarity of the best-matching chunk, in `[-1, 1]`; higher is more similar
`chunk_seq`	integer	Which chunk of the note matched best (0-based)
`snippet`	string	The matching chunk’s text, truncated to ~280 characters

Examples

Find notes by meaning

smolbren similar "how should agents decide what goes into the context window"

[
  {
    "id": "blogs/context-engineering",
    "path": "blogs/context-engineering.md",
    "type": "blog",
    "title": "Context engineering",
    "score": 0.71,
    "chunk_seq": 2,
    "snippet": "…deciding what an AI system knows, when it knows it, where that knowledge came from…"
  }
]

Restrict to one note type

smolbren similar "retrieval quality tradeoffs" --type journal --limit 5

similar needs both an index and embeddings. Exit code 5 means run smolbren index first; exit code 6 means run smolbren embed first. For a search that combines keyword precision with semantic recall, see search --hybrid.

Commands

Output & Errors

smolbren similar — semantic similarity search over your vault

Synopsis

Arguments and flags

Output

Examples

​Synopsis

​Arguments and flags

​Output

​Examples

Synopsis

Arguments and flags

Output

Examples