Embeddings & search

Model

Chunks and queries are embedded with BAAI/bge-small-en-v1.5 via fastembed, which runs locally on CPU — no API calls, no key. Override the model in config.toml; changing it requires re-indexing every bookmark.

Vector store

Each bookmark collection maps to one Chroma collection (col_<id>) using cosine distance. Search embeds the query, runs a nearest-neighbour lookup capped at top_k, and returns each hit as {text, url, title, score} where score = 1 − distance (higher is closer).

An empty or missing collection returns no results rather than an error.

search vs ask

Both tools run the identical retrieval. search_collection is framed for a keyword query and returns a bare chunk list; ask_collection takes a natural-language question and wraps the chunks with the question so the client can synthesise an answer. Neither does any generation itself — that’s the client’s job.