embeddingslisted

# br8n — Embeddings (which provider, and switching it) Semantic search needs vectors. br8n produces them one of three ways: | Provider | When it applies | Dim | |---|---|---| | `remote` | `AI_GATEWAY_API_KEY` or `OPENAI_API_KEY` is set | 1536 | | `local` | the `br8n[local-embeddings]` extra is installed, no key is set, and the KB is on the **local tier** — cloud pgvector columns are 1536-wide, so `local` always refuses on cloud | 384 | | `none` | neither — capture and chronological surfaces still work, search is text-only | — | ## Step 1 — Report the current state Call `mcp__plugin_br8n_br8n__br8n_embeddings_get()`. Lead with the provider, model and *why* it was chosen (`source`), then flag anything actionable: - `ready: false` with `provider: local` → the model is still downloading (~130 MB, first use only). Search stays text-only for a minute; nothing is lost. - `pending_findings`/`pending_nodes` above zero → a re-embed is draining in the background. It refills on ordinary reads; no action needed. - `provider: none` → say what would fix it: either set a key, or `pip install 'br8n[local-embeddings]'` and switch to local. - `pending_switch` not `null` → the environment quietly changed (e.g. a key went missing) and would flip the space (`pending_switch.stored` → `pending_switch.detected`), but existing vectors are at risk, so br8n left them alone instead of rebuilding. Tell the user what changed and offer to apply it — that offer is exactly Step 2. ## Step