List all vector datasets in the RosalindDB instance, with each dataset's dimension, status, and row count.

Create a new empty vector dataset. You choose the name and the vector dimension; vectors are added afterwards with ingest_vectors.

Get a single dataset's details: dimension, status (empty/validating/indexing/indexed/error), row count, and timestamps. Useful for polling indexing progress after ingest.

Delete a dataset and all its vectors. This is a soft-delete; the dataset becomes immediately unavailable.

Ingest (upsert) vector records into a dataset (last-write-wins per id). Each record needs an id, a values array matching the dataset dimension, and optional flat metadata. The response reports accepted/rejected counts and per-record errors. Read-your-writes depends on the server's recall tier: if the result has NO job_id the write was synchronous and is immediately queryable; if it returns a job_id, indexing is asynchronous (eventually consistent) — poll get_dataset until status is 'indexed'. For dumps over ~10 MiB use the async import flow.

Run a vector similarity search against a dataset. Returns nearest neighbours sorted by L2 distance (lower score = closer; 0.0 is exact). The result 'mode' names the tier that answered: 'recall' = the read-your-writes recall tier; 'hot'/'cold' = the consolidated object-storage tier ('hot' = shard already cached in memory, 'cold' = first fetch from object storage); 'ephemeral' = no shard yet (computed on demand). With the recall tier on, a just-ingested vector is immediately searchable (read-your-writes). Supports an optional flat metadata filter (exact-match AND semantics; run exhaustively server-side).

Fetch a single vector record by id: its id and metadata. Set include_values=true to also return the stored embedding for a recall-resident vector (a consolidated/cold-only vector omits it even when requested — its absence is expected, not an error). Errors with not_found if the id is absent or deleted.

List vector records (id + metadata) in a dataset, optionally filtered by a flat exact-match metadata filter, with a page limit and cursor. Returns { vectors, next_cursor }. Useful for enumerating or auditing the memories an agent has stored.

Delete a single vector (memory) by id. With the recall tier on this is a synchronous tombstone — the vector is immediately gone from queries (read-your-deletes); otherwise it queues an async rebuild and returns a job_id. Deleting an unknown id is a clean no-op; a recall-tier failure surfaces as a retryable 503 (recall_delete_failed).

Get the instance's current usage and quotas: vectors stored vs quota, queries today vs daily quota, and the quota reset time.

List the instance's API keys (metadata only; raw key values are never returned). Shows each key's name, creation time, last use, and whether it has been revoked.