Work with rows

Rows are the working records inside an active dataset. Agents should inspect the dataset first, then read or change rows using the most stable identifier available.

Inspect first

Before row work, call:

get_dataset

The response gives the agent the headers, index column, instructions, schema, project context, relationships, and asset references.

List rows

Use listing when you know the dataset and need a bounded page:

list_dataset_rows

REST:

GET https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows

List rows supports pagination, text query, header filters, sort, and direction.

Search rows

Use profile-wide search when the relevant dataset is unknown:

search_rows

REST:

POST https://rowset.lvtd.dev/api/search

Use dataset search when you know the dataset and want ranked matches:

search_dataset_rows

REST:

POST https://rowset.lvtd.dev/api/datasets/{dataset_key}/search

Search uses hybrid vector and lexical retrieval when vector search is enabled. Rowset/Postgres remains the source of truth.

Read one row

Prefer index lookup when the dataset has a meaningful key:

get_dataset_row_by_index

REST:

GET https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows/by-index?index_value=TASK-001

Use internal row id when you already have it:

get_dataset_row

Create and update rows

Create:

create_dataset_row

REST:

POST https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows

Patch by index:

update_dataset_row_by_index

REST:

PATCH https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows/by-index?index_value=TASK-001

For retry-prone agent workflows, use a stable index, patch absolute final values, and read the row after an uncertain response. The idempotent AI-agent updates guide includes a complete create-or-update and timeout-recovery pattern. When a row points to another dataset, apply the referential-integrity contract: verify the target, write the parent first, store the exact index, and resolve the link after the mutation. For cleanup jobs, use the same exact-key contract inside a reversible AI data-cleaning workflow that separates raw rows, proposed changes, approval, and verified publication. For product and stock workflows, the AI inventory agent guide applies that contract to catalogs, source observations, action proposals, and destination read-back.

Patch by row id:

update_dataset_row

REST:

PATCH https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows/{row_id}

Delete rows

Deleting a row is destructive. Agents should ask first unless the user explicitly requested deletion.

delete_dataset_row

REST:

DELETE https://rowset.lvtd.dev/api/datasets/{dataset_key}/rows/{row_id}

For mistaken whole datasets, prefer archive and restore over deleting row by row.