How do I connect Qdrant to Lodol?

Authenticate Qdrant once from your workspace and every action and trigger it supports becomes available to drop into a workflow. There is no glue code to write or maintain.

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Qdrant

DatabasesAI Search & Memory

Store embeddings in Qdrant and search them: manage collections, upsert points with payloads, filter, and run vector and recommendation searches.

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20 actions

Actions

Steps your workflow can run in Qdrant.

List collectionsList every collection on the Qdrant cluster. Qdrant reports only their names here; use 'Get collection' for the size and configuration of one.

Get collectionRead one collection's status, how many points it holds, its vector configuration, and which payload keys are indexed (the keys a filter is allowed to use).

Collection existsCheck whether a collection exists. Answers true or false rather than failing, so a workflow can branch on it before creating or writing.

Create collectionCreate a collection to hold vectors. Vector size must match the embedding model you will store (for example 1536 for OpenAI text-embedding-3-small, 384 for all-MiniLM-L6-v2), and cannot be changed afterwards.

Delete collectionDelete a collection with every point in it, and every alias pointing at it. This cannot be undone.

Create payload indexIndex a payload key so points can be filtered and ordered by it. Qdrant Cloud refuses filters on unindexed keys, so run this once for every key your filters use.

Delete payload indexDrop the index on a payload key. The payload itself is left alone, but filters on that key stop working.

List aliasesList the aliases pointing at one collection.

Create aliasPoint an alias at a collection. Steps that name the alias follow it, so rebuilding a collection under a new name and re-pointing the alias swaps them over with no downtime. An alias that already points elsewhere is re-pointed.

Delete aliasRemove an alias. The collection it pointed at is untouched.

Upsert pointsAdd points to a collection, or overwrite points that are already there. Each point is an id (a whole number or a UUID), a vector, and an optional payload of anything you want to store or filter on.

Get pointsFetch points by id. Ids the collection does not hold come back under 'missing_ids' rather than failing the step.

Delete pointsDelete points from a collection, either by id or by filter. Give one or the other, not both.

Update vectorsReplace the vectors of points that already exist, leaving their payloads untouched. Unlike 'Upsert points' this never creates a point.

Set payloadMerge keys into the payload of the selected points. Keys you do not name are left as they are. Select the points by id or by filter, not both.

Delete payload keysRemove named keys from the payload of the selected points, keeping the rest of the payload and the vectors.

Clear payloadRemove the entire payload of the selected points, keeping their vectors. Select the points by id or by filter.

Scroll pointsPage through a collection's points without searching. Feed 'next_page_offset' from one run into 'Start after' on the next to get the following page.

Count pointsCount the points in a collection, or just the ones matching a filter.

Query pointsSearch a collection: for the points nearest a vector, nearest a point already stored, or most like a set of examples. Fill in one of the three - leaving all three blank returns points matching the filter in the collection's own order.