Vector Store Retriever

Node catalog Artificial intelligence

On this page

Retrieve chunks by vector similarity

Usage and specifics

documentsSource supplies the complete simple store output. queryVectorSource supplies the question embedding, such as nodes.question.vectors[0]. Keep the same model and dimensions as the documents. topK limits matches; a dimension mismatch causes an error.

Technical type : ai_vector_store_retriever. Runtime : nodulz.

Credentials and prerequisites

This node defines no dedicated external credential. Remote sources or execution targets may still need their own access credentials.

See the credentials guide if a remote source or execution target requires access.

Parameters

Names below are technical keys. Defaults are not a ready-to-run configuration: complete fields required by your operation. Paths containing [] describe list items; some fields depend on the selected mode.

Parameter	Type	Default	Values / limits
`query`	`string`	`""`	-
`topK`	`number`	`4`	-; ≥ 1, ≤ 1000
`searchMode`	`enum`	`"vector"`	`vector`, `hybrid`
`candidateK`	`number`	`20`	-; ≥ 1, ≤ 1000
`rerank`	`boolean`	`false`	-
`chunksSource`	`string`	`""`	-
`vectorsSource`	`string`	`""`	-
`documentsSource`	`string`	`""`	-
`credentialId`	`string`	-	-
`model`	`string`	`""`	-
`workflowId`	`string`	`""`	-

Initial configuration

Initial catalog configuration example, to adapt before running. Keep secrets in credentials.

json

{
  "query": "",
  "topK": 4,
  "searchMode": "vector",
  "candidateK": 20,
  "rerank": false,
  "chunksSource": "",
  "vectorsSource": "",
  "documentsSource": "",
  "model": "",
  "workflowId": ""
}

Verification

Test with representative data. Check node output in the inspector and logs, then use editor mapping for downstream fields. A trigger test with a simulated payload does not validate external service delivery.