Contextual Compression Retriever

Node catalog Artificial intelligence

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Compress retrieved chunks with an LLM

Usage and specifics

With a model credential, this node compresses retrieved passages according to query. Without a usable credential, it uses keyword matching. Supply chunksSource and set topK.

Technical type : ai_contextual_compression_retriever. Runtime : nodulz.

Credentials and prerequisites

Create and configure the credential : ai-model

Create credentials in your nodulz instance’s Credentials area, then select them on the node. Availability depends on ownership and sharing. See the credentials guide for access and rotation.

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.