RAG: Search (vector)

rag_search · action · Utility · Available · v1.0.0

Description

Semantic retrieval over the TENANT vector knowledge base: embeds the query (OpenAI/Voyage/Ollama, BYOK) and runs cosine KNN on the selected vector DB, returning the top-k most similar chunks as grounding context for an agent/LLM node. Strict tenant isolation.

⚙️ Configuration parameters

Fields shown in the editor when configuring the node. Generated directly from the NodeDefconfigFields.

Field	Type	Required	Default	Description
`databaseId` Vector DB	`db-picker`	yes	—	Un database con engine vettoriale (embedded o pgvector).
`collection` Collezione	`string`	yes	—	Nome della collezione vettoriale dentro il DB.
`query` Query	`string`	no	—	Testo da cercare. Vuoto = usa l'input del nodo precedente (stringa o {query}/{text}). Supporta {{espressioni}}.
`provider` Provider embedding	`enum` `openaivoyageollama`	no	`openai`	DEVE combaciare col provider/modello usato in ingest (stesso spazio vettoriale).
`model` Modello embedding	`string`	no	`text-embedding-3-small`	Stesso modello dell'ingest, altrimenti i vettori non sono confrontabili.
`apiKey` API key (BYOK)	`string`	no	—	Chiave del provider embedding (per openai/voyage). Ollama self-hosted non la richiede.
`topK` Top K risultati	`number`	no	`5`	—
`minScore` Similarità minima (0-1)	`number`	no	—	Scarta i risultati sotto questa soglia. Vuoto = nessuna soglia.
`filterJson` Filtro payload	`key-value`	no	—	Coppie chiave-valore: ritorna solo i chunk col payload corrispondente.

💡 Configuration example

JSON snippet of the node as it appears in the workflow. Values are derived fromdefaultValue and from required parameters.

{
  "id": "node-rag_search-1",
  "defId": "rag_search",
  "label": "RAG: Search (vector)",
  "config": {
    "databaseId": "<databaseId>",
    "collection": "<collection>",
    "provider": "openai",
    "model": "text-embedding-3-small",
    "topK": 5
  }
}

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