RAG: Index (vector)

rag_ingest · action · Utility · Available · v1.0.0

Description

Index content into the TENANT vector knowledge base: embeds the text (BYOK provider) and upserts into the vector DB, creating the collection on first use. Idempotent — without an explicit id a deterministic one is derived from content, so re-runs never duplicate.

⚙️ 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 (creata se non esiste).
`content` Contenuto da indicizzare	`string`	no	—	Testo da embeddizzare. Vuoto = usa l'input del nodo precedente (stringa o {content}/{text}).
`id` ID record	`string`	no	—	Vuoto = id deterministico dal contenuto (idempotenza). Fornisci un id stabile (es. {{input.docId}}) per aggiornare lo stesso record.
`provider` Provider embedding	`enum` `openaivoyageollama`	no	`openai`	—
`model` Modello embedding	`string`	no	`text-embedding-3-small`	Le dimensioni della collezione derivano da questo modello — non cambiarlo dopo il primo ingest.
`apiKey` API key (BYOK)	`string`	no	—	—
`distance` Distanza	`enum` `cosineeuclideandot`	no	`cosine`	—
`payloadJson` Metadati (payload)	`key-value`	no	—	Coppie chiave-valore salvate col chunk (es. source, lang). Il contenuto viene sempre incluso automaticamente.

💡 Configuration example

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

{
  "id": "node-rag_ingest-1",
  "defId": "rag_ingest",
  "label": "RAG: Index (vector)",
  "config": {
    "databaseId": "<databaseId>",
    "collection": "<collection>",
    "provider": "openai",
    "model": "text-embedding-3-small",
    "distance": "cosine"
  }
}

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