nodes/generated

Interface VectorStorePGVectorNodeParameters

Source
interface VectorStorePGVectorNodeParameters {
    embeddingBatchSize?: number;
    id?: string;
    includeDocumentMetadata?: boolean;
    mode?:
        | "load"
        | "insert"
        | "retrieve"
        | "retrieve-as-tool";
    options?: | {
        collection?: {
            values: {
                collectionName: string;
                collectionTableName: string;
                useCollection?: boolean;
            };
        };
        columnNames?: {
            values: {
                contentColumnName: string;
                idColumnName: string;
                metadataColumnName: string;
                vectorColumnName: string;
            };
        };
    }
    | {
        collection?: {
            values: {
                collectionName: string;
                collectionTableName: string;
                useCollection?: boolean;
            };
        };
        columnNames?: {
            values: {
                contentColumnName: string;
                idColumnName: string;
                metadataColumnName: string;
                vectorColumnName: string;
            };
        };
        distanceStrategy?: | "cosine"
        | "innerProduct"
        | "euclidean";
        metadata?: {
            metadataValues: { name: string; value?: string }[];
        };
    };
    prompt?: string;
    tableName?: string;
    toolDescription?: string;
    toolName?: string;
    topK?: number;
    useReranker?: boolean;
}

Properties§

§readonly embeddingBatchSize?: number

Number of documents to embed in a single batch Default: 200

§readonly id?: string

ID of an embedding entry

§readonly includeDocumentMetadata?: boolean

Whether or not to include document metadata Default: true

§readonly mode?: "load" | "insert" | "retrieve" | "retrieve-as-tool"

Default: "retrieve"

§readonly options?:
    | {
        collection?: {
            values: {
                collectionName: string;
                collectionTableName: string;
                useCollection?: boolean;
            };
        };
        columnNames?: {
            values: {
                contentColumnName: string;
                idColumnName: string;
                metadataColumnName: string;
                vectorColumnName: string;
            };
        };
    }
    | {
        collection?: {
            values: {
                collectionName: string;
                collectionTableName: string;
                useCollection?: boolean;
            };
        };
        columnNames?: {
            values: {
                contentColumnName: string;
                idColumnName: string;
                metadataColumnName: string;
                vectorColumnName: string;
            };
        };
        distanceStrategy?: | "cosine"
        | "innerProduct"
        | "euclidean";
        metadata?: {
            metadataValues: { name: string; value?: string }[];
        };
    }

Default: {}

§readonly prompt?: string

Search prompt to retrieve matching documents from the vector store using similarity-based ranking

§readonly tableName?: string

The table name to store the vectors in. If table does not exist, it will be created. Default: "n8n_vectors"

§readonly toolDescription?: string

Explain to the LLM what this tool does, a good, specific description would allow LLMs to produce expected results much more often Type options: {"rows":2}

§readonly toolName?: string

Name of the vector store

§readonly topK?: number

Number of top results to fetch from vector store Default: 4

§readonly useReranker?: boolean

Whether or not to rerank results