nodes/generated

Interface VectorStoreSupabaseNodeParameters

Source


interface VectorStoreSupabaseNodeParameters {
    embeddingBatchSize?: number;
    id?: string;
    includeDocumentMetadata?: boolean;
    mode?:
        | "update"
        | "load"
        | "insert"
        | "retrieve"
        | "retrieve-as-tool";
    options?: | { queryName?: string }
    | {
        metadata?: {
            metadataValues: { name: string; value?: string }[];
        };
        queryName?: string;
    };
    prompt?: string;
    tableName?: { mode: "id"
    | "list"; value: 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?:
    | "update"
    | "load"
    | "insert"
    | "retrieve"
    | "retrieve-as-tool"

Default: "retrieve"

§readonly options?:
    | { queryName?: string }
    | {
        metadata?: {
            metadataValues: { name: string; value?: string }[];
        };
        queryName?: string;
    }

Default: {}

§readonly prompt?: string

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

§readonly tableName?: { ... }

Default: {"mode":"list","value":""}

§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