nodes/generated

Interface VectorStoreAzureAISearchNodeParameters

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interface VectorStoreAzureAISearchNodeParameters {
    embeddingBatchSize?: number;
    id?: string;
    includeDocumentMetadata?: boolean;
    indexName?: string;
    mode?:
        | "update"
        | "load"
        | "insert"
        | "retrieve"
        | "retrieve-as-tool";
    options?: | { clearIndex?: boolean }
    | {
        filter?: string;
        queryType?: "vector" | "hybrid" | "semanticHybrid";
        semanticConfiguration?: string;
    };
    prompt?: string;
    toolDescription?: string;
    toolName?: string;
    topK?: number;
    useReranker?: boolean;
}

Properties§

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readonly embeddingBatchSize?: number

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

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readonly id?: string

ID of an embedding entry

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readonly includeDocumentMetadata?: boolean

Whether or not to include document metadata Default: true

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readonly indexName?: string

The name of the Azure AI Search index. Will be created automatically if it does not exist. Default: "n8n-vectorstore"

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

Default: "retrieve"

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readonly options?:
    | { clearIndex?: boolean }
    | {
        filter?: string;
        queryType?: "vector" | "hybrid" | "semanticHybrid";
        semanticConfiguration?: string;
    }

Default: {}

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readonly prompt?: string

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

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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}

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readonly toolName?: string

Name of the vector store

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readonly topK?: number

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

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readonly useReranker?: boolean

Whether or not to rerank results