nodes/generated

Interface VectorStoreMilvusNodeParameters

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interface VectorStoreMilvusNodeParameters {
    embeddingBatchSize?: number;
    id?: string;
    includeDocumentMetadata?: boolean;
    milvusCollection?: {
        mode: "id" | "list";
        value: string;
    };
    mode?: | "load"
    | "insert"
    | "retrieve"
    | "retrieve-as-tool";
    options?: { clearCollection?: boolean };
    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 milvusCollection?: { mode: "id" | "list"; value: string }

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

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

Default: "retrieve"

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readonly options?: { clearCollection?: boolean }

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