interface VectorStoreAzureAISearchNodeParameters {
embeddingBatchSize?: number;
id?: string;
includeDocumentMetadata?: boolean;
indexName?: string;
mode?:
| "update"
| "load"
| "insert"
| "retrieve"
| "retrieve-as-tool";
options?: | {
clearIndex?: boolean;
metadataKeysToInsert?: string;
}
| {
filter?: string;
queryType?: "vector"
| "hybrid"
| "semanticHybrid";
semanticConfiguration?: string;
};
prompt?: string;
toolDescription?: string;
toolName?: string;
topK?: number;
useReranker?: boolean;
}Properties§
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readonly embedding Batch Size?: number§
readonly id?: stringID of an embedding entry
readonly include Document Metadata?: booleanWhether or not to include document metadata Default: true
readonly index Name?: stringThe name of the Azure AI Search index. Will be created automatically if it does not exist. Default: "n8n-vectorstore"
readonly mode?:
| "update"
| "load"
| "insert"
| "retrieve"
| "retrieve-as-tool"Default: "retrieve"
readonly options?:
| {
clearIndex?: boolean;
metadataKeysToInsert?: string;
}
| {
filter?: string;
queryType?: "vector"
| "hybrid"
| "semanticHybrid";
semanticConfiguration?: string;
}Default: {}
readonly prompt?: stringSearch prompt to retrieve matching documents from the vector store using similarity-based ranking
readonly tool Description?: stringExplain 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 tool Name?: stringName of the vector store
readonly top K?: numberNumber of top results to fetch from vector store Default: 4
readonly use Reranker?: booleanWhether or not to rerank results
Number of documents to embed in a single batch Default: 200