@n8n/n8n-nodes-langchain
Version:
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text/typescript
/**
* Milvus Vector Store Node - Version 1
* Discriminator: mode=retrieve
*/
interface Credentials {
milvusApi: CredentialReference;
}
/** Retrieve documents from vector store to be used as vector store with AI nodes */
export type LcVectorStoreMilvusV1RetrieveParams = {
/**
* Exposes the store as an `ai_vectorStore` subnode for another node (e.g. `toolVectorStore`). Declare with `vectorStore({...})`. Required subnodes: `embedding`. For RAG with an AI Agent directly, prefer `mode: 'retrieve-as-tool'`.
* <patterns>
* <pattern title="retrieve mode — feed another node as a subnode (generic)">
* // Substitute the type literal and provider-specific parameters — see the rest of this file.
* const store = vectorStore({
* type: '@n8n/n8n-nodes-langchain.vectorStoreXxx',
* config: {
* name: 'Knowledge Base',
* parameters: { mode: 'retrieve' /* + provider-specific parameters *\/ },
* subnodes: { embedding: embeddingsOpenAi }
* }
* });
*
* const retrieverTool = tool({
* type: '@n8n/n8n-nodes-langchain.toolVectorStore',
* config: {
* name: 'KB Retriever',
* parameters: { description: 'Search the product knowledge base' },
* subnodes: { vectorStore: store, model: openAiModel }
* }
* });
* </pattern>
* </patterns>
*/
mode: 'retrieve';
milvusCollection?: { __rl: true; mode: 'list' | 'id'; value: string; cachedResultName?: string };
/**
* Whether or not to rerank results
* @default false
*/
useReranker?: boolean | Expression<boolean>;
};
export interface LcVectorStoreMilvusV1RetrieveSubnodeConfig {
embedding: EmbeddingInstance | EmbeddingInstance[];
/**
* @displayOptions.show { useReranker: [true] }
*/
reranker: RerankerInstance;
}
export type LcVectorStoreMilvusV1RetrieveNode = {
type: '@n8n/n8n-nodes-langchain.vectorStoreMilvus';
version: 1;
config: NodeConfig<LcVectorStoreMilvusV1RetrieveParams> & { credentials?: Credentials } & { subnodes: LcVectorStoreMilvusV1RetrieveSubnodeConfig };
};