UNPKG

@n8n/n8n-nodes-langchain

Version:
61 lines (56 loc) 2.1 kB
/** * Supabase Vector Store Node - Version 1.2 * Discriminator: mode=insert */ interface Credentials { supabaseApi: CredentialReference; } /** Insert documents into vector store */ export type LcVectorStoreSupabaseV12InsertParams = { /** * Sits on the main flow — pipe the documents you want to embed into this node. Declare with `vectorStore({...})`. Required subnodes: `embedding` and `documentLoader`. If the goal is letting an LLM query the store, use `mode: 'retrieve-as-tool'` instead. * <patterns> * <pattern title="insert mode — upsert documents (generic, works for any vectorStore* node)"> * // Substitute the type literal and provider-specific parameters (e.g. pineconeIndex, * // qdrantCollection, supabaseTableName) — see the rest of this file for the exact shape. * const store = vectorStore({ * type: '@n8n/n8n-nodes-langchain.vectorStoreXxx', * config: { * name: 'Knowledge Base', * parameters: { * mode: 'insert', * // ...provider-specific parameters * }, * subnodes: { embedding: embeddingsOpenAi, documentLoader: defaultDataLoader } * } * }); * </pattern> * </patterns> */ mode: 'insert'; tableName?: { __rl: true; mode: 'list' | 'id'; value: string; cachedResultName?: string }; /** * Number of documents to embed in a single batch * @default 200 */ embeddingBatchSize?: number | Expression<number>; /** * Options * @default {} */ options?: { /** Name of the query to use for matching documents * @default match_documents */ queryName?: string | Expression<string>; }; }; export interface LcVectorStoreSupabaseV12InsertSubnodeConfig { embedding: EmbeddingInstance | EmbeddingInstance[]; documentLoader: DocumentLoaderInstance | DocumentLoaderInstance[]; } export type LcVectorStoreSupabaseV12InsertNode = { type: '@n8n/n8n-nodes-langchain.vectorStoreSupabase'; version: 1.2; config: NodeConfig<LcVectorStoreSupabaseV12InsertParams> & { credentials?: Credentials } & { subnodes: LcVectorStoreSupabaseV12InsertSubnodeConfig }; };