@n8n/n8n-nodes-langchain

Version:
76 lines (71 loc) • 2.67 kB
text/typescript
/**
 * Redis Vector Store Node - Version 1.2
 * Discriminator: mode=insert
 */


interface Credentials {
  redis: CredentialReference;
}

/** Insert documents into vector store */
export type LcVectorStoreRedisV12InsertParams = {
  /**
   * 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';
  redisIndex?: { __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?: {
    /** Prefix for Redis keys storing the documents
     */
    keyPrefix?: string | Expression<string>;
    /** Whether existing documents and the index should be overwritten
     * @default false
     */
    overwriteDocuments?: boolean | Expression<boolean>;
    /** The hash key to be used to store the metadata of the document
     */
    metadataKey?: string | Expression<string>;
    /** The hash key to be used to store the content of the document
     */
    contentKey?: string | Expression<string>;
    /** The hash key to be used to store the embedding of the document
     */
    vectorKey?: string | Expression<string>;
    /** Time-to-live for the documents in seconds
     */
    ttl?: number | Expression<number>;
  };
};

export interface LcVectorStoreRedisV12InsertSubnodeConfig {
  embedding: EmbeddingInstance | EmbeddingInstance[];
  documentLoader: DocumentLoaderInstance | DocumentLoaderInstance[];
}

export type LcVectorStoreRedisV12InsertNode = {
  type: '@n8n/n8n-nodes-langchain.vectorStoreRedis';
  version: 1.2;
  config: NodeConfig<LcVectorStoreRedisV12InsertParams> & { credentials?: Credentials } & { subnodes: LcVectorStoreRedisV12InsertSubnodeConfig };
};