/**
 * Redis Vector Store Node - Version 1.3
 * Discriminator: mode=load
 */


interface Credentials {
  redis: CredentialReference;
}

/** Get many ranked documents from vector store for query */
export type LcVectorStoreRedisV13LoadParams = {
  /**
   * One-shot similarity search on the main flow using the `prompt` parameter. Declare with `vectorStore({...})`. Required subnodes: `embedding`. For LLM-driven querying (RAG), use `mode: 'retrieve-as-tool'` instead.
   * <patterns>
   * <pattern title="load mode — one-shot similarity search (generic)">
   * // Substitute the type literal and provider-specific parameters — see the rest of this file.
   * const lookup = vectorStore({
   *   type: '@n8n/n8n-nodes-langchain.vectorStoreXxx',
   *   config: {
   *     name: 'Knowledge Base',
   *     parameters: {
   *       mode: 'load',
   *       prompt: expr('{{ $json.query }}'),
   *       // ...provider-specific parameters
   *     },
   *     subnodes: { embedding: embeddingsOpenAi }
   *   }
   * });
   * </pattern>
   * </patterns>
   */
  mode: 'load';
  redisIndex?: { __rl: true; mode: 'list' | 'id'; value: string; cachedResultName?: string };
/**
 * Search prompt to retrieve matching documents from the vector store using similarity-based ranking
 */
    prompt: string | Expression<string>;
/**
 * Number of top results to fetch from vector store
 * @default 4
 */
    topK?: number | Expression<number>;
/**
 * Whether or not to include document metadata
 * @default true
 */
    includeDocumentMetadata?: boolean | Expression<boolean>;
/**
 * Whether or not to rerank results
 * @default false
 */
    useReranker?: boolean | Expression<boolean>;
/**
 * Options
 * @default {}
 */
    options?: {
    /** The comma-separated list of words by which to apply additional full-text metadata filtering
     */
    metadataFilter?: string | Expression<string>;
    /** Prefix for Redis keys storing the documents
     */
    keyPrefix?: string | Expression<string>;
    /** 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>;
  };
};

export interface LcVectorStoreRedisV13LoadSubnodeConfig {
  embedding: EmbeddingInstance | EmbeddingInstance[];
  /**
   * @displayOptions.show { useReranker: [true] }
   */
  reranker: RerankerInstance;
}

export type LcVectorStoreRedisV13LoadNode = {
  type: '@n8n/n8n-nodes-langchain.vectorStoreRedis';
  version: 1.3;
  credentials?: Credentials;
  config: NodeConfig<LcVectorStoreRedisV13LoadParams> & { subnodes: LcVectorStoreRedisV13LoadSubnodeConfig };
};