/**
 * Zep Vector Store Node - Version 1
 * Discriminator: mode=insert
 */


interface Credentials {
  zepApi: CredentialReference;
}

/** Insert documents into vector store */
export type LcVectorStoreZepV1InsertParams = {
  /**
   * 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';
  collectionName: string | Expression<string>;
/**
 * Options
 * @default {}
 */
    options?: {
    /** Whether to allow using characters from the Unicode surrogate blocks
     * @default 1536
     */
    embeddingDimensions?: number | Expression<number>;
    /** Whether to automatically embed documents when they are added
     * @default true
     */
    isAutoEmbedded?: boolean | Expression<boolean>;
  };
};

export interface LcVectorStoreZepV1InsertSubnodeConfig {
  embedding: EmbeddingInstance | EmbeddingInstance[];
  documentLoader: DocumentLoaderInstance | DocumentLoaderInstance[];
}

export type LcVectorStoreZepV1InsertNode = {
  type: '@n8n/n8n-nodes-langchain.vectorStoreZep';
  version: 1;
  credentials?: Credentials;
  config: NodeConfig<LcVectorStoreZepV1InsertParams> & { subnodes: LcVectorStoreZepV1InsertSubnodeConfig };
};