/**
 * Postgres PGVector Store Node - Version 1.1
 * Discriminator: mode=insert
 */


interface Credentials {
  postgres: CredentialReference;
}

/** Insert documents into vector store */
export type LcVectorStorePGVectorV11InsertParams = {
  /**
   * 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';
/**
 * The table name to store the vectors in. If table does not exist, it will be created.
 * @default n8n_vectors
 */
    tableName?: string | Expression<string>;
/**
 * Number of documents to embed in a single batch
 * @default 200
 */
    embeddingBatchSize?: number | Expression<number>;
/**
 * Options
 * @default {}
 */
    options?: {
    /** Collection of vectors
     * @default {"values":{"useCollection":false,"collectionName":"n8n","collectionTable":"n8n_vector_collections"}}
     */
    collection?: {
        /** Collection Settings
     */
    values?: {
      /** Use Collection
       * @default false
       */
      useCollection?: boolean | Expression<boolean>;
      /** Collection Name
       * @displayOptions.show { useCollection: [true] }
       * @default n8n
       */
      collectionName?: string | Expression<string>;
      /** Collection Table Name
       * @displayOptions.show { useCollection: [true] }
       * @default n8n_vector_collections
       */
      collectionTableName?: string | Expression<string>;
    };
  };
    /** The names of the columns in the PGVector table
     * @default {"values":{"idColumnName":"id","vectorColumnName":"embedding","contentColumnName":"text","metadataColumnName":"metadata"}}
     */
    columnNames?: {
        /** Column Name Settings
     */
    values?: {
      /** ID Column Name
       * @default id
       */
      idColumnName?: string | Expression<string>;
      /** Vector Column Name
       * @default embedding
       */
      vectorColumnName?: string | Expression<string>;
      /** Content Column Name
       * @default text
       */
      contentColumnName?: string | Expression<string>;
      /** Metadata Column Name
       * @default metadata
       */
      metadataColumnName?: string | Expression<string>;
    };
  };
  };
};

export interface LcVectorStorePGVectorV11InsertSubnodeConfig {
  embedding: EmbeddingInstance | EmbeddingInstance[];
  documentLoader: DocumentLoaderInstance | DocumentLoaderInstance[];
}

export type LcVectorStorePGVectorV11InsertNode = {
  type: '@n8n/n8n-nodes-langchain.vectorStorePGVector';
  version: 1.1;
  credentials?: Credentials;
  config: NodeConfig<LcVectorStorePGVectorV11InsertParams> & { subnodes: LcVectorStorePGVectorV11InsertSubnodeConfig };
};