{"version":3,"file":"mariadb.cjs","names":["VectorStore","embeddings: EmbeddingsInterface","config: MariaDBStoreArgs","identifier: string","alwaysQuote: boolean","definition: string","config: MariaDBStoreArgs & { dimensions?: number }","texts: string[]","metadatas: object[] | object","dbConfig: MariaDBStoreArgs & { dimensions?: number }","Document","docs: Document[]","documents: Document[]","options?: { ids?: string[] }","vectors: number[][]","vector: number[]","params: {\n ids?: string[];\n filter?: Record;\n }","params","filter?: Record","_filter: Record","parameters: any[]","filter: Record","groupOperator: string","_value: Record","query: number[]","k: number","parameters: unknown[]"],"sources":["../../src/vectorstores/mariadb.ts"],"sourcesContent":["/* eslint-disable @typescript-eslint/no-explicit-any */\nimport mariadb, { type Pool, type PoolConfig } from \"mariadb\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { Document } from \"@langchain/core/documents\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\n\ntype Metadata = Record;\n\nexport type DistanceStrategy = \"COSINE\" | \"EUCLIDEAN\";\n\nconst STANDARD_SIMPLE_OPERATOR = new Map([\n [\"$eq\", \"=\"],\n [\"$ne\", \"!=\"],\n [\"$lt\", \"<\"],\n [\"$lte\", \"<=\"],\n [\"$gt\", \">\"],\n [\"$gte\", \">=\"],\n]);\n\nconst STANDARD_LIST_OPERATOR = new Map([\n [\"$in\", \"in\"],\n [\"$nin\", \"not in\"],\n]);\n\nconst STANDARD_BETWEEN_OPERATOR = new Map([\n [\"$like\", \"like\"],\n [\"$nlike\", \"no like\"],\n]);\n\nconst GROUP_OPERATORS = new Map([\n [\"$or\", \"or\"],\n [\"$and\", \"and\"],\n [\"$not\", \"not\"],\n]);\n\nconst SUPPORTED_OPERATORS = new Map([\n ...STANDARD_SIMPLE_OPERATOR,\n ...STANDARD_LIST_OPERATOR,\n ...STANDARD_BETWEEN_OPERATOR,\n ...GROUP_OPERATORS,\n]);\n\n/**\n * Interface that defines the arguments required to create a\n * `MariaDBStore` instance. It includes MariaDB connection options,\n * table name and verbosity level.\n */\nexport interface MariaDBStoreArgs {\n connectionOptions?: PoolConfig;\n pool?: Pool;\n tableName?: string;\n collectionTableName?: string;\n collectionName?: string;\n collectionMetadata?: Metadata | null;\n schemaName?: string | null;\n columns?: {\n idColumnName?: string;\n vectorColumnName?: string;\n contentColumnName?: string;\n metadataColumnName?: string;\n };\n verbose?: boolean;\n /**\n * The amount of documents to chunk by when\n * adding vectors.\n * @default 500\n */\n chunkSize?: number;\n ids?: string[];\n distanceStrategy?: DistanceStrategy;\n}\n\n/**\n * MariaDB vector store integration.\n *\n * Setup:\n * Install `@langchain/community` and `mariadb`.\n *\n * If you wish to generate ids, you should also install the `uuid` package.\n *\n * ```bash\n * npm install @langchain/community mariadb uuid\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.vectorstores_mariadb.MariaDB.html#constructor)\n *\n *
\n * Instantiate\n *\n * ```typescript\n * import {\n * MariaDBStore,\n * DistanceStrategy,\n * } from \"@langchain/community/vectorstores/mariadb\";\n *\n * // Or other embeddings\n * import { OpenAIEmbeddings } from \"@langchain/openai\";\n * import { PoolConfig } from \"mariadb\";\n *\n * const embeddings = new OpenAIEmbeddings({\n * model: \"text-embedding-3-small\",\n * });\n *\n * // Sample config\n * const config = {\n * connectionOptions: {\n * host: \"127.0.0.1\",\n * port: 3306,\n * user: \"myuser\",\n * password: \"ChangeMe\",\n * database: \"api\",\n * } as PoolConfig,\n * tableName: \"testlangchainjs\",\n * columns: {\n * idColumnName: \"id\",\n * vectorColumnName: \"vector\",\n * contentColumnName: \"content\",\n * metadataColumnName: \"metadata\",\n * },\n * // supported distance strategies: COSINE (default) or EUCLIDEAN\n * distanceStrategy: \"COSINE\" as DistanceStrategy,\n * };\n *\n * const vectorStore = await MariaDBStore.initialize(embeddings, config);\n * ```\n *
\n *\n *
\n *\n *
\n * Add documents\n *\n * ```typescript\n * import type { Document } from '@langchain/core/documents';\n *\n * const document1 = { pageContent: \"foo\", metadata: { baz: \"bar\" } };\n * const document2 = { pageContent: \"thud\", metadata: { bar: \"baz\" } };\n * const document3 = { pageContent: \"i will be deleted :(\", metadata: {} };\n *\n * const documents: Document[] = [document1, document2, document3];\n * const ids = [\"1\", \"2\", \"3\"];\n * await vectorStore.addDocuments(documents, { ids });\n * ```\n *
\n *\n *
\n *\n *
\n * Delete documents\n *\n * ```typescript\n * await vectorStore.delete({ ids: [\"3\"] });\n * ```\n *
\n *\n *
\n *\n *
\n * Similarity search\n *\n * ```typescript\n * const results = await vectorStore.similaritySearch(\"thud\", 1);\n * for (const doc of results) {\n * console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * thud [{\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *\n *
\n * Similarity search with filter\n *\n * ```typescript\n * const resultsWithFilter = await vectorStore.similaritySearch(\"thud\", 1, {\"country\": \"BG\"});\n *\n * for (const doc of resultsWithFilter) {\n * console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * foo [{\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *\n *
\n * Similarity search with score\n *\n * ```typescript\n * const resultsWithScore = await vectorStore.similaritySearchWithScore(\"qux\", 1);\n * for (const [doc, score] of resultsWithScore) {\n * console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);\n * }\n * // Output: * [SIM=0.000000] qux [{\"bar\":\"baz\",\"baz\":\"bar\"}]\n * ```\n *
\n *\n *
\n *\n *
\n * As a retriever\n *\n * ```typescript\n * const retriever = vectorStore.asRetriever({\n * searchType: \"mmr\", // Leave blank for standard similarity search\n * k: 1,\n * });\n * const resultAsRetriever = await retriever.invoke(\"thud\");\n * console.log(resultAsRetriever);\n *\n * // Output: [Document({ metadata: { \"baz\":\"bar\" }, pageContent: \"thud\" })]\n * ```\n *
\n *\n *
\n */\nexport class MariaDBStore extends VectorStore {\n tableName: string;\n\n collectionTableName?: string;\n\n collectionName = \"langchain\";\n\n collectionId?: string;\n\n collectionMetadata: Metadata | null;\n\n schemaName: string | null;\n\n idColumnName: string;\n\n vectorColumnName: string;\n\n contentColumnName: string;\n\n metadataColumnName: string;\n\n _verbose?: boolean;\n\n pool: Pool;\n\n chunkSize = 500;\n\n distanceStrategy: DistanceStrategy;\n\n constructor(embeddings: EmbeddingsInterface, config: MariaDBStoreArgs) {\n super(embeddings, config);\n this.tableName = this.escapeId(config.tableName ?? \"langchain\", false);\n if (\n config.collectionName !== undefined &&\n config.collectionTableName === undefined\n ) {\n throw new Error(\n `If supplying a \"collectionName\", you must also supply a \"collectionTableName\".`\n );\n }\n\n this.collectionTableName = config.collectionTableName\n ? this.escapeId(config.collectionTableName, false)\n : undefined;\n\n this.collectionName = config.collectionName\n ? this.escapeId(config.collectionName, false)\n : \"langchaincol\";\n\n this.collectionMetadata = config.collectionMetadata ?? null;\n this.schemaName = config.schemaName\n ? this.escapeId(config.schemaName, false)\n : null;\n\n this.vectorColumnName = this.escapeId(\n config.columns?.vectorColumnName ?? \"embedding\",\n false\n );\n this.contentColumnName = this.escapeId(\n config.columns?.contentColumnName ?? \"text\",\n false\n );\n this.idColumnName = this.escapeId(\n config.columns?.idColumnName ?? \"id\",\n false\n );\n this.metadataColumnName = this.escapeId(\n config.columns?.metadataColumnName ?? \"metadata\",\n false\n );\n\n if (!config.connectionOptions && !config.pool) {\n throw new Error(\n \"You must provide either a `connectionOptions` object or a `pool` instance.\"\n );\n }\n\n const langchainVerbose = getEnvironmentVariable(\"LANGCHAIN_VERBOSE\");\n\n if (langchainVerbose === \"true\") {\n this._verbose = true;\n } else if (langchainVerbose === \"false\") {\n this._verbose = false;\n } else {\n this._verbose = config.verbose;\n }\n\n if (config.pool) {\n this.pool = config.pool;\n } else {\n const poolConf = { ...config.connectionOptions, rowsAsArray: true };\n // add query to log if verbose\n if (this._verbose) poolConf.logger = { query: console.log };\n this.pool = mariadb.createPool(poolConf);\n }\n this.chunkSize = config.chunkSize ?? 500;\n\n this.distanceStrategy =\n config.distanceStrategy ?? (\"COSINE\" as DistanceStrategy);\n }\n\n get computedTableName() {\n return this.schemaName == null\n ? this.tableName\n : `${this.schemaName}.${this.tableName}`;\n }\n\n get computedCollectionTableName() {\n return this.schemaName == null\n ? `${this.collectionTableName}`\n : `\"${this.schemaName}\".\"${this.collectionTableName}\"`;\n }\n\n /**\n * Escape identifier\n *\n * @param identifier identifier value\n * @param alwaysQuote must identifier be quoted if not required\n */\n private escapeId(identifier: string, alwaysQuote: boolean): string {\n if (!identifier || identifier === \"\")\n throw new Error(\"Identifier is required\");\n\n const len = identifier.length;\n const simpleIdentifier = /^[0-9a-zA-Z$_]*$/;\n if (simpleIdentifier.test(identifier)) {\n if (len < 1 || len > 64) {\n throw new Error(\"Invalid identifier length\");\n }\n if (alwaysQuote) return `\\`${identifier}\\``;\n\n // Identifier names may begin with a numeral, but can't only contain numerals unless quoted.\n if (/^\\d+$/.test(identifier)) {\n // identifier containing only numerals must be quoted\n return `\\`${identifier}\\``;\n }\n // identifier containing only numerals must be quoted\n return identifier;\n } else {\n if (identifier.includes(\"\\u0000\")) {\n throw new Error(\"Invalid name - containing u0000 character\");\n }\n let ident = identifier;\n if (/^`.+`$/.test(identifier)) {\n ident = identifier.substring(1, identifier.length - 1);\n }\n if (len < 1 || len > 64) {\n throw new Error(\"Invalid identifier length\");\n }\n return `\\`${ident.replace(/`/g, \"``\")}\\``;\n }\n }\n\n private printable(definition: string): string {\n return definition.replaceAll(/[^0-9a-zA-Z_]/g, \"\");\n }\n\n /**\n * Static method to create a new `MariaDBStore` instance from a\n * connection. It creates a table if one does not exist, and calls\n * `connect` to return a new instance of `MariaDBStore`.\n *\n * @param embeddings - Embeddings instance.\n * @param fields - `MariaDBStoreArgs` instance\n * @param fields.dimensions Number of dimensions in your vector data type. default to 1536.\n * @returns A new instance of `MariaDBStore`.\n */\n static async initialize(\n embeddings: EmbeddingsInterface,\n config: MariaDBStoreArgs & { dimensions?: number }\n ): Promise {\n const { dimensions, ...rest } = config;\n const mariadbStore = new MariaDBStore(embeddings, rest);\n await mariadbStore.ensureTableInDatabase(dimensions);\n await mariadbStore.ensureCollectionTableInDatabase();\n await mariadbStore.loadCollectionId();\n\n return mariadbStore;\n }\n\n /**\n * Static method to create a new `MariaDBStore` instance from an\n * array of texts and their metadata. It converts the texts into\n * `Document` instances and adds them to the store.\n *\n * @param texts - Array of texts.\n * @param metadatas - Array of metadata objects or a single metadata object.\n * @param embeddings - Embeddings instance.\n * @param dbConfig - `MariaDBStoreArgs` instance.\n * @returns Promise that resolves with a new instance of `MariaDBStore`.\n */\n static async fromTexts(\n texts: string[],\n metadatas: object[] | object,\n embeddings: EmbeddingsInterface,\n dbConfig: MariaDBStoreArgs & { dimensions?: number }\n ): Promise {\n const docs = [];\n for (let i = 0; i < texts.length; i += 1) {\n const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;\n const newDoc = new Document({\n pageContent: texts[i],\n metadata,\n });\n docs.push(newDoc);\n }\n\n return MariaDBStore.fromDocuments(docs, embeddings, dbConfig);\n }\n\n /**\n * Static method to create a new `MariaDBStore` instance from an\n * array of `Document` instances. It adds the documents to the store.\n *\n * @param docs - Array of `Document` instances.\n * @param embeddings - Embeddings instance.\n * @param dbConfig - `MariaDBStoreArgs` instance.\n * @returns Promise that resolves with a new instance of `MariaDBStore`.\n */\n static async fromDocuments(\n docs: Document[],\n embeddings: EmbeddingsInterface,\n dbConfig: MariaDBStoreArgs & { dimensions?: number }\n ): Promise {\n const instance = await MariaDBStore.initialize(embeddings, dbConfig);\n await instance.addDocuments(docs, { ids: dbConfig.ids });\n return instance;\n }\n\n _vectorstoreType(): string {\n return \"mariadb\";\n }\n\n /**\n * Method to add documents to the vector store. It converts the documents into\n * vectors, and adds them to the store.\n *\n * @param documents - Array of `Document` instances.\n * @param options - Optional arguments for adding documents\n * @returns Promise that resolves when the documents have been added.\n */\n async addDocuments(\n documents: Document[],\n options?: { ids?: string[] }\n ): Promise {\n const texts = documents.map(({ pageContent }) => pageContent);\n\n return this.addVectors(\n await this.embeddings.embedDocuments(texts),\n documents,\n options\n );\n }\n\n /**\n * Inserts a row for the collectionName provided at initialization if it does not\n * exist and set the collectionId.\n */\n private async loadCollectionId(): Promise {\n if (this.collectionId) {\n return;\n }\n\n if (this.collectionTableName) {\n const queryResult = await this.pool.query(\n {\n sql: `SELECT uuid from ${this.computedCollectionTableName} WHERE label = ?`,\n rowsAsArray: true,\n },\n [this.collectionName]\n );\n if (queryResult.length > 0) {\n this.collectionId = queryResult[0][0];\n } else {\n const insertString = `INSERT INTO ${this.computedCollectionTableName}(label, cmetadata) VALUES (?, ?) RETURNING uuid`;\n const insertResult = await this.pool.query(\n { sql: insertString, rowsAsArray: true },\n [this.collectionName, this.collectionMetadata]\n );\n this.collectionId = insertResult[0][0];\n }\n }\n }\n\n /**\n * Method to add vectors to the vector store. It converts the vectors into\n * rows and inserts them into the database.\n *\n * @param vectors - Array of vectors.\n * @param documents - Array of `Document` instances.\n * @param options - Optional arguments for adding documents\n * @returns Promise that resolves when the vectors have been added.\n */\n async addVectors(\n vectors: number[][],\n documents: Document[],\n options?: { ids?: string[] }\n ): Promise {\n const ids = options?.ids;\n\n // Either all documents have ids or none of them do to avoid confusion.\n if (ids !== undefined && ids.length !== vectors.length) {\n throw new Error(\n \"The number of ids must match the number of vectors provided.\"\n );\n }\n await this.loadCollectionId();\n\n const insertQuery = `INSERT INTO ${this.computedTableName}(${\n this.idColumnName\n },${this.contentColumnName},${this.metadataColumnName},${\n this.vectorColumnName\n }${this.collectionId ? \",collection_id\" : \"\"}) VALUES (${\n ids ? \"?\" : \"UUID_v7()\"\n }, ?, ?, ?${this.collectionId ? \", ?\" : \"\"})`;\n\n try {\n const batchParams = [];\n for (let i = 0; i < vectors.length; i += 1) {\n const param = [\n ids ? ids[i] : null,\n documents[i].pageContent,\n documents[i].metadata,\n this.getFloat32Buffer(vectors[i]),\n this.collectionId,\n ];\n if (!ids) param.shift();\n if (!this.collectionId) param.pop();\n batchParams.push(param);\n }\n await this.pool.batch(insertQuery, batchParams);\n } catch (e) {\n console.error(e);\n throw new Error(`Error inserting: ${(e as Error).message}`);\n }\n }\n\n /**\n * Convert float array to binary value\n * @param vector embedding value\n * @private\n */\n private getFloat32Buffer(vector: number[]) {\n return Buffer.from(new Float32Array(vector).buffer);\n }\n\n /**\n * Method to delete documents from the vector store. It deletes the\n * documents that match the provided ids\n *\n * @param ids - array of ids\n * @returns Promise that resolves when the documents have been deleted.\n * @example\n * await vectorStore.delete([\"id1\", \"id2\"]);\n */\n async delete(params: {\n ids?: string[];\n filter?: Record;\n }): Promise {\n const { ids, filter } = params;\n\n if (!(ids || filter)) {\n throw new Error(\n \"You must specify either ids or a filter when deleting documents.\"\n );\n }\n await this.loadCollectionId();\n\n if (ids) {\n // delete by ids\n await this.pool.query(\n `DELETE FROM ${this.computedTableName} WHERE ${\n this.idColumnName\n } IN (?) ${this.collectionId ? \" AND collection_id = ?\" : \"\"}`,\n [ids, this.collectionId]\n );\n } else if (filter) {\n // delete by filter\n const [filterPart, params] = this.filterConverter(filter);\n if (filterPart.length === 0) throw new Error(\"Wrong filter.\");\n await this.pool.query(\n `DELETE FROM ${this.computedTableName} WHERE ${filterPart} ${\n this.collectionId ? \" AND collection_id = ?\" : \"\"\n }`,\n [...params, this.collectionId]\n );\n }\n }\n\n private filterConverter(filter?: Record): [string, any[]] {\n if (!filter) return [\"\", []];\n const _filter: Record = filter ?? {};\n const parameters: any[] = [];\n let sqlFilter = this.subFilterConverter(_filter, parameters, \"$and\");\n if (sqlFilter.charAt(0) === \"(\") {\n sqlFilter = sqlFilter.substring(1, sqlFilter.length - 1);\n }\n return [sqlFilter, parameters];\n }\n\n private subFilterConverter(\n filter: Record,\n parameters: any[],\n groupOperator: string\n ): string {\n const sqlFilterPart = [];\n\n for (const [key, value] of Object.entries(filter)) {\n if (typeof value === \"object\" && value !== null) {\n const _value: Record = value;\n for (const [type, subvalue] of Object.entries(_value)) {\n let realvalue = subvalue;\n if (STANDARD_LIST_OPERATOR.has(type)) {\n if (!Array.isArray(realvalue)) {\n if (\n typeof realvalue !== \"string\" ||\n typeof realvalue !== \"number\"\n ) {\n throw new Error(\n \"value for in/not in filter are expected to be an array type\"\n );\n }\n realvalue = [realvalue];\n }\n\n const placeholders = realvalue.map(() => \"?\").join(\",\");\n sqlFilterPart.push(\n `JSON_VALUE(${\n this.metadataColumnName\n }, '$.${key}') ${STANDARD_LIST_OPERATOR.get(\n type\n )} (${placeholders})`\n );\n parameters.push(...realvalue);\n } else if (GROUP_OPERATORS.has(type)) {\n sqlFilterPart.push(\n this.subFilterConverter(realvalue, parameters, type)\n );\n } else if (SUPPORTED_OPERATORS.has(type)) {\n sqlFilterPart.push(\n `JSON_VALUE(${\n this.metadataColumnName\n }, '$.${key}') ${SUPPORTED_OPERATORS.get(type)} ?`\n );\n parameters.push(realvalue);\n } else {\n throw new Error(\n `unknown type operation, must be in ${SUPPORTED_OPERATORS.keys()}`\n );\n }\n }\n } else {\n sqlFilterPart.push(\n `JSON_VALUE(${this.metadataColumnName}, '$.${key}') = ?`\n );\n parameters.push(value);\n }\n }\n if (sqlFilterPart.length > 1) {\n return `(${sqlFilterPart.join(\n ` ${GROUP_OPERATORS.get(groupOperator)} `\n )})`;\n } else {\n return sqlFilterPart[0];\n }\n }\n\n /**\n * Method to perform a similarity search in the vector store. It returns\n * the `k` most similar documents to the query vector, along with their\n * similarity scores.\n *\n * @param query - Query vector.\n * @param k - Number of most similar documents to return.\n * @param filter - Optional filter to apply to the search.\n * @returns Promise that resolves with an array of tuples, each containing a `Document` and its similarity score.\n */\n async similaritySearchVectorWithScore(\n query: number[],\n k: number,\n filter?: Record\n ): Promise<[Document, number][]> {\n const parameters: unknown[] = [this.getFloat32Buffer(query)];\n const whereClauses = [];\n\n await this.loadCollectionId();\n\n if (this.collectionId) {\n whereClauses.push(\"collection_id = ?\");\n parameters.push(this.collectionId);\n }\n\n if (filter) {\n const [filterPart, params] = this.filterConverter(filter);\n whereClauses.push(filterPart);\n parameters.push(...params);\n }\n\n // limit\n parameters.push(k);\n\n const whereClause = whereClauses.length\n ? `WHERE ${whereClauses.join(\" AND \")}`\n : \"\";\n\n const queryString = `SELECT ${this.idColumnName},${this.contentColumnName},${this.metadataColumnName},VEC_DISTANCE_${this.distanceStrategy}(${this.vectorColumnName}, ?) as distance FROM ${this.computedTableName} ${whereClause} ORDER BY distance ASC LIMIT ?`;\n\n const documents = await this.pool.execute(\n { sql: queryString, rowsAsArray: true },\n parameters\n );\n\n const results = [] as [Document, number][];\n for (const doc of documents) {\n if (doc[3] != null && doc[1] != null) {\n const document = new Document({\n id: doc[0],\n pageContent: doc[1],\n metadata: doc[2],\n });\n results.push([document, doc[3]]);\n }\n }\n return results;\n }\n\n /**\n * Method to ensure the existence of the table in the database. It creates\n * the table if it does not already exist.\n * @param dimensions Number of dimensions in your vector data type. Default to 1536.\n * @returns Promise that resolves when the table has been ensured.\n */\n async ensureTableInDatabase(dimensions = 1536): Promise {\n const tableQuery = `CREATE TABLE IF NOT EXISTS ${this.computedTableName}(${\n this.idColumnName\n } UUID NOT NULL DEFAULT UUID_v7() PRIMARY KEY,${\n this.contentColumnName\n } TEXT,${this.metadataColumnName} JSON,${\n this.vectorColumnName\n } VECTOR(${dimensions}) NOT NULL, VECTOR INDEX ${this.printable(\n `${this.tableName}_${this.vectorColumnName}`\n )}_idx (${this.vectorColumnName}) ) ENGINE=InnoDB`;\n await this.pool.query(tableQuery);\n }\n\n /**\n * Method to ensure the existence of the collection table in the database.\n * It creates the table if it does not already exist.\n *\n * @returns Promise that resolves when the collection table has been ensured.\n */\n async ensureCollectionTableInDatabase(): Promise {\n try {\n if (this.collectionTableName != null) {\n await Promise.all([\n this.pool.query(\n `CREATE TABLE IF NOT EXISTS ${\n this.computedCollectionTableName\n }(uuid UUID NOT NULL DEFAULT UUID_v7() PRIMARY KEY,\n label VARCHAR(256), cmetadata JSON, UNIQUE KEY idx_${this.printable(\n this.collectionTableName\n )}_label\n (label))`\n ),\n this.pool.query(\n `ALTER TABLE ${this.computedTableName}\n ADD COLUMN IF NOT EXISTS collection_id uuid,\n ADD CONSTRAINT FOREIGN KEY IF NOT EXISTS ${this.printable(\n this.tableName\n )}_collection_id_fkey (collection_id)\n REFERENCES ${\n this.computedCollectionTableName\n }(uuid) ON DELETE CASCADE`\n ),\n ]);\n }\n } catch (e) {\n console.error(e);\n throw new Error(\n `Error adding column or creating index: ${(e as Error).message}`\n );\n }\n }\n\n /**\n * Close the pool.\n *\n * @returns Promise that resolves when the pool is terminated.\n */\n async end(): Promise {\n return this.pool.end();\n 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