{"version":3,"file":"singlestore.cjs","names":["OrderingDirective: Record","config: SingleStoreVectorStoreConfig","newOptions: ConnectionOptions","result: ConnectionOptions","VectorStore","embeddings: EmbeddingsInterface","config: SearchConfig","documents: Document[]","vectors: number[][]","query: string","vector: number[]","k: number","filter?: Metadata","whereArgs: string[]","record: Metadata","argList: string[]","whereTokens: string[]","whereClauses: string[]","result: [Document, number][]","Document","k?: number","_callbacks?: Callbacks | undefined","query: number[]","texts: string[]","metadatas: object[]","dbConfig: SingleStoreVectorStoreConfig","docs: Document[]"],"sources":["../../src/vectorstores/singlestore.ts"],"sourcesContent":["import type {\n Pool,\n RowDataPacket,\n OkPacket,\n ResultSetHeader,\n FieldPacket,\n PoolOptions,\n} from \"mysql2/promise\";\nimport { format } from \"mysql2\";\nimport { createPool } from \"mysql2/promise\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport { Document, DocumentInterface } from \"@langchain/core/documents\";\nimport { Callbacks } from \"@langchain/core/callbacks/manager\";\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport type Metadata = Record;\n\nexport type DistanceMetrics = \"DOT_PRODUCT\" | \"EUCLIDEAN_DISTANCE\";\n\nexport type SearchStrategy =\n | \"VECTOR_ONLY\"\n | \"TEXT_ONLY\"\n | \"FILTER_BY_TEXT\"\n | \"FILTER_BY_VECTOR\"\n | \"WEIGHTED_SUM\";\n\nconst OrderingDirective: Record = {\n DOT_PRODUCT: \"DESC\",\n EUCLIDEAN_DISTANCE: \"\",\n};\n\nexport interface ConnectionOptions extends PoolOptions {}\n\ntype ConnectionWithUri = {\n connectionOptions?: never;\n connectionURI: string;\n};\n\ntype ConnectionWithOptions = {\n connectionURI?: never;\n connectionOptions: ConnectionOptions;\n};\n\ntype ConnectionConfig = ConnectionWithUri | ConnectionWithOptions;\n\ntype SearchConfig = {\n searchStrategy?: SearchStrategy;\n filterThreshold?: number;\n textWeight?: number;\n vectorWeight?: number;\n vectorselectCountMultiplier?: number;\n};\n\nexport type SingleStoreVectorStoreConfig = ConnectionConfig & {\n tableName?: string;\n idColumnName?: string;\n contentColumnName?: string;\n vectorColumnName?: string;\n metadataColumnName?: string;\n distanceMetric?: DistanceMetrics;\n useVectorIndex?: boolean;\n vectorIndexName?: string;\n vectorIndexOptions?: Metadata;\n vectorSize?: number;\n useFullTextIndex?: boolean;\n searchConfig?: SearchConfig;\n};\n\n/**\n * Adds the connect attributes to the connection options.\n * @param config A SingleStoreVectorStoreConfig object.\n */\nfunction withConnectAttributes(\n config: SingleStoreVectorStoreConfig\n): ConnectionOptions {\n let newOptions: ConnectionOptions = {};\n if (config.connectionURI) {\n newOptions = {\n uri: config.connectionURI,\n };\n } else if (config.connectionOptions) {\n newOptions = {\n ...config.connectionOptions,\n };\n }\n const result: ConnectionOptions = {\n ...newOptions,\n connectAttributes: {\n ...newOptions.connectAttributes,\n },\n };\n\n if (!result.connectAttributes) {\n result.connectAttributes = {};\n }\n\n result.connectAttributes = {\n ...result.connectAttributes,\n _connector_name: \"langchain js sdk\",\n _connector_version: \"2.0.0\",\n _driver_name: \"Node-MySQL-2\",\n };\n\n return result;\n}\n\n/**\n * Class for interacting with SingleStoreDB, a high-performance\n * distributed SQL database. It provides vector storage and vector\n * functions.\n */\nexport class SingleStoreVectorStore extends VectorStore {\n connectionPool: Pool;\n\n tableName: string;\n\n idColumnName: string;\n\n contentColumnName: string;\n\n vectorColumnName: string;\n\n metadataColumnName: string;\n\n distanceMetric: DistanceMetrics;\n\n useVectorIndex: boolean;\n\n vectorIndexName: string;\n\n vectorIndexOptions: Metadata;\n\n vectorSize: number;\n\n useFullTextIndex: boolean;\n\n searchConfig: SearchConfig;\n\n _vectorstoreType(): string {\n return \"singlestore\";\n }\n\n constructor(\n embeddings: EmbeddingsInterface,\n config: SingleStoreVectorStoreConfig\n ) {\n super(embeddings, config);\n this.connectionPool = createPool(withConnectAttributes(config));\n this.tableName = config.tableName ?? \"embeddings\";\n this.idColumnName = config.idColumnName ?? \"id\";\n this.contentColumnName = config.contentColumnName ?? \"content\";\n this.vectorColumnName = config.vectorColumnName ?? \"vector\";\n this.metadataColumnName = config.metadataColumnName ?? \"metadata\";\n this.distanceMetric = config.distanceMetric ?? \"DOT_PRODUCT\";\n this.useVectorIndex = config.useVectorIndex ?? false;\n this.vectorIndexName = config.vectorIndexName ?? \"\";\n this.vectorIndexOptions = config.vectorIndexOptions ?? {};\n this.vectorSize = config.vectorSize ?? 1536;\n this.useFullTextIndex = config.useFullTextIndex ?? false;\n this.searchConfig = config.searchConfig ?? {\n searchStrategy: \"VECTOR_ONLY\",\n filterThreshold: 1.0,\n textWeight: 0.5,\n vectorWeight: 0.5,\n vectorselectCountMultiplier: 10,\n };\n }\n\n /**\n * Creates a new table in the SingleStoreDB database if it does not\n * already exist.\n */\n async createTableIfNotExists(): Promise {\n let fullTextIndex = \"\";\n if (this.useFullTextIndex) {\n fullTextIndex = `, FULLTEXT(${this.contentColumnName})`;\n }\n if (this.useVectorIndex) {\n let vectorIndexOptions = \"\";\n if (Object.keys(this.vectorIndexOptions).length > 0) {\n vectorIndexOptions = `INDEX_OPTIONS '${JSON.stringify(\n this.vectorIndexOptions\n )}'`;\n }\n await this.connectionPool\n .execute(`CREATE TABLE IF NOT EXISTS ${this.tableName} (\n ${this.idColumnName} BIGINT AUTO_INCREMENT PRIMARY KEY,\n ${this.contentColumnName} LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n ${this.vectorColumnName} VECTOR(${this.vectorSize}, F32) NOT NULL,\n ${this.metadataColumnName} JSON,\n VECTOR INDEX ${this.vectorIndexName} (${this.vectorColumnName}) ${vectorIndexOptions}\n ${fullTextIndex});`);\n } else {\n await this.connectionPool\n .execute(`CREATE TABLE IF NOT EXISTS ${this.tableName} (\n ${this.idColumnName} BIGINT AUTO_INCREMENT PRIMARY KEY,\n ${this.contentColumnName} LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n ${this.vectorColumnName} BLOB,\n ${this.metadataColumnName} JSON\n ${fullTextIndex});`);\n }\n }\n\n /**\n * Ends the connection to the SingleStoreDB database.\n */\n async end(): Promise {\n return this.connectionPool.end();\n }\n\n /**\n * Sets the search configuration for the SingleStoreVectorStore instance.\n * @param config A SearchConfig object.\n */\n async setSearchConfig(config: SearchConfig): Promise {\n this.searchConfig = {\n searchStrategy: config.searchStrategy ?? \"VECTOR_ONLY\",\n filterThreshold: config.filterThreshold ?? 1.0,\n textWeight: config.textWeight ?? 0.5,\n vectorWeight: config.vectorWeight ?? 0.5,\n vectorselectCountMultiplier: config.vectorselectCountMultiplier ?? 10,\n };\n }\n\n /**\n * Adds new documents to the SingleStoreDB database.\n * @param documents An array of Document objects.\n */\n async addDocuments(documents: Document[]): Promise {\n const texts = documents.map(({ pageContent }) => pageContent);\n const vectors = await this.embeddings.embedDocuments(texts);\n return this.addVectors(vectors, documents);\n }\n\n /**\n * Adds new vectors to the SingleStoreDB database.\n * @param vectors An array of vectors.\n * @param documents An array of Document objects.\n */\n async addVectors(vectors: number[][], documents: Document[]): Promise {\n await this.createTableIfNotExists();\n const { tableName } = this;\n\n await Promise.all(\n vectors.map(async (vector, idx) => {\n try {\n await this.connectionPool.query(\n format(\n `INSERT INTO ${tableName}(\n ${this.contentColumnName},\n ${this.vectorColumnName},\n ${this.metadataColumnName})\n VALUES (?, JSON_ARRAY_PACK('[?]'), ?);`,\n [\n documents[idx].pageContent,\n vector,\n JSON.stringify(documents[idx].metadata),\n ]\n )\n );\n } catch (error) {\n console.error(`Error adding vector at index ${idx}:`, error);\n }\n })\n );\n if (this.useFullTextIndex || this.useVectorIndex) {\n await this.connectionPool.query(`OPTIMIZE TABLE ${tableName} FLUSH;`);\n }\n }\n\n /**\n *\n * Performs a similarity search on the texts stored in the SingleStoreDB\n * using the specified search strategy and distance metric.\n * @param query A string representing the query text.\n * @param vector An array of numbers representing the query vector.\n * @param k The number of nearest neighbors to return.\n * @param filter Optional metadata to filter the texts by.\n * @returns Top matching documents with score\n */\n async similaritySearchTextAndVectorWithScore(\n query: string,\n vector: number[],\n k: number,\n filter?: Metadata\n ): Promise<[Document, number][]> {\n if (!this.searchConfig.searchStrategy) {\n throw new Error(\"Search strategy is required.\");\n }\n if (\n this.searchConfig.searchStrategy !== \"VECTOR_ONLY\" &&\n !this.useFullTextIndex\n ) {\n throw new Error(\n \"Full text index is required for text-based search strategies.\"\n );\n }\n if (\n (this.searchConfig.searchStrategy === \"FILTER_BY_TEXT\" ||\n this.searchConfig.searchStrategy === \"FILTER_BY_VECTOR\") &&\n !this.searchConfig.filterThreshold &&\n this.searchConfig.filterThreshold !== 0\n ) {\n throw new Error(\n \"Filter threshold is required for filter-based search strategies.\"\n );\n }\n if (\n this.searchConfig.searchStrategy === \"WEIGHTED_SUM\" &&\n ((!this.searchConfig.textWeight && this.searchConfig.textWeight !== 0) ||\n (!this.searchConfig.vectorWeight &&\n this.searchConfig.vectorWeight !== 0) ||\n (!this.searchConfig.vectorselectCountMultiplier &&\n this.searchConfig.vectorselectCountMultiplier !== 0))\n ) {\n throw new Error(\n \"Text and vector weight and vector select count multiplier are required for weighted sum search strategy.\"\n );\n }\n if (\n this.searchConfig.searchStrategy === \"WEIGHTED_SUM\" &&\n this.distanceMetric !== \"DOT_PRODUCT\"\n ) {\n throw new Error(\n \"Weighted sum search strategy is only available for DOT_PRODUCT distance metric.\"\n );\n }\n const filterThreshold = this.searchConfig.filterThreshold ?? 1.0;\n // build the where clause from filter\n const whereArgs: string[] = [];\n const buildWhereClause = (record: Metadata, argList: string[]): string => {\n const whereTokens: string[] = [];\n for (const key in record)\n if (record[key] !== undefined) {\n if (\n typeof record[key] === \"object\" &&\n record[key] != null &&\n !Array.isArray(record[key])\n ) {\n whereTokens.push(\n buildWhereClause(record[key], argList.concat([key]))\n );\n } else {\n whereTokens.push(\n `JSON_EXTRACT_JSON(${this.metadataColumnName}, `.concat(\n Array.from({ length: argList.length + 1 }, () => \"?\").join(\n \", \"\n ),\n \") = ?\"\n )\n );\n whereArgs.push(...argList, key, JSON.stringify(record[key]));\n }\n }\n return whereTokens.join(\" AND \");\n };\n const filterByTextClause = (): string => {\n whereArgs.push(query, filterThreshold.toString());\n return `MATCH (${this.contentColumnName}) AGAINST (?) > ?`;\n };\n const filterByVectorClause = (): string => {\n whereArgs.push(JSON.stringify(vector), filterThreshold.toString());\n return this.distanceMetric === \"DOT_PRODUCT\"\n ? `${this.distanceMetric}(${this.vectorColumnName}, JSON_ARRAY_PACK(?)) > ?`\n : `${this.distanceMetric}(${this.vectorColumnName}, JSON_ARRAY_PACK(?)) < ?`;\n };\n const whereClauses: string[] = [];\n if (filter) {\n whereClauses.push(buildWhereClause(filter, []));\n }\n if (this.searchConfig.searchStrategy === \"FILTER_BY_TEXT\") {\n whereClauses.push(filterByTextClause());\n }\n if (this.searchConfig.searchStrategy === \"FILTER_BY_VECTOR\") {\n whereClauses.push(filterByVectorClause());\n }\n const whereClause =\n whereClauses.length > 0 ? `WHERE ${whereClauses.join(\" AND \")}` : \"\";\n\n let queryText = \"\";\n switch (this.searchConfig.searchStrategy) {\n case \"TEXT_ONLY\":\n case \"FILTER_BY_VECTOR\":\n queryText = format(\n `SELECT ${this.contentColumnName}, ${this.metadataColumnName},\n MATCH (${this.contentColumnName}) AGAINST (?) as __score\n FROM ${this.tableName} ${whereClause} ORDER BY __score DESC LIMIT ?;`,\n [query, ...whereArgs, k]\n );\n break;\n case \"VECTOR_ONLY\":\n case \"FILTER_BY_TEXT\":\n queryText = format(\n `SELECT ${this.contentColumnName}, ${this.metadataColumnName},\n ${this.distanceMetric}(${\n this.vectorColumnName\n }, JSON_ARRAY_PACK('[?]')) as __score\n FROM ${this.tableName} ${whereClause} ORDER BY __score ${\n OrderingDirective[this.distanceMetric]\n } LIMIT ?;`,\n [vector, ...whereArgs, k]\n );\n break;\n case \"WEIGHTED_SUM\":\n queryText = format(\n `SELECT ${this.contentColumnName}, ${\n this.metadataColumnName\n }, __score1 * ? + __score2 * ? as __score\n FROM (\n SELECT ${this.idColumnName}, ${this.contentColumnName}, ${\n this.metadataColumnName\n }, MATCH (${this.contentColumnName}) AGAINST (?) as __score1 \n FROM ${this.tableName} ${whereClause}) r1 FULL OUTER JOIN (\n SELECT ${this.idColumnName}, ${this.distanceMetric}(${\n this.vectorColumnName\n }, JSON_ARRAY_PACK('[?]')) as __score2\n FROM ${this.tableName} ${whereClause} ORDER BY __score2 ${\n OrderingDirective[this.distanceMetric]\n } LIMIT ?\n ) r2 ON r1.${this.idColumnName} = r2.${\n this.idColumnName\n } ORDER BY __score ${OrderingDirective[this.distanceMetric]} LIMIT ?`,\n [\n this.searchConfig.textWeight,\n this.searchConfig.vectorWeight,\n query,\n ...whereArgs,\n vector,\n ...whereArgs,\n k * (this.searchConfig.vectorselectCountMultiplier ?? 10),\n k,\n ]\n );\n break;\n default:\n throw new Error(\"Invalid search strategy.\");\n }\n const [rows]: [\n (\n | RowDataPacket[]\n | RowDataPacket[][]\n | OkPacket\n | OkPacket[]\n | ResultSetHeader\n ),\n FieldPacket[],\n ] = await this.connectionPool.query(queryText);\n const result: [Document, number][] = [];\n for (const row of rows as RowDataPacket[]) {\n const rowData = row as unknown as Record;\n result.push([\n new Document({\n pageContent: rowData[this.contentColumnName] as string,\n metadata: rowData[this.metadataColumnName] as Record,\n }),\n Number(rowData.score),\n ]);\n }\n return result;\n }\n\n /**\n * Performs a similarity search on the texts stored in the SingleStoreDB\n * @param query A string representing the query text.\n * @param k The number of nearest neighbors to return. By default, it is 4.\n * @param filter Optional metadata to filter the texts by.\n * @param _callbacks - Callbacks object, not used in this implementation.\n * @returns Top matching documents\n */\n async similaritySearch(\n query: string,\n k?: number,\n filter?: Metadata,\n _callbacks?: Callbacks | undefined\n ): Promise[]> {\n // @typescript-eslint/no-explicit-any\n const queryVector = await this.embeddings.embedQuery(query);\n return this.similaritySearchTextAndVectorWithScore(\n query,\n queryVector,\n k ?? 4,\n filter\n ).then((result) => result.map(([doc]) => doc));\n }\n\n /**\n * Performs a similarity search on the texts stored in the SingleStoreDB\n * @param query A string representing the query text.\n * @param k The number of nearest neighbors to return. By default, it is 4.\n * @param filter Optional metadata to filter the texts by.\n * @param _callbacks\n * @returns Top matching documents with score\n */\n async similaritySearchWithScore(\n query: string,\n k?: number,\n filter?: Metadata,\n _callbacks?: Callbacks | undefined\n ): Promise<[DocumentInterface, number][]> {\n // @typescript-eslint/no-explicit-any\n const queryVector = await this.embeddings.embedQuery(query);\n return this.similaritySearchTextAndVectorWithScore(\n query,\n queryVector,\n k ?? 4,\n filter\n );\n }\n\n /**\n * Performs a similarity search on the vectors stored in the SingleStoreDB\n * database.\n * @param query An array of numbers representing the query vector.\n * @param k The number of nearest neighbors to return.\n * @param filter Optional metadata to filter the vectors by.\n * @returns Top matching vectors with score\n */\n async similaritySearchVectorWithScore(\n query: number[],\n k: number,\n filter?: Metadata\n ): Promise<[Document, number][]> {\n if (this.searchConfig.searchStrategy !== \"VECTOR_ONLY\") {\n throw new Error(\n \"similaritySearchVectorWithScore is only available for VECTOR_ONLY search strategy.\"\n );\n }\n return this.similaritySearchTextAndVectorWithScore(\"\", query, k, filter);\n }\n\n /**\n * Creates a new instance of the SingleStoreVectorStore class from a list\n * of texts.\n * @param texts An array of strings.\n * @param metadatas An array of metadata objects.\n * @param embeddings An Embeddings object.\n * @param dbConfig A SingleStoreVectorStoreConfig object.\n * @returns A new SingleStoreVectorStore instance\n */\n static async fromTexts(\n texts: string[],\n metadatas: object[],\n embeddings: EmbeddingsInterface,\n dbConfig: SingleStoreVectorStoreConfig\n ): Promise {\n const docs = texts.map((text, idx) => {\n const metadata = Array.isArray(metadatas) ? metadatas[idx] : metadatas;\n return new Document({\n pageContent: text,\n metadata,\n });\n });\n return SingleStoreVectorStore.fromDocuments(docs, embeddings, dbConfig);\n }\n\n /**\n * Creates a new instance of the SingleStoreVectorStore class from a list\n * of Document objects.\n * @param docs An array of Document objects.\n * @param embeddings An Embeddings object.\n * @param dbConfig A SingleStoreVectorStoreConfig object.\n * @returns A new SingleStoreVectorStore instance\n */\n static async fromDocuments(\n docs: Document[],\n embeddings: EmbeddingsInterface,\n dbConfig: SingleStoreVectorStoreConfig\n ): Promise {\n const instance = new this(embeddings, dbConfig);\n await instance.addDocuments(docs);\n return instance;\n 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