@langchain/community/dist/embeddings/alibaba_tongyi.d.cts.map

Third-party integrations for LangChain.js

{"version":3,"file":"alibaba_tongyi.d.cts","names":["Embeddings","EmbeddingsParams","AlibabaTongyiEmbeddingsParams","EmbeddingCreateParams","AlibabaTongyiEmbeddings","Partial","Promise"],"sources":["../../src/embeddings/alibaba_tongyi.d.ts"],"sourcesContent":["import { Embeddings, type EmbeddingsParams } from \"@langchain/core/embeddings\";\nexport interface AlibabaTongyiEmbeddingsParams extends EmbeddingsParams {\n    /** Model name to use */\n    modelName: \"multimodal-embedding-v1\" | \"text-embedding-v1\" | \"text-embedding-v2\" | \"text-embedding-v3\" | \"text-embedding-v4\";\n    /**\n     * Timeout to use when making requests to AlibabaTongyi.\n     */\n    timeout?: number;\n    /**\n     * The maximum number of documents to embed in a single request. This is\n     * limited by the AlibabaTongyi API to a maximum of 2048.\n     */\n    batchSize?: number;\n    /**\n     * Whether to strip new lines from the input text.\n     */\n    stripNewLines?: boolean;\n    parameters?: {\n        /**\n         * 取值：query 或者 document，默认值为 document\n         * 说明：文本转换为向量后可以应用于检索、聚类、分类等下游任务，\n         * \t对检索这类非对称任务为了达到更好的检索效果建议区分查询文本（query）和\n         * \t底库文本（document）类型, 聚类、分类等对称任务可以不用特殊指定，\n         * \t采用系统默认值\"document\"即可\n         */\n        text_type?: \"query\" | \"document\";\n    };\n}\ninterface EmbeddingCreateParams {\n    model: AlibabaTongyiEmbeddingsParams[\"modelName\"];\n    input: {\n        texts: string[];\n    };\n    parameters?: AlibabaTongyiEmbeddingsParams[\"parameters\"];\n}\nexport declare class AlibabaTongyiEmbeddings extends Embeddings implements AlibabaTongyiEmbeddingsParams {\n    modelName: AlibabaTongyiEmbeddingsParams[\"modelName\"];\n    batchSize: number;\n    stripNewLines: boolean;\n    apiKey: string;\n    parameters: EmbeddingCreateParams[\"parameters\"];\n    constructor(fields?: Partial<AlibabaTongyiEmbeddingsParams> & {\n        verbose?: boolean;\n        apiKey?: string;\n    });\n    /**\n     * Method to generate embeddings for an array of documents. Splits the\n     * documents into batches and makes requests to the AlibabaTongyi API to generate\n     * embeddings.\n     * @param texts Array of documents to generate embeddings for.\n     * @returns Promise that resolves to a 2D array of embeddings for each document.\n     */\n    embedDocuments(texts: string[]): Promise<number[][]>;\n    /**\n     * Method to generate an embedding for a single document. Calls the\n     * embeddingWithRetry method with the document as the input.\n     * @param text Document to generate an embedding for.\n     * @returns Promise that resolves to an embedding for the document.\n     */\n    embedQuery(text: string): Promise<number[]>;\n    /**\n     * Method to generate an embedding params.\n     * @param texts Array of documents to generate embeddings for.\n     * @returns an embedding params.\n     */\n    private getParams;\n    private embeddingWithRetry;\n}\nexport {};\n//# sourceMappingURL=alibaba_tongyi.d.ts.map"],"mappings":";;;UACiBE,6BAAAA,SAAsCD;;EAAtCC,SAAAA,EAAAA,yBAA6B,GAAA,mBAASD,GAAgB,mBAAA,GAAA,mBAAA,GAAA,mBAAA;EA2B7DE;AAOV;;EAKgBA,OAAAA,CAAAA,EAAAA,MAAAA;EACiBD;;;;EANoBF,SAAAA,CAAAA,EAAAA,MAAAA;EAAsBE;AAA6B;;;;;;;;;;;;;;UAP9FC,qBAAAA;SACCD;;;;eAIMA;;cAEIE,uBAAAA,SAAgCJ,UAAAA,YAAsBE;aAC5DA;;;;cAICC;uBACSE,QAAQH;;;;;;;;;;;mCAWII;;;;;;;4BAOPA"}