76 lines
2.9 KiB
TypeScript
76 lines
2.9 KiB
TypeScript
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
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export interface AlibabaTongyiEmbeddingsParams extends EmbeddingsParams {
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/** Model name to use */
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modelName: "text-embedding-v2";
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/**
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* Timeout to use when making requests to AlibabaTongyi.
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*/
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timeout?: number;
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/**
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* The maximum number of documents to embed in a single request. This is
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* limited by the AlibabaTongyi API to a maximum of 2048.
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*/
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batchSize?: number;
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/**
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* Whether to strip new lines from the input text.
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*/
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stripNewLines?: boolean;
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parameters?: {
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/**
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* 取值：query 或者 document，默认值为 document
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* 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，
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* 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
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* 底库文本（document）类型, 聚类、分类等对称任务可以不用特殊指定，
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* 采用系统默认值"document"即可
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*/
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text_type?: "query" | "document";
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};
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}
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interface EmbeddingCreateParams {
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model: AlibabaTongyiEmbeddingsParams["modelName"];
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input: {
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texts: string[];
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};
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parameters?: AlibabaTongyiEmbeddingsParams["parameters"];
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}
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export declare class AlibabaTongyiEmbeddings extends Embeddings implements AlibabaTongyiEmbeddingsParams {
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modelName: AlibabaTongyiEmbeddingsParams["modelName"];
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batchSize: number;
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stripNewLines: boolean;
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apiKey: string;
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parameters: EmbeddingCreateParams["parameters"];
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constructor(fields?: Partial<AlibabaTongyiEmbeddingsParams> & {
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verbose?: boolean;
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apiKey?: string;
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});
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/**
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* Method to generate embeddings for an array of documents. Splits the
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* documents into batches and makes requests to the AlibabaTongyi API to generate
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* embeddings.
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* @param texts Array of documents to generate embeddings for.
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* @returns Promise that resolves to a 2D array of embeddings for each document.
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*/
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embedDocuments(texts: string[]): Promise<number[][]>;
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/**
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* Method to generate an embedding for a single document. Calls the
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* embeddingWithRetry method with the document as the input.
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* @param text Document to generate an embedding for.
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* @returns Promise that resolves to an embedding for the document.
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*/
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embedQuery(text: string): Promise<number[]>;
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/**
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* Method to generate an embedding params.
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* @param texts Array of documents to generate embeddings for.
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* @returns an embedding params.
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*/
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private getParams;
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/**
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* Private method to make a request to the OpenAI API to generate
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* embeddings. Handles the retry logic and returns the response from the
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* API.
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* @param request Request to send to the OpenAI API.
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* @returns Promise that resolves to the response from the API.
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*/
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private embeddingWithRetry;
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}
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export {};
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