@langchain/community/dist/embeddings/gradient_ai.d.ts.map

Third-party integrations for LangChain.js

{"version":3,"file":"gradient_ai.d.ts","names":["Embeddings","EmbeddingsParams","GradientEmbeddingsParams","GradientEmbeddings","Promise"],"sources":["../../src/embeddings/gradient_ai.d.ts"],"sourcesContent":["import { Embeddings, EmbeddingsParams } from \"@langchain/core/embeddings\";\n/**\n * Interface for GradientEmbeddings parameters. Extends EmbeddingsParams and\n * defines additional parameters specific to the GradientEmbeddings class.\n */\nexport interface GradientEmbeddingsParams extends EmbeddingsParams {\n    /**\n     * Gradient AI Access Token.\n     * Provide Access Token if you do not wish to automatically pull from env.\n     */\n    gradientAccessKey?: string;\n    /**\n     * Gradient Workspace Id.\n     * Provide workspace id if you do not wish to automatically pull from env.\n     */\n    workspaceId?: string;\n}\n/**\n * Class for generating embeddings using the Gradient AI's API. Extends the\n * Embeddings class and implements GradientEmbeddingsParams and\n */\nexport declare class GradientEmbeddings extends Embeddings implements GradientEmbeddingsParams {\n    gradientAccessKey?: string;\n    workspaceId?: string;\n    batchSize: number;\n    model: any;\n    constructor(fields: GradientEmbeddingsParams);\n    /**\n     * Method to generate embeddings for an array of documents. Splits the\n     * documents into batches and makes requests to the Gradient 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     * embedDocuments 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 set the model to use for generating embeddings.\n     * @sets the class' `model` value to that of the retrieved Embeddings Model.\n     */\n    setModel(): Promise<void>;\n}\n//# sourceMappingURL=gradient_ai.d.ts.map"],"mappings":";;;;;;AAKA;AAgBA;AAKwBE,UArBPA,wBAAAA,SAAiCD,gBAqB1BC,CAAAA;EAQaE;;;;EAbiCF,iBAAAA,CAAAA,EAAAA,MAAAA;EAAwB;;;;;;;;;;cAAzEC,kBAAAA,SAA2BH,UAAAA,YAAsBE;;;;;sBAK9CA;;;;;;;;mCAQaE;;;;;;;4BAOPA;;;;;cAKdA"}