@langchain/openai/dist/embeddings.d.cts.map

OpenAI integrations for LangChain.js

{"version":3,"file":"embeddings.d.cts","names":["ClientOptions","OpenAI","OpenAIClient","Embeddings","EmbeddingsParams","OpenAIApiKey","OpenAIEmbeddingModelId","EmbeddingModel","NonNullable","OpenAIEmbeddingsParams","OpenAIEmbeddings","TOutput","Partial","Promise","EmbeddingCreateParams","CreateEmbeddingResponse"],"sources":["../src/embeddings.d.ts"],"sourcesContent":["import { type ClientOptions, OpenAI as OpenAIClient } from \"openai\";\nimport { Embeddings, type EmbeddingsParams } from \"@langchain/core/embeddings\";\nimport type { OpenAIApiKey } from \"./types.js\";\n/**\n * @see https://platform.openai.com/docs/guides/embeddings#embedding-models\n */\nexport type OpenAIEmbeddingModelId = OpenAIClient.EmbeddingModel | (string & NonNullable<unknown>);\n/**\n * Interface for OpenAIEmbeddings parameters. Extends EmbeddingsParams and\n * defines additional parameters specific to the OpenAIEmbeddings class.\n */\nexport interface OpenAIEmbeddingsParams extends EmbeddingsParams {\n    /**\n     * Model name to use\n     * Alias for `model`\n     * @deprecated Use \"model\" instead.\n     */\n    modelName: OpenAIEmbeddingModelId;\n    /** Model name to use */\n    model: OpenAIEmbeddingModelId;\n    /**\n     * The number of dimensions the resulting output embeddings should have.\n     * Only supported in `text-embedding-3` and later models.\n     */\n    dimensions?: number;\n    /**\n     * Timeout to use when making requests to OpenAI.\n     */\n    timeout?: number;\n    /**\n     * The maximum number of documents to embed in a single request. This is\n     * limited by the OpenAI API to a maximum of 2048.\n     */\n    batchSize?: number;\n    /**\n     * Whether to strip new lines from the input text. This is recommended by\n     * OpenAI for older models, but may not be suitable for all use cases.\n     * See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500\n     */\n    stripNewLines?: boolean;\n    /**\n     * The format to return the embeddings in. Can be either 'float' or 'base64'.\n     */\n    encodingFormat?: \"float\" | \"base64\";\n}\n/**\n * Class for generating embeddings using the OpenAI API.\n *\n * To use with Azure, import the `AzureOpenAIEmbeddings` class.\n *\n * @example\n * ```typescript\n * // Embed a query using OpenAIEmbeddings to generate embeddings for a given text\n * const model = new OpenAIEmbeddings();\n * const res = await model.embedQuery(\n *   \"What would be a good company name for a company that makes colorful socks?\",\n * );\n * console.log({ res });\n *\n * ```\n */\nexport declare class OpenAIEmbeddings<TOutput = number[]> extends Embeddings<TOutput> implements Partial<OpenAIEmbeddingsParams> {\n    model: string;\n    /** @deprecated Use \"model\" instead */\n    modelName: string;\n    batchSize: number;\n    stripNewLines: boolean;\n    /**\n     * The number of dimensions the resulting output embeddings should have.\n     * Only supported in `text-embedding-3` and later models.\n     */\n    dimensions?: number;\n    timeout?: number;\n    organization?: string;\n    encodingFormat?: \"float\" | \"base64\";\n    protected client: OpenAIClient;\n    protected clientConfig: ClientOptions;\n    protected apiKey?: OpenAIApiKey;\n    constructor(fields?: Partial<OpenAIEmbeddingsParams> & {\n        verbose?: boolean;\n        /**\n         * The OpenAI API key to use.\n         * Alias for `apiKey`.\n         */\n        openAIApiKey?: OpenAIApiKey;\n        /** The OpenAI API key to use. */\n        apiKey?: OpenAIApiKey;\n        configuration?: ClientOptions;\n    });\n    /**\n     * Method to generate embeddings for an array of documents. Splits the\n     * documents into batches and makes requests to the OpenAI 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<TOutput[]>;\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<TOutput>;\n    /**\n     * Private method to make a request to the OpenAI API to generate\n     * embeddings. Handles the retry logic and returns the response from the\n     * API.\n     * @param request Request to send to the OpenAI API.\n     * @returns Promise that resolves to the response from the API.\n     */\n    protected embeddingWithRetry(request: OpenAIClient.EmbeddingCreateParams): Promise<OpenAIClient.CreateEmbeddingResponse & {\n        _request_id?: string | null | undefined;\n    }>;\n}\n//# sourceMappingURL=embeddings.d.ts.map"],"mappings":";;;;;;;;AAMA;AAKiBS,KALLH,sBAAAA,GAAyBJ,QAAAA,CAAaK,cAKX,GAAA,CAAA,MAAA,GALsCC,WAKtC,CAAA,OAAA,CAAA,CAAA;;;;AAAyB;AAkD3CE,UAlDJD,sBAAAA,SAA+BL,gBAkDX,CAAA;EAAwCO;;;;;EAiB5CF,SAAAA,EA7DlBH,sBA6DkBG;EAARG;EAMFP,KAAAA,EAjEZC,sBAiEYD;EAENA;;;;EAiBqBM,UAAAA,CAAAA,EAAAA,MAAAA;EAARE;;;EAQiDA,OAAAA,CAAAA,EAAAA,MAAAA;EAlDbV;;AAAsC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;cAAnFO,6CAA6CP,WAAWQ,oBAAoBC,QAAQH;;;;;;;;;;;;;;oBAcnFP;0BACMF;qBACLK;uBACEO,QAAQH;;;;;;mBAMVJ;;aAENA;oBACOL;;;;;;;;;mCASaa,QAAQF;;;;;;;4BAOfE,QAAQF;;;;;;;;wCAQIT,QAAAA,CAAaY,wBAAwBD,QAAQX,QAAAA,CAAaa"}