import * as z from "zod/v4"; import { ApiEndpoint } from "./apiendpoint.js"; import { BatchRequest, BatchRequest$Outbound } from "./batchrequest.js"; export type CreateBatchJobRequest = { /** * The list of input files to be used for batch inference, these files should be `jsonl` files, containing the input data corresponding to the bory request for the batch inference in a "body" field. An example of such file is the following: ```json {"custom_id": "0", "body": {"max_tokens": 100, "messages": [{"role": "user", "content": "What is the best French cheese?"}]}} {"custom_id": "1", "body": {"max_tokens": 100, "messages": [{"role": "user", "content": "What is the best French wine?"}]}} ``` */ inputFiles?: Array | null | undefined; requests?: Array | null | undefined; endpoint: ApiEndpoint; /** * The model to be used for batch inference. */ model?: string | null | undefined; /** * In case you want to use a specific agent from the **deprecated** agents api for batch inference, you can specify the agent ID here. */ agentId?: string | null | undefined; /** * The metadata of your choice to be associated with the batch inference job. */ metadata?: { [k: string]: string; } | null | undefined; /** * The timeout in hours for the batch inference job. */ timeoutHours?: number | undefined; }; /** @internal */ export type CreateBatchJobRequest$Outbound = { input_files?: Array | null | undefined; requests?: Array | null | undefined; endpoint: string; model?: string | null | undefined; agent_id?: string | null | undefined; metadata?: { [k: string]: string; } | null | undefined; timeout_hours: number; }; /** @internal */ export declare const CreateBatchJobRequest$outboundSchema: z.ZodType; export declare function createBatchJobRequestToJSON(createBatchJobRequest: CreateBatchJobRequest): string; //# sourceMappingURL=createbatchjobrequest.d.ts.map