import {z} from 'zod' export type Document = {body?: string | undefined, source?: string | undefined, title?: string | undefined, date?: string | undefined}; export type BatchRag = Array<{text?: string | undefined, documents?: Array | undefined}>; export type UseCaseConfigRagExtDoc = {memoryUuid?: string | undefined, answerNotFoundMessage?: string | undefined}; export type ModelConfig = {vectorQuantizationMethod?: string | undefined, dimReductionSize?: number | undefined}; export type RagExtDocRequest = {batch?: BatchRag | undefined, useCaseConfig?: UseCaseConfigRagExtDoc | undefined, modelConfig?: ModelConfig | undefined}; export type CreateApiAiAsyncPredictionRagMODELIDArgs = {Authorization: string, 'Content-Type'?: string | undefined, body: RagExtDocRequest}; export type CreateApiAiAsyncPredictionRagMODELIDOptions = {baseUrl?: string | undefined, authToken?: string | undefined, fetch?: typeof fetch | undefined}; export const pOSTresponse = z.object({chunkingId: z.string().optional(), status: z.string().optional()}); export const validateResponse = async ( schema: { readonly '~standard': { readonly validate: (value: unknown) => ({ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray | undefined }> }) | Promise<{ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray | undefined }> }> } }, value: unknown ): Promise => { const result = await schema['~standard'].validate(value) if (result.issues !== undefined) { const detail = result.issues .map(({ message, path }) => path?.length ? `${path.map((segment) => String(typeof segment === 'object' ? segment.key : segment)).join('.')}: ${message}` : message ) .join('; ') throw new Error(`Response does not match the schema: ${detail}`, { cause: { issues: result.issues } }) } return result.value }; /** * RAG use case * * POST /ai/async-prediction/rag/{MODEL_ID} */ export const createApiAiAsyncPredictionRagMODELID = async (args: CreateApiAiAsyncPredictionRagMODELIDArgs, options?: CreateApiAiAsyncPredictionRagMODELIDOptions) => { const {baseUrl = "https://commerce_studio_id.experiencemanager.lucidworks.com", fetch: fetchFn = fetch, authToken} = options ?? {} const query = new URLSearchParams() const headers = new Headers() headers.set('content-type', "application/json") headers.set('accept', "application/json") if (args.Authorization != null) headers.set("Authorization", String(args.Authorization)) if (args['Content-Type'] != null) headers.set("Content-Type", String(args['Content-Type'])) if (authToken !== undefined) headers.set('authorization', `Bearer ${authToken}`) const url = `${baseUrl.replace(/\/+$/, '')}/ai/async-prediction/rag/${encodeURIComponent(String(args.MODEL_ID))}${query.size > 0 ? `?${query}` : ''}` const res = await fetchFn(url, {method: "POST", headers, body: JSON.stringify(args.body)}) if (!res.ok) throw new Error(`Request failed with status ${res.status}`, {cause: {status: res.status, body: await res.text()}}) return validateResponse(pOSTresponse, await res.json()) };