import {z} from 'zod'

export type UseCaseConfigEmbedding = {dataType?: string | undefined};

export type ModelConfigEmbedding = {lowercaseInput?: boolean | undefined};

export type BatchEmbeddingEncoders = {batch?: (Array<{text?: string | undefined}>) | undefined, useCaseConfig?: UseCaseConfigEmbedding | undefined, modelConfig?: ModelConfigEmbedding | undefined};

export type CreateApiAiPredictionEmbeddingTextEncoderArgs = {Authorization: string, 'Content-Type'?: string | undefined, body: BatchEmbeddingEncoders};

export type CreateApiAiPredictionEmbeddingTextEncoderOptions = {baseUrl?: string | undefined, authToken?: string | undefined, fetch?: typeof fetch | undefined};

export const jsonNode = z.object({});

export const embeddingEncodersResponse = z.object({predictions: z.array(z.object({vector: jsonNode.optional()})).optional()});

export const validateResponse = async <Output>(
  schema: {
    readonly '~standard': {
      readonly validate: (value: unknown) => ({ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }) | Promise<{ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }>
    }
  },
  value: unknown
): Promise<Output> => {
  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
};

/**
 * English language model text encoder
 * 
 * POST /ai/prediction/embedding/text-encoder
 */
export const createApiAiPredictionEmbeddingTextEncoder = async (args: CreateApiAiPredictionEmbeddingTextEncoderArgs, options?: CreateApiAiPredictionEmbeddingTextEncoderOptions) => {
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/prediction/embedding/text-encoder${query.size > 0 ? `?${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(embeddingEncodersResponse, await res.json())
};