import {z} from 'zod'

export type OpenAIEmbeddingRequest = {model: string, input: string | Array<string>, encoding_format?: ('float' | 'base64') | undefined, dimensions?: number | undefined, user?: string | undefined, fallbacks?: Array<string> | undefined};

export type CreateApiLangchainV1EmbeddingsArgs = {body: OpenAIEmbeddingRequest};

export type CreateApiLangchainV1EmbeddingsOptions = {baseUrl?: string | undefined, BearerAuth?: string | undefined, fetch?: typeof fetch | undefined};

export const chatPromptTokensDetails = z.object({text_tokens: z.number().int().optional(), audio_tokens: z.number().int().optional(), image_tokens: z.number().int().optional(), cached_read_tokens: z.number().int().optional(), cached_write_tokens: z.number().int().optional()});

export const chatCompletionTokensDetails = z.object({text_tokens: z.number().int().optional(), accepted_prediction_tokens: z.number().int().optional(), audio_tokens: z.number().int().optional(), citation_tokens: z.number().int().optional(), num_search_queries: z.number().int().optional(), reasoning_tokens: z.number().int().optional(), image_tokens: z.number().int().optional(), rejected_prediction_tokens: z.number().int().optional()});

export const bifrostCost = z.object({input_tokens_cost: z.number().optional(), output_tokens_cost: z.number().optional(), reasoning_tokens_cost: z.number().optional(), citation_tokens_cost: z.number().optional(), search_queries_cost: z.number().optional(), request_cost: z.number().optional(), total_cost: z.number().optional()});

export const bifrostLLMUsage = z.object({prompt_tokens: z.number().int().optional(), prompt_tokens_details: chatPromptTokensDetails.optional(), completion_tokens: z.number().int().optional(), completion_tokens_details: chatCompletionTokensDetails.optional(), total_tokens: z.number().int().optional(), cost: bifrostCost.optional()});

export const modelProvider = z.enum(["openai", "azure", "anthropic", "bedrock", "cohere", "vertex", "vllm", "mistral", "ollama", "groq", "sgl", "parasail", "perplexity", "replicate", "cerebras", "deepseek", "gemini", "openrouter", "elevenlabs", "huggingface", "nebius", "xai", "runway", "fireworks"]);

export const bifrostCacheDebug = z.object({cache_hit: z.boolean().optional(), cache_id: z.string().optional(), hit_type: z.string().optional(), requested_provider: z.string().optional(), requested_model: z.string().optional(), provider_used: z.string().optional(), model_used: z.string().optional(), input_tokens: z.number().int().optional(), threshold: z.number().optional(), similarity: z.number().optional()});

export const bifrostResponseExtraFields = z.object({request_type: z.string().optional(), provider: modelProvider.optional(), model_requested: z.string().optional(), model_deployment: z.string().optional(), latency: z.number().int().optional(), chunk_index: z.number().int().optional(), raw_request: z.object({}).optional(), raw_response: z.object({}).optional(), cache_debug: bifrostCacheDebug.optional()});

export const embeddingResponse = z.object({data: z.array(z.object({index: z.number().int().optional(), object: z.string().optional(), embedding: z.union([z.string(), z.array(z.number()), z.array(z.array(z.number()))]).optional()})).optional(), model: z.string().optional(), object: z.string().optional(), usage: bifrostLLMUsage.optional(), extra_fields: bifrostResponseExtraFields.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
};

/**
 * Create embeddings (LangChain - OpenAI format)
 * 
 * POST /langchain/v1/embeddings
 * 
 * Also accepts auth: BasicAuth, VirtualKeyAuth, ApiKeyAuth
 */
export const createApiLangchainV1Embeddings = async (args: CreateApiLangchainV1EmbeddingsArgs, options?: CreateApiLangchainV1EmbeddingsOptions) => {
const {baseUrl = "http://localhost:8080", fetch: fetchFn = fetch, BearerAuth} = options ?? {}

const query = new URLSearchParams()
const headers = new Headers()
headers.set('content-type', "application/json")
headers.set('accept', "application/json")

if (BearerAuth !== undefined) headers.set('authorization', `Bearer ${BearerAuth}`)

const url = `${baseUrl.replace(/\/+$/, '')}/langchain/v1/embeddings${query.size > 0 ? `?${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(embeddingResponse, await res.json())
};