import {z} from 'zod'

export type KBWebPage = {_id?: string | undefined, src?: string | undefined, title?: string | undefined, scrapping_status?: boolean | undefined};

export type KBSource = {_id?: string | undefined, type: 'web-site' | 'document' | 'plain-text', data: string, name?: string | undefined, metadata?: string | undefined, url?: string | undefined, pages?: Array<KBWebPage> | undefined};

export type KBSourceConfig = {sources?: Array<KBSource> | undefined};

export type KBLLMConfig = {model_id?: string | undefined, model_name?: string | undefined, provider?: string | undefined, name?: string | undefined};

export type KBRerankingConfig = {weighting_multiplier_coefficient?: number | undefined};

export type KBChunkingConfig = {web?: ({chunking_method?: ('fixed_size' | 'dynamic') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, image?: ({chunking_method?: ('fixed_size' | 'dynamic') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, pdf?: ({chunking_method?: ('fixed_size' | 'dynamic' | 'by_page') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, split_pdf_pages?: boolean | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, docx?: ({chunking_method?: ('fixed_size' | 'dynamic' | 'by_page') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, ppt?: ({chunking_method?: ('fixed_size' | 'dynamic' | 'by_page') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, txt?: ({chunking_method?: ('fixed_size' | 'dynamic') | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, csv?: ({chunking_method?: 'by_line' | undefined, chunk_size?: number | undefined, chunk_overlap?: number | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined};

export type KBIngestionConfig = {delay_between_documents_ms?: number | undefined, web?: ({waiting_time_for_web_scraping?: number | undefined, library?: ('Cheerio' | 'Puppeteer') | undefined, selector?: string | undefined, exclude?: string | undefined, proxy?: ({status?: boolean | undefined, country?: string | undefined}) | undefined}) | undefined, image?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, pdf?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, docx?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, ppt?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, txt?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined, csv?: ({strategy?: ('parse' | 'ocr') | undefined, instructions?: string | undefined, llm_model?: KBLLMConfig | undefined}) | undefined};

export type KnowledgeBaseCreateRequest = {name: string, source?: KBSourceConfig | undefined, LLM?: KBLLMConfig | undefined, reranking?: KBRerankingConfig | undefined, chunking?: KBChunkingConfig | undefined, ingestion?: KBIngestionConfig | undefined, is_document_confidential?: boolean | undefined};

export type CreateApiAssistantKnowledgesArgs = {body: KnowledgeBaseCreateRequest};

export type CreateApiAssistantKnowledgesOptions = {baseUrl?: string | undefined, Bearer?: string | undefined, fetch?: typeof fetch | undefined};

export const kBWebPage = z.object({_id: z.string().optional(), src: z.string().optional(), title: z.string().optional(), scrapping_status: z.boolean().optional()});

export const kBSource = z.object({_id: z.string().optional(), type: z.enum(["web-site", "document", "plain-text"]), data: z.string(), name: z.string().optional(), metadata: z.string().optional(), url: z.string().optional(), pages: z.array(kBWebPage).optional()});

export const kBSourceConfig = z.object({sources: z.array(kBSource).optional()});

export const kBLLMConfig = z.object({model_id: z.string().optional(), model_name: z.string().optional(), provider: z.string().optional(), name: z.string().optional()});

export const kBRerankingConfig = z.object({weighting_multiplier_coefficient: z.number().optional()});

export const kBChunkingConfig = z.object({web: z.object({chunking_method: z.enum(["fixed_size", "dynamic"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), image: z.object({chunking_method: z.enum(["fixed_size", "dynamic"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), pdf: z.object({chunking_method: z.enum(["fixed_size", "dynamic", "by_page"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), split_pdf_pages: z.boolean().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), docx: z.object({chunking_method: z.enum(["fixed_size", "dynamic", "by_page"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), ppt: z.object({chunking_method: z.enum(["fixed_size", "dynamic", "by_page"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), txt: z.object({chunking_method: z.enum(["fixed_size", "dynamic"]).optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), csv: z.object({chunking_method: z.literal("by_line").optional(), chunk_size: z.number().int().optional(), chunk_overlap: z.number().int().optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional()});

export const kBIngestionConfig = z.object({delay_between_documents_ms: z.number().int().optional(), web: z.object({waiting_time_for_web_scraping: z.number().int().optional(), library: z.enum(["Cheerio", "Puppeteer"]).optional(), selector: z.string().optional(), exclude: z.string().optional(), proxy: z.object({status: z.boolean().optional(), country: z.string().optional()}).optional()}).optional(), image: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), pdf: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), docx: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), ppt: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), txt: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional(), csv: z.object({strategy: z.enum(["parse", "ocr"]).optional(), instructions: z.string().optional(), llm_model: kBLLMConfig.optional()}).optional()});

export const knowledgeBaseFull = z.object({_id: z.string().optional(), name: z.string().optional(), user_id: z.string().optional(), collaborators: z.array(z.string()).optional(), is_shared: z.boolean().optional(), need_training: z.boolean().optional(), training: z.boolean().optional(), is_document_confidential: z.boolean().optional(), is_archived: z.boolean().optional(), progress: z.string().optional(), source: kBSourceConfig.optional(), LLM: kBLLMConfig.optional(), reranking: kBRerankingConfig.optional(), chunking: kBChunkingConfig.optional(), ingestion: kBIngestionConfig.optional(), createdAt: z.string().optional(), updatedAt: z.string().optional(), __v: z.number().int().optional()});

export const knowledgeBaseResponse = z.object({knowledge: knowledgeBaseFull.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 a knowledge base
 * 
 * POST /assistant_knowledges
 */
export const createApiAssistantKnowledges = async (args: CreateApiAssistantKnowledgesArgs, options?: CreateApiAssistantKnowledgesOptions) => {
const {baseUrl = "https://apis.smartly.ai", fetch: fetchFn = fetch, Bearer} = options ?? {}

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

if (Bearer !== undefined) headers.set("Authorization", Bearer)

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