import { readFile } from "node:fs/promises";
import { join } from "node:path";

import type { AgentFn, Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
import { createRole } from "@uncaged/nerve-workflow-utils";
import { z } from "zod";

import { resolveQueueForQuestioner } from "../lib/knowledge-queue.js";
import { resolveWorkdir } from "../lib/workdir.js";

const questionerExtractSchema = z.object({
  questions: z
    .array(
      z.object({
        id: z.string(),
        question: z.string(),
        domain: z.string(),
      }),
    )
    .length(5),
});

export type QuestionerMeta = {
  /** Empty when no .knowledge cards and no work to do. */
  card: string;
  questions: { id: string; question: string; domain: string }[];
  remaining_queue: string[];
};

export type CreateQuestionerRoleDeps = {
  extract: LlmExtractorConfig;
};

function questionerSystem(): string {
  return `You are the **questioner** in an extract-knowledge workflow.

Read the given markdown knowledge card. Propose exactly **five** technical questions that are **not** already answered or covered by that card.

Rules:
- Questions must be concrete and technical.
- Each question needs a stable string id (e.g. q1, q2, q3, q4, q5), a short domain label (e.g. routing, storage), and the question text.
- Do not assume access to other files or tools — reason only from the card content shown.`;
}

function questionerUser(card: string, cardBody: string, remainingHint: string[]): string {
  return `Current card path: ${card}

Remaining queue after this card (paths, may be empty): ${JSON.stringify(remainingHint)}

--- Card content ---

${cardBody}`;
}

export async function questionerPrompt(ctx: ThreadContext): Promise<string> {
  const messages = ctx.steps as unknown as WorkflowMessage[];
  const cwd = resolveWorkdir(ctx.start);
  const queue = await resolveQueueForQuestioner(ctx.start, messages, cwd);
  if (queue.length === 0) {
    throw new Error(
      "questioner: prompt invoked with empty queue — wrapped role should short-circuit before LLM",
    );
  }
  const card = queue[0]!;
  const remaining_queue = queue.slice(1);
  let cardBody: string;
  try {
    cardBody = await readFile(join(cwd, card), "utf8");
  } catch (e) {
    const msg = e instanceof Error ? e.message : String(e);
    throw new Error(`questioner: failed to read ${card}: ${msg}`);
  }
  return `${questionerSystem()}\n\n${questionerUser(card, cardBody, remaining_queue)}`;
}

export function createQuestionerRole(adapter: AgentFn, { extract }: CreateQuestionerRoleDeps): Role<QuestionerMeta> {
  const inner = createRole(adapter, questionerPrompt, questionerExtractSchema, extract);

  return async (ctx: ThreadContext) => {
    const messages = ctx.steps as unknown as WorkflowMessage[];
    const cwd = resolveWorkdir(ctx.start);
    const queue = await resolveQueueForQuestioner(ctx.start, messages, cwd);
    if (queue.length === 0) {
      return {
        content:
          "questioner: no `.knowledge` markdown files found and no seed path in the trigger prompt; queue is empty.",
        meta: {
          card: "",
          questions: [],
          remaining_queue: [],
        },
      };
    }

    const card = queue[0]!;
    const remaining_queue = queue.slice(1);
    const r = await inner(ctx);
    return {
      content: r.content,
      meta: {
        card,
        questions: r.meta.questions,
        remaining_queue,
      },
    };
  };
}