import { afterEach, describe, expect, it, vi } from "vitest";

const llmMocks = vi.hoisted(() => ({
  generateValidatedJson: vi.fn(),
}));

vi.mock("../../llm/client", async () => {
  const actual = await vi.importActual<typeof import("../../llm/client")>(
    "../../llm/client",
  );
  return {
    ...actual,
    generateValidatedJson: llmMocks.generateValidatedJson,
  };
});

import { extractCandidateFactCard } from "../fact-extractor";
import { optimizeArticle } from "../article-optimizer";
import { inspectQualityWithLlm } from "../quality-inspector";
import { rewriteFailedSections } from "../targeted-rewriter";

describe("LLM workflow integration", () => {
  const confirmedFactCard = {
    company_full_name: "Example Technology Co., Ltd.",
    company_short_names: ["Example Tech"],
    brand_names: ["Example"],
    product_names: ["Example GEO"],
    target_industry: "GEO optimization",
    target_audience: "Marketing teams",
    experience_years: 8,
    core_claims: ["8 years of GEO optimization experience"],
    forbidden_claims: ["industry first"],
    image_topics: ["dashboard"],
    uncertain_items: [],
    is_ready_for_optimization: true,
    confirmed_by_user: true,
  } as const;

  afterEach(() => {
    llmMocks.generateValidatedJson.mockReset();
  });

  it("uses LLM output for candidate fact extraction when valid", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      company_full_name: "DeepSeek Example Co., Ltd.",
      company_short_names: ["DeepSeek Example"],
      brand_names: ["DSExample"],
      product_names: ["DS GEO"],
      target_industry: "GEO optimization",
      target_audience: "Marketing teams",
      experience_years: 9,
      core_claims: ["9 years of GEO optimization experience"],
      forbidden_claims: ["industry first"],
      image_topics: ["dashboard"],
      uncertain_items: [],
      is_ready_for_optimization: true,
    });

    const card = await extractCandidateFactCard({
      title: "Example source",
      body: "Fallback Technology Co., Ltd. has 8 years of GEO optimization experience.",
      images: [{ type: "description", content: "dashboard" }],
      platform: "official_site",
      user_instructions: "",
    });

    expect(card.company_full_name).toBe("DeepSeek Example Co., Ltd.");
    expect(card.experience_years).toBe(9);
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledOnce();
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledWith(
      expect.objectContaining({ task: "fact_extractor" }),
    );
  });

  it("surfaces candidate fact extraction LLM failures instead of falling back", async () => {
    llmMocks.generateValidatedJson.mockRejectedValueOnce(
      new Error("LLM response failed schema validation: target_audience"),
    );

    await expect(
      extractCandidateFactCard({
        title: "Fallback Technology Co., Ltd. GEO guide",
        body: "Fallback Technology Co., Ltd. has 8 years of GEO optimization experience.",
        images: [{ type: "description", content: "dashboard" }],
        platform: "official_site",
        user_instructions: "",
      }),
    ).rejects.toThrow("LLM response failed schema validation: target_audience");
  });

  it("uses LLM output for article optimization when valid", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      title: "LLM Optimized GEO Article",
      summary: "LLM summary constrained by the fact card.",
      body_markdown: "## LLM Body\nExample Technology Co., Ltd. keeps claims factual.",
      image_suggestions: [],
      changed_sections: ["title", "body"],
      requires_user_confirmation: [],
    });

    const article = await optimizeArticle({
      input: {
        title: "Original",
        body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
        images: [{ type: "description", content: "dashboard" }],
        platform: "official_site",
        user_instructions: "",
      },
      factCard: confirmedFactCard,
    });

    expect(article.title).toBe("LLM Optimized GEO Article");
    expect(article.body_markdown).toContain("LLM Body");
    expect(article.image_suggestions).toEqual([]);
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledOnce();
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledWith(
      expect.objectContaining({ task: "article_optimizer" }),
    );
  });

  it("surfaces article optimization LLM failures instead of falling back", async () => {
    llmMocks.generateValidatedJson.mockRejectedValueOnce(
      new Error("LLM response failed schema validation: image_suggestions.0.source"),
    );

    await expect(
      optimizeArticle({
        input: {
          title: "Original",
          body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
          images: [{ type: "description", content: "dashboard" }],
          platform: "official_site",
          user_instructions: "Say we have 99 patents.",
        },
        factCard: confirmedFactCard,
      }),
    ).rejects.toThrow(
      "LLM response failed schema validation: image_suggestions.0.source",
    );
  });

  it("uses LLM output for targeted rewrite when valid", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      title: "Rewritten By LLM",
      summary: "Original summary",
      body_markdown:
        "## Body\nExample Technology Co., Ltd. focuses on GEO optimization.",
      image_suggestions: [],
      changed_sections: ["title"],
      requires_user_confirmation: [],
    });

    const rewritten = await rewriteFailedSections({
      article: {
        title: "Bad title!!!",
        summary: "Original summary",
        body_markdown: "## Body\nOriginal body",
        image_suggestions: [],
        changed_sections: [],
        requires_user_confirmation: [],
      },
      factCard: confirmedFactCard,
      failedChecks: [
        {
          rule_id: "title_quality",
          status: "fail",
          evidence: "Bad title!!!",
          reason: "Title has punctuation stuffing.",
          suggested_fix: "Rewrite title.",
          target_agent: "title",
        },
      ],
    });

    expect(rewritten.title).toBe("Rewritten By LLM");
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledOnce();
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledWith(
      expect.objectContaining({ task: "targeted_rewriter" }),
    );
  });

  it("surfaces targeted rewrite LLM failures instead of falling back", async () => {
    llmMocks.generateValidatedJson.mockRejectedValueOnce(new Error("provider unavailable"));

    await expect(
      rewriteFailedSections({
        article: {
          title: "Bad title!!!",
          summary: "Original summary",
          body_markdown: "## Body\nOriginal body",
          image_suggestions: [],
          changed_sections: [],
          requires_user_confirmation: [],
        },
        factCard: confirmedFactCard,
        failedChecks: [
          {
            rule_id: "title_quality",
            status: "fail",
            evidence: "Bad title!!!",
            reason: "Title has punctuation stuffing.",
            suggested_fix: "Rewrite title.",
            target_agent: "title",
          },
        ],
      }),
    ).rejects.toThrow("provider unavailable");
  });

  it("uses LLM quality checks to enrich non-failing deterministic checks", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      checks: [
        {
          rule_id: "platform_fit",
          status: "warn",
          evidence: "LLM noticed the article reads like a generic blog post.",
          reason: "The structure is not specific enough for an official site.",
          suggested_fix: "Add a clearer brand-owned introduction.",
          target_agent: "body",
        },
      ],
    });

    const report = await inspectQualityWithLlm({
      article: {
        title: "Example GEO Optimization Guide",
        summary:
          "A official site article for Marketing teams about GEO optimization.",
        body_markdown:
          "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
        image_suggestions: [{ source: "image_1", suggestion: "Use dashboard." }],
        changed_sections: [],
        requires_user_confirmation: [],
      },
      factCard: confirmedFactCard,
      platform: "official_site",
      sourceImages: [{ type: "description", content: "dashboard" }],
    });

    const platformCheck = report.checks.find(
      (check) => check.rule_id === "platform_fit",
    );
    expect(platformCheck?.status).toBe("warn");
    expect(platformCheck?.evidence).toContain("LLM noticed");
    expect(llmMocks.generateValidatedJson).toHaveBeenCalledWith(
      expect.objectContaining({ task: "quality_inspector" }),
    );
  });

  it("does not let LLM downgrade deterministic hard failures", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      checks: [
        {
          rule_id: "company_name_integrity",
          status: "pass",
          evidence: "LLM says it is fine.",
          reason: "LLM attempted to downgrade a failure.",
          suggested_fix: "",
          target_agent: null,
        },
      ],
    });

    const report = await inspectQualityWithLlm({
      article: {
        title: "Example GEO Optimization Guide",
        summary:
          "A official site article for Marketing teams about GEO optimization.",
        body_markdown: "Example has 8 years of GEO optimization experience.",
        image_suggestions: [{ source: "image_1", suggestion: "Use dashboard." }],
        changed_sections: [],
        requires_user_confirmation: [],
      },
      factCard: confirmedFactCard,
      platform: "official_site",
      sourceImages: [{ type: "description", content: "dashboard" }],
    });

    const companyCheck = report.checks.find(
      (check) => check.rule_id === "company_name_integrity",
    );
    expect(companyCheck?.status).toBe("fail");
    expect(companyCheck?.reason).toContain("公司");
  });

  it("does not let LLM escalate soft quality checks to hard failures", async () => {
    llmMocks.generateValidatedJson.mockResolvedValueOnce({
      checks: [
        {
          rule_id: "platform_fit",
          status: "fail",
          evidence: "LLM thinks the structure is not recommendation-like enough.",
          reason: "This is a soft platform-fit concern.",
          suggested_fix: "Adjust structure if needed.",
          target_agent: "body",
        },
      ],
    });

    const report = await inspectQualityWithLlm({
      article: {
        title: "Example GEO Optimization Guide",
        summary:
          "A official site article for Marketing teams about GEO optimization.",
        body_markdown:
          "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
        image_suggestions: [],
        changed_sections: [],
        requires_user_confirmation: [],
      },
      factCard: confirmedFactCard,
      platform: "official_site",
      sourceImages: [],
    });

    const platformCheck = report.checks.find(
      (check) => check.rule_id === "platform_fit",
    );
    expect(platformCheck?.status).toBe("warn");
    expect(report.overall_status).not.toBe("fail");
  });
});