# -*- coding: utf-8 -*-

from pathlib import Path

from pluto.doc_summary import (
    DocSummary,
    apply_doc_summary_context,
    generate_doc_summary,
    save_doc_summaries,
)


def test_generate_doc_summary_returns_valid_summary_with_mocked_llm(monkeypatch, tmp_path):
    corpus = tmp_path / "corpus"
    corpus.mkdir()
    (corpus / "paper.md").write_text("# Paper\n\nThis is about retrieval.", encoding="utf-8")

    monkeypatch.setattr(
        "pluto.doc_summary._call_summary_llm",
        lambda **kwargs: """
        {
          "title": "Retrieval Paper",
          "domain": "information retrieval",
          "key_claims": ["Chunk context improves retrieval"],
          "structure": ["intro", "methodology", "results"],
          "open_questions": ["How robust is it?"]
        }
        """,
    )

    summary = generate_doc_summary("paper", corpus)

    assert isinstance(summary, DocSummary)
    assert summary.doc_id == "paper"
    assert summary.title == "Retrieval Paper"
    assert summary.domain == "information retrieval"
    assert summary.key_claims == ["Chunk context improves retrieval"]


def test_generate_doc_summary_falls_back_when_llm_fails(monkeypatch, tmp_path):
    corpus = tmp_path / "corpus"
    corpus.mkdir()
    (corpus / "paper.md").write_text("# Paper\n\nBody.", encoding="utf-8")

    def fail(**kwargs):
        raise RuntimeError("model unavailable")

    monkeypatch.setattr("pluto.doc_summary._call_summary_llm", fail)

    summary = generate_doc_summary("paper", corpus)

    assert summary.doc_id == "paper"
    assert summary.title == "paper"
    assert summary.key_claims == []
    assert summary.open_questions == []


def test_context_prefix_is_prepended_to_chunk_text(tmp_path):
    corpus = tmp_path / "corpus"
    corpus.mkdir()
    summary = DocSummary(
        doc_id="paper",
        title="Retrieval Paper",
        domain="AI",
        key_claims=["Claim A", "Claim B"],
        structure=[],
        open_questions=[],
        created_at="2026-01-01T00:00:00+00:00",
    )
    save_doc_summaries(corpus, {"paper": summary})

    result = apply_doc_summary_context("Original chunk", "paper", corpus)

    assert result.startswith("[Document context: Retrieval Paper | Domain: AI | Key claims: Claim A; Claim B]")
    assert result.endswith("Original chunk")