AI/ontology_platform/tests/unit/test_convert_document.py

"""Regression tests for the OntoCast `convert_document` agent.

Covers the multi-file corpus extension described in
`docs/통합설계서.md` §5 Phase 0 and OntoCast 분석 §13.1 / §21.1.

Original behavior: the loop overwrote `state.input_text` on every iteration,
so multi-file input silently lost all but the last file. The fix accumulates
into one corpus with explicit file-boundary separators while keeping
single-file behavior byte-identical.
"""

from __future__ import annotations

import json
import sys
from pathlib import Path
from types import SimpleNamespace

import pytest

REPO_ROOT = Path(__file__).resolve().parents[2]
VENDORED_ONTOCAST = REPO_ROOT / "vendored" / "ontocast"
if str(VENDORED_ONTOCAST) not in sys.path:
    sys.path.insert(0, str(VENDORED_ONTOCAST))

import sys

import ontocast.agent  # __init__.py 실행으로 서브모듈이 sys.modules에 등록됨  # noqa: E402

convert_document_module = sys.modules["ontocast.agent.convert_document"]
from ontocast.onto.enum import Status  # noqa: E402


class _StubConverter:
    """Minimal stand-in for `ConverterTool`.

    Accepts a fake PDF/DOCX file (any bytes) and returns a dict shaped like
    the real converter's output.
    """

    supported_extensions = {".pdf", ".docx"}

    def __init__(self, mapping: dict[bytes, str]) -> None:
        self._mapping = mapping

    def __call__(self, file_content: bytes) -> dict[str, str]:
        return {"text": self._mapping[file_content]}


def _make_state(files: dict[str, bytes]) -> SimpleNamespace:
    """Build a lightweight stand-in for `AgentState`.

    We intentionally avoid constructing the real Pydantic model here — its
    initialization touches many unrelated fields and tools. We only mirror
    the attributes that `convert_document` reads or writes.
    """
    captured_text: list[str] = []

    def set_text(text: str) -> None:
        captured_text.append(text)

    state = SimpleNamespace(
        files=files,
        status=None,
        input_text="",
        ontology_user_instruction="",
        facts_user_instruction="",
        source_url=None,
        set_text=set_text,
        _captured_text=captured_text,
    )
    return state


def _make_tools(converter_mapping: dict[bytes, str]) -> SimpleNamespace:
    return SimpleNamespace(converter=_StubConverter(converter_mapping))


# ─── Case 1: single PDF — output must equal the file's text verbatim ─────
def test_single_pdf_passes_through_unchanged() -> None:
    pdf_bytes = b"%PDF-1.4 fake"
    state = _make_state({"sample.pdf": pdf_bytes})
    tools = _make_tools({pdf_bytes: "Hello from PDF."})

    result = convert_document_module.convert_document(state, tools)

    assert result.status == Status.SUCCESS
    assert state._captured_text == ["Hello from PDF."]


# ─── Case 2: single JSON — text + corpus metadata flows through ──────────
def test_single_json_extracts_metadata_and_text() -> None:
    payload = {
        "text": "Body of the article.",
        "url": "https://example.com/a",
        "ontology_user_instruction": "Focus on organizations.",
        "facts_user_instruction": "Extract person-org links.",
    }
    state = _make_state({"a.json": json.dumps(payload).encode("utf-8")})
    tools = _make_tools({})

    result = convert_document_module.convert_document(state, tools)

    assert result.status == Status.SUCCESS
    assert state._captured_text == ["Body of the article."]
    assert state.source_url == "https://example.com/a"
    assert state.ontology_user_instruction == "Focus on organizations."
    assert state.facts_user_instruction == "Extract person-org links."


# ─── Case 3: multiple PDFs — both bodies survive with boundary marker ────
def test_multiple_pdfs_are_concatenated_with_boundary() -> None:
    """The legacy bug: only the last file's text survived. After the fix,
    both texts appear in the corpus separated by `=== File: <name> ===`."""
    pdf_a = b"%PDF-1.4 A"
    pdf_b = b"%PDF-1.4 B"
    state = _make_state({"a.pdf": pdf_a, "b.pdf": pdf_b})
    tools = _make_tools({pdf_a: "Text A.", pdf_b: "Text B."})

    convert_document_module.convert_document(state, tools)

    corpus = state._captured_text[-1]
    assert "Text A." in corpus
    assert "Text B." in corpus
    assert "=== File: a.pdf ===" in corpus
    assert "=== File: b.pdf ===" in corpus
    # Ordering: a.pdf before b.pdf (insertion order preserved)
    assert corpus.index("Text A.") < corpus.index("Text B.")


# ─── Case 4: multiple JSONs — first-wins for corpus metadata ─────────────
def test_multiple_jsons_keep_first_metadata() -> None:
    """`ontology_user_instruction`, `facts_user_instruction`, `source_url`
    are corpus-level singletons. The first JSON file that provides each
    wins; later JSONs do not overwrite."""
    payload_a = {
        "text": "Body A.",
        "url": "https://first.example/a",
        "ontology_user_instruction": "First instruction.",
        "facts_user_instruction": "First facts.",
    }
    payload_b = {
        "text": "Body B.",
        "url": "https://second.example/b",
        "ontology_user_instruction": "Second instruction (must be ignored).",
        "facts_user_instruction": "Second facts (must be ignored).",
    }
    state = _make_state(
        {
            "a.json": json.dumps(payload_a).encode("utf-8"),
            "b.json": json.dumps(payload_b).encode("utf-8"),
        }
    )
    tools = _make_tools({})

    convert_document_module.convert_document(state, tools)

    assert state.source_url == "https://first.example/a"
    assert state.ontology_user_instruction == "First instruction."
    assert state.facts_user_instruction == "First facts."
    corpus = state._captured_text[-1]
    assert "Body A." in corpus and "Body B." in corpus


# ─── Case 5: unsupported extension fails fast ────────────────────────────
def test_unsupported_extension_returns_failed() -> None:
    state = _make_state({"weird.xyz": b"???"})
    tools = _make_tools({})

    result = convert_document_module.convert_document(state, tools)

    assert result.status == Status.FAILED


# ─── Case 6: empty files dict is a no-op success ─────────────────────────
def test_empty_files_is_noop_success() -> None:
    state = _make_state({})
    tools = _make_tools({})

    result = convert_document_module.convert_document(state, tools)

    assert result.status == Status.SUCCESS
    assert state._captured_text == []  # set_text never called


# ─── Case 7: mixed PDF + JSON in one corpus ──────────────────────────────
def test_mixed_pdf_and_json_combine_with_boundaries() -> None:
    pdf_bytes = b"%PDF-1.4 mix"
    json_payload = {"text": "JSON body."}
    state = _make_state(
        {
            "a.pdf": pdf_bytes,
            "b.json": json.dumps(json_payload).encode("utf-8"),
        }
    )
    tools = _make_tools({pdf_bytes: "PDF body."})

    convert_document_module.convert_document(state, tools)

    corpus = state._captured_text[-1]
    assert "=== File: a.pdf ===" in corpus
    assert "=== File: b.json ===" in corpus
    assert "PDF body." in corpus
    assert "JSON body." in corpus