"""Retrieval quality eval over the golden query set (`make eval`).

Runs against the CONFIGURED embedding endpoint — it must be live. The
corpus is indexed as public documents for a single eval user: this measures
retrieval QUALITY; permission behavior is covered by the unit tests.
"""

import pytest
from sqlalchemy.ext.asyncio import AsyncSession

from app.auth.passwords import hash_password
from app.models import (
    Department,
    Document,
    DocumentStatus,
    DocumentVisibility,
    User,
    UserRole,
)
from app.rag.indexing import reindex_document
from app.rag.retrieval import results_are_low_confidence, search
from tests.fixtures.loader import load_corpus, load_golden_queries

pytestmark = pytest.mark.eval

RECALL_FLOOR = 0.8  # baseline 2026-07: 25/25 = 1.00


async def test_retrieval_golden_set(db: AsyncSession) -> None:
    corpus = load_corpus()
    queries = load_golden_queries()

    department = Department(name="Eval")
    db.add(department)
    await db.flush()
    user = User(
        email="eval@test.dev",
        name="Eval User",
        role=UserRole.member,
        password_hash=hash_password("eval-only"),
        department_id=department.id,
    )
    db.add(user)
    await db.flush()

    slug_by_document_id = {}
    for doc in corpus:
        document = Document(
            title=doc.title,
            status=DocumentStatus.published,
            visibility=DocumentVisibility.public,
            content_md=doc.content_md,
            meta={"slug": doc.slug},
            author_id=user.id,
            department_id=department.id,
        )
        db.add(document)
        await db.flush()
        slug_by_document_id[document.id] = doc.slug
        await reindex_document(db, document)  # real embeddings
    await db.commit()

    hits = 0
    expected_total = 0
    misses: list[str] = []
    no_answer_violations: list[str] = []
    report: list[str] = []

    for entry in queries:
        results = await search(db, entry["query"], user=user, top_k=5)
        top_slugs = [slug_by_document_id[r.document_id] for r in results]
        if entry["expected"]:
            expected_total += 1
            hit = any(slug in entry["expected"] for slug in top_slugs)
            hits += int(hit)
            if not hit:
                misses.append(entry["query"])
            report.append(
                f"{'HIT ' if hit else 'MISS'} {entry['query'][:58]!r} -> {top_slugs[:3]}"
            )
        else:
            top = results[0] if results else None
            fts = top.fts_match if top else False
            distance = top.vector_distance if top else None
            report.append(
                f"NOANS {entry['query'][:58]!r} fts={fts} distance={distance:.3f}"
                if distance is not None
                else f"NOANS {entry['query'][:58]!r} fts={fts} distance=None"
            )
            confident_nothing = results_are_low_confidence(results)
            if not confident_nothing:
                no_answer_violations.append(entry["query"])

    recall = hits / expected_total
    print("\n".join(report))
    print(f"\nrecall@5: {hits}/{expected_total} = {recall:.2f}")

    assert recall >= RECALL_FLOOR, f"recall {recall:.2f} below floor; misses: {misses}"
    assert not no_answer_violations, (
        f"no-answer queries returned confident results: {no_answer_violations}"
    )