import sys
from pathlib import Path

import pytest

REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

from pageindex.filesystem.semantic_index import (
    SemanticIndexRecord,
    SQLiteVecSemanticIndex,
)


def test_sqlite_vec_semantic_index_round_trip(tmp_path):
    index = SQLiteVecSemanticIndex(tmp_path / "semantic.sqlite")
    index.reset(dimension=3, metadata={"field_mode": "summary"})

    index.upsert_many(
        [
            SemanticIndexRecord(
                file_ref="file_a",
                external_id="doc_a",
                source_type="github",
                source_path="github/a.json",
                title="Multipart upload limits",
                text="multipart upload limits",
                vector=[1.0, 0.0, 0.0],
                metadata={"topic": "uploads"},
            ),
            SemanticIndexRecord(
                file_ref="file_b",
                external_id="doc_b",
                source_type="slack",
                source_path="slack/b.json",
                title="GPU cache issue",
                text="gpu cache issue",
                vector=[0.0, 1.0, 0.0],
                metadata={"topic": "runtime"},
            ),
        ]
    )

    assert index.info()["document_count"] == 2

    results = index.search([0.9, 0.1, 0.0], limit=2)
    assert [item.external_id for item in results] == ["doc_a", "doc_b"]

    filtered = index.search(
        [0.9, 0.1, 0.0],
        limit=2,
        filters={"source_type": "slack"},
    )
    assert [item.external_id for item in filtered] == ["doc_b"]


def test_sqlite_vec_semantic_index_file_ref_filter_not_limited_by_global_rank(tmp_path):
    index = SQLiteVecSemanticIndex(tmp_path / "semantic.sqlite")
    index.reset(dimension=2, metadata={"field_mode": "summary"})

    records = [
        SemanticIndexRecord(
            file_ref=f"file_off_{item:02d}",
            external_id=f"doc_off_{item:02d}",
            source_type="documents",
            source_path=f"other/{item:02d}.pdf",
            title=f"Off scope {item:02d}",
            text="off scope",
            vector=[1.0, 0.0],
        )
        for item in range(30)
    ]
    records.append(
        SemanticIndexRecord(
            file_ref="file_in_scope",
            external_id="doc_in_scope",
            source_type="documents",
            source_path="documents/in-scope.pdf",
            title="In scope",
            text="in scope",
            vector=[0.0, 1.0],
        )
    )
    index.upsert_many(records)

    results = index.search(
        [1.0, 0.0],
        limit=1,
        filters={"file_ref": ["file_in_scope"]},
    )

    assert [item.file_ref for item in results] == ["file_in_scope"]


def test_summary_projection_indexes_unified_metadata_summary(tmp_path):
    from pageindex.filesystem.projection_indexing import SummaryProjectionIndexer

    class FakeEmbedder:
        def embed(self, texts):
            return [[1.0, 0.0, 0.0] for _ in texts]

    indexer = SummaryProjectionIndexer(
        tmp_path / "projection",
        embedder=FakeEmbedder(),
        embedding_provider="test",
        embedding_model="fake",
        embedding_dimensions=3,
    )

    result = indexer.upsert_summary(
        {
            "file_ref": "file_a",
            "external_id": "doc_a",
            "source_type": "documents",
            "source_path": "docs/a.pdf",
            "title": "A",
            "metadata": {
                "summary": "Unified metadata summary.",
                "department": "ops",
            },
        }
    )

    assert result["status"] == "ready"
    hits = indexer.index.search([1.0, 0.0, 0.0], limit=1)
    assert hits[0].external_id == "doc_a"
    assert hits[0].metadata["summary"] == "Unified metadata summary."
    assert hits[0].metadata["department"] == "ops"


def test_summary_projection_dimension_mismatch_preserves_existing_index(tmp_path):
    from pageindex.filesystem.projection_indexing import SummaryProjectionIndexer

    class FakeEmbedder:
        def embed(self, texts):
            return [[1.0, 0.0, 0.0, 0.0] for _ in texts]

    index_dir = tmp_path / "projection"
    index = SQLiteVecSemanticIndex(index_dir / "summary_only_vector.sqlite")
    index.reset(
        dimension=3,
        metadata={
            "channel": "summary",
            "embedding_provider": "test",
            "embedding_model": "fake",
            "embedding_dimensions": 3,
        },
    )
    index.upsert_many(
        [
            SemanticIndexRecord(
                file_ref="file_a",
                external_id="doc_a",
                source_type="documents",
                source_path="docs/a.pdf",
                title="A",
                text="summary",
                vector=[1.0, 0.0, 0.0],
            )
        ]
    )

    with pytest.raises(RuntimeError, match="summary projection index dimension mismatch"):
        SummaryProjectionIndexer(
            index_dir,
            embedder=FakeEmbedder(),
            embedding_provider="test",
            embedding_model="fake",
            embedding_dimensions=4,
        )

    preserved = SQLiteVecSemanticIndex(index.db_path)
    assert preserved.info()["dimension"] == 3
    assert preserved.info()["document_count"] == 1
    assert preserved.search([1.0, 0.0, 0.0], limit=1)[0].external_id == "doc_a"


def test_hash_embedding_provider_is_not_available():
    from pageindex.filesystem.hybrid_projection import make_embedder

    with pytest.raises(ValueError, match="unknown embedding provider: hash"):
        make_embedder("hash", "unused", dimensions=256, timeout=1)