import tempfile
from pathlib import Path
from unittest.mock import MagicMock, patch

import pytest

from companion.rag.search import SearchEngine
from companion.rag.vector_store import VectorStore


@patch("companion.rag.search.OllamaEmbedder")
def test_search_returns_results(mock_embedder_cls):
    mock_embedder = MagicMock()
    mock_embedder.embed.return_value = [[1.0, 0.0, 0.0, 0.0]]
    mock_embedder_cls.return_value = mock_embedder

    with tempfile.TemporaryDirectory() as tmp:
        store = VectorStore(uri=tmp, dimensions=4)
        store.upsert(
            ids=["a"],
            texts=["hello world"],
            embeddings=[[1.0, 0.0, 0.0, 0.0]],
            metadatas=[{"source_file": "a.md", "source_directory": "docs"}],
        )
        engine = SearchEngine(
            vector_store=store,
            embedder_base_url="http://localhost:11434",
            embedder_model="dummy",
            embedder_batch_size=32,
            default_top_k=5,
            similarity_threshold=0.0,
            hybrid_search_enabled=False,
        )
        results = engine.search("hello")
        assert len(results) == 1
        assert results[0]["source_file"] == "a.md"


@patch("companion.rag.search.OllamaEmbedder")
def test_search_raises_on_embedder_failure(mock_embedder_cls):
    mock_embedder = MagicMock()
    mock_embedder.embed.side_effect = RuntimeError("Connection failed")
    mock_embedder_cls.return_value = mock_embedder

    with tempfile.TemporaryDirectory() as tmp:
        store = VectorStore(uri=tmp, dimensions=4)
        engine = SearchEngine(
            vector_store=store,
            embedder_base_url="http://localhost:11434",
            embedder_model="dummy",
            embedder_batch_size=32,
            default_top_k=5,
            similarity_threshold=0.0,
            hybrid_search_enabled=False,
        )
        with pytest.raises(RuntimeError, match="Failed to generate embedding"):
            engine.search("hello")