"""Tests for services.embeddings — fastembed backend. We don't actually load the fastembed model in tests (heavy download). Instead, mock _get_model to return a fake that produces deterministic vectors. """ from unittest.mock import AsyncMock, MagicMock, patch import pytest from scribe.services.embeddings import ( _cosine_similarity, get_embedding, ) # ─── cosine_similarity (pure logic) ────────────────────────────────────────── def test_cosine_similarity_orthogonal_is_zero(): assert _cosine_similarity([1.0, 0.0], [0.0, 1.0]) == 0.0 def test_cosine_similarity_identical_is_one(): assert _cosine_similarity([1.0, 0.0], [1.0, 0.0]) == pytest.approx(1.0) def test_cosine_similarity_opposite_is_negative_one(): assert _cosine_similarity([1.0, 0.0], [-1.0, 0.0]) == pytest.approx(-1.0) def test_cosine_similarity_zero_length_safe(): """Zero-magnitude vector must not divide-by-zero.""" assert _cosine_similarity([0.0, 0.0], [1.0, 0.0]) == 0.0 assert _cosine_similarity([1.0, 0.0], [0.0, 0.0]) == 0.0 def test_cosine_similarity_mismatched_dim_returns_zero(): """Cross-migration safety: a 768-dim vs 384-dim comparison must not crash.""" assert _cosine_similarity([1.0] * 5, [1.0] * 3) == 0.0 def test_cosine_similarity_empty_inputs(): assert _cosine_similarity([], []) == 0.0 assert _cosine_similarity([], [1.0]) == 0.0 # ─── get_embedding (fastembed path) ────────────────────────────────────────── @pytest.mark.asyncio async def test_get_embedding_returns_list_of_floats(): """get_embedding wraps the embedder; the result is a Python list of floats.""" fake_vec = MagicMock() fake_vec.tolist.return_value = [0.1, 0.2, 0.3, 0.4] fake_embedder = MagicMock() fake_embedder.embed = MagicMock(return_value=iter([fake_vec])) with patch( "scribe.services.embeddings._get_model", AsyncMock(return_value=fake_embedder), ): out = await get_embedding("hello world") assert out == [0.1, 0.2, 0.3, 0.4] fake_embedder.embed.assert_called_once_with(["hello world"]) @pytest.mark.asyncio async def test_get_embedding_propagates_model_load_failures(): """If fastembed can't initialize, the error propagates — callers catch and degrade to keyword search.""" with patch( "scribe.services.embeddings._get_model", AsyncMock(side_effect=RuntimeError("model load failed")), ): with pytest.raises(RuntimeError, match="model load failed"): await get_embedding("x")