from unittest.mock import MagicMock
import pytest
from src.core.settings import Settings
from src.ingestion.embedding.dense_encoder import DenseEncoder
from src.ingestion.models import Chunk
from src.libs.embedding.base_embedding import BaseEmbedding
def test_dense_encoder_outputs_count_and_dimension():
settings = MagicMock(spec=Settings)
embedding = MagicMock(spec=BaseEmbedding)
embedding.embed.return_value = [[0.0, 1.0], [2.0, 3.0]]
encoder = DenseEncoder(settings, embedding=embedding)
chunks = [Chunk(text="a"), Chunk(text="b")]
vectors = encoder.encode(chunks)
assert len(vectors) == 2
assert all(len(v) == 2 for v in vectors)
assert embedding.embed.call_args.args[0] == ["a", "b"]
def test_dense_encoder_empty_input():
settings = MagicMock(spec=Settings)
embedding = MagicMock(spec=BaseEmbedding)
encoder = DenseEncoder(settings, embedding=embedding)
vectors = encoder.encode([])
assert vectors == []
embedding.embed.assert_not_called()
def test_dense_encoder_raises_on_count_mismatch():
settings = MagicMock(spec=Settings)
embedding = MagicMock(spec=BaseEmbedding)
embedding.embed.return_value = [[0.0, 1.0]]
encoder = DenseEncoder(settings, embedding=embedding)
chunks = [Chunk(text="a"), Chunk(text="b")]
with pytest.raises(ValueError, match="vector count mismatch"):
encoder.encode(chunks)
def test_dense_encoder_raises_on_dimension_mismatch():
settings = MagicMock(spec=Settings)
embedding = MagicMock(spec=BaseEmbedding)
embedding.embed.return_value = [[0.0, 1.0], [2.0]]
encoder = DenseEncoder(settings, embedding=embedding)
chunks = [Chunk(text="a"), Chunk(text="b")]
with pytest.raises(ValueError, match="vector dimension mismatch"):
encoder.encode(chunks)
