Timmy-time-dashboard/tests/unit/test_memory_embeddings.py

"""Unit tests for timmy.memory.embeddings — embedding, similarity, and keyword overlap."""

import math
from unittest.mock import MagicMock, patch

import pytest

import timmy.memory.embeddings as emb
from timmy.memory.embeddings import (
    _keyword_overlap,
    _simple_hash_embedding,
    cosine_similarity,
    embed_text,
)

# ── _simple_hash_embedding ──────────────────────────────────────────────────


class TestSimpleHashEmbedding:
    def test_returns_128_dim_vector(self):
        vec = _simple_hash_embedding("hello world")
        assert len(vec) == 128

    def test_normalized(self):
        vec = _simple_hash_embedding("some text for embedding")
        mag = math.sqrt(sum(x * x for x in vec))
        assert mag == pytest.approx(1.0, abs=1e-6)

    def test_deterministic(self):
        a = _simple_hash_embedding("same input")
        b = _simple_hash_embedding("same input")
        assert a == b

    def test_different_texts_differ(self):
        a = _simple_hash_embedding("hello world")
        b = _simple_hash_embedding("goodbye moon")
        assert a != b

    def test_empty_string(self):
        vec = _simple_hash_embedding("")
        assert len(vec) == 128
        # All zeros normalised stays zero (mag fallback to 1.0)
        assert all(x == 0.0 for x in vec)

    def test_long_text_truncates_at_50_words(self):
        """Words beyond 50 should not change the result."""
        short = " ".join(f"word{i}" for i in range(50))
        long = short + " extra1 extra2 extra3"
        assert _simple_hash_embedding(short) == _simple_hash_embedding(long)


# ── cosine_similarity ────────────────────────────────────────────────────────


class TestCosineSimilarity:
    def test_identical_vectors(self):
        v = [1.0, 2.0, 3.0]
        assert cosine_similarity(v, v) == pytest.approx(1.0)

    def test_orthogonal_vectors(self):
        a = [1.0, 0.0]
        b = [0.0, 1.0]
        assert cosine_similarity(a, b) == pytest.approx(0.0)

    def test_opposite_vectors(self):
        a = [1.0, 0.0]
        b = [-1.0, 0.0]
        assert cosine_similarity(a, b) == pytest.approx(-1.0)

    def test_zero_vector_returns_zero(self):
        assert cosine_similarity([0.0, 0.0], [1.0, 2.0]) == 0.0
        assert cosine_similarity([1.0, 2.0], [0.0, 0.0]) == 0.0

    def test_both_zero_vectors(self):
        assert cosine_similarity([0.0], [0.0]) == 0.0


# ── _keyword_overlap ─────────────────────────────────────────────────────────


class TestKeywordOverlap:
    def test_full_overlap(self):
        assert _keyword_overlap("hello world", "hello world") == pytest.approx(1.0)

    def test_partial_overlap(self):
        assert _keyword_overlap("hello world", "hello moon") == pytest.approx(0.5)

    def test_no_overlap(self):
        assert _keyword_overlap("hello", "goodbye") == pytest.approx(0.0)

    def test_empty_query(self):
        assert _keyword_overlap("", "anything") == 0.0

    def test_case_insensitive(self):
        assert _keyword_overlap("Hello World", "hello world") == pytest.approx(1.0)


# ── embed_text ───────────────────────────────────────────────────────────────


class TestEmbedText:
    def setup_method(self):
        self._saved_model = emb.EMBEDDING_MODEL
        emb.EMBEDDING_MODEL = None

    def teardown_method(self):
        emb.EMBEDDING_MODEL = self._saved_model

    def test_uses_fallback_when_model_disabled(self):
        emb.EMBEDDING_MODEL = False
        vec = embed_text("test")
        assert len(vec) == 128  # hash fallback dimension

    def test_uses_model_when_available(self):
        import numpy as np

        mock_model = MagicMock()
        mock_model.encode.return_value = np.array([0.1, 0.2, 0.3])
        emb.EMBEDDING_MODEL = mock_model

        result = embed_text("test")
        assert result == pytest.approx([0.1, 0.2, 0.3])
        mock_model.encode.assert_called_once_with("test")


# ── _get_embedding_model ─────────────────────────────────────────────────────


class TestGetEmbeddingModel:
    def setup_method(self):
        self._saved_model = emb.EMBEDDING_MODEL
        emb.EMBEDDING_MODEL = None

    def teardown_method(self):
        emb.EMBEDDING_MODEL = self._saved_model

    def test_skip_embeddings_setting(self):
        mock_settings = MagicMock()
        mock_settings.timmy_skip_embeddings = True
        with patch.dict("sys.modules", {"config": MagicMock(settings=mock_settings)}):
            emb.EMBEDDING_MODEL = None
            result = emb._get_embedding_model()
        assert result is False

    def test_fallback_when_transformers_missing(self):
        mock_settings = MagicMock()
        mock_settings.timmy_skip_embeddings = False
        with patch.dict(
            "sys.modules",
            {
                "config": MagicMock(settings=mock_settings),
                "sentence_transformers": None,
            },
        ):
            emb.EMBEDDING_MODEL = None
            result = emb._get_embedding_model()
        assert result is False

    def test_returns_cached_model(self):
        sentinel = object()
        emb.EMBEDDING_MODEL = sentinel
        assert emb._get_embedding_model() is sentinel