diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000000000000000000000000000000000000..09dc40d305813f3444d710896bd0ca4af484d6bf --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,41 @@ +name: Test + +on: + push: + branches: + - main + pull_request: + +env: + POETRY_VERSION: "1.5.1" + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.11" + steps: + - uses: actions/checkout@v4 + - name: Cache Poetry + uses: actions/cache@v3 + with: + path: ~/.poetry + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + - name: Install poetry + run: | + pipx install poetry==$POETRY_VERSION + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: poetry + - name: Install dependencies + run: | + poetry install + - name: Pytest + run: | + make test diff --git a/.gitignore b/.gitignore index df57182d1a723b6eef2b8c4855918fb7a0907bd4..5e807c4d1ed56d35548cc859123a3cc2666acbcb 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,6 @@ venv/ .env*.local .env mac.env + +# Code coverage history +.coverage diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..2c0733315e415bfb5e5b353f9996ecd964d395b2 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/Makefile b/Makefile index 6fcc35a18cc71e9e22b0b8793f1789aa589a7d0c..372221c63b5f6ba50b28a1c171645b44d4e94f0e 100644 --- a/Makefile +++ b/Makefile @@ -9,3 +9,6 @@ lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep lint lint_diff: poetry run black $(PYTHON_FILES) --check poetry run ruff . + +test: + poetry run pytest -vv --cov=semantic_router --cov-report=term-missing --cov-fail-under=100 diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb index fa6b78c7ec8a9b18e1638135e352787236999b4f..98fccf6972026d62e6d53a3c96492ca90287207e 100644 --- a/docs/examples/hybrid-layer.ipynb +++ b/docs/examples/hybrid-layer.ipynb @@ -46,30 +46,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", - "os.environ[\"COHERE_API_KEY\"] = \"<<COHERE_API_KEY>>\"" + "os.environ[\"COHERE_API_KEY\"] = \"BQBiUqqjDRsYl1QKKux4JsqKdDkjyInS5T3Z3eJP\"" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jamesbriggs/opt/anaconda3/envs/decision-layer/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from semantic_router.schema import Decision\n", "\n", @@ -95,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -167,40 +157,18 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'politics'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dl(\"don't you love politics?\")" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'chitchat'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dl(\"how's the weather today?\")" ] diff --git a/poetry.lock b/poetry.lock index f47ac40ca97c59a3e5e3d18ea89f13bd816f3729..8aaee95e2f97fcca98a70d0b023978aabf71d13e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,111 +2,99 @@ [[package]] name = "aiohttp" -version = "3.8.6" +version = "3.9.1" description = "Async http client/server framework (asyncio)" optional = false -python-versions = ">=3.6" +python-versions = ">=3.8" files = [ - {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, - {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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authors = [ "James Briggs <james@aurelio.ai>", "Siraj Aizlewood <siraj@aurelio.ai>", "Simonas Jakubonis <simonas@aurelio.ai>", - "Luca Mannini <luca@aurelio.ai>" + "Luca Mannini <luca@aurelio.ai>", + "Bogdan Buduroiu <bogdan@aurelio.ai>" ] readme = "README.md" @@ -15,6 +16,7 @@ python = "^3.10" pydantic = "^1.8.2" openai = "^0.28.1" cohere = "^4.32" +numpy = "^1.25.2" pinecone-text = "^0.7.0" @@ -22,6 +24,9 @@ pinecone-text = "^0.7.0" ipykernel = "^6.26.0" ruff = "^0.1.5" black = "^23.11.0" +pytest = "^7.4.3" +pytest-mock = "^3.12.0" +pytest-cov = "^4.1.0" [build-system] requires = ["poetry-core"] diff --git a/semantic_router/layer.py b/semantic_router/layer.py index 3bb685ccb20b65f1437e5b25df7b30582c4f311a..0c00e91664e606c91c959c990d9d1cf6123b6d3a 100644 --- a/semantic_router/layer.py +++ b/semantic_router/layer.py @@ -8,6 +8,7 @@ from semantic_router.encoders import ( OpenAIEncoder, BM25Encoder ) +from semantic_router.linear import similarity_matrix, top_scores from semantic_router.schema import Decision @@ -69,13 +70,9 @@ class DecisionLayer: xq = np.squeeze(xq) # Reduce to 1d array. if self.index is not None: - index_norm = norm(self.index, axis=1) - xq_norm = norm(xq.T) - sim = np.dot(self.index, xq.T) / (index_norm * xq_norm) - # get indices of top_k records - top_k = min(top_k, sim.shape[0]) - idx = np.argpartition(sim, -top_k)[-top_k:] - scores = sim[idx] + # calculate similarity matrix + sim = similarity_matrix(xq, self.index) + scores, idx = top_scores(sim, top_k) # get the utterance categories (decision names) decisions = self.categories[idx] if self.categories is not None else [] return [ diff --git a/semantic_router/linear.py b/semantic_router/linear.py new file mode 100644 index 0000000000000000000000000000000000000000..1c13262fbe55bfe4cd92f75c61fb33899d60337e --- /dev/null +++ b/semantic_router/linear.py @@ -0,0 +1,30 @@ +from typing import Tuple + +import numpy as np +from numpy.linalg import norm + + +def similarity_matrix(xq: np.ndarray, index: np.ndarray) -> np.ndarray: + """Compute the similarity scores between a query vector and a set of vectors. + + Args: + xq: A query vector (1d ndarray) + index: A set of vectors. + + Returns: + The similarity between the query vector and the set of vectors. + """ + + index_norm = norm(index, axis=1) + xq_norm = norm(xq.T) + sim = np.dot(index, xq.T) / (index_norm * xq_norm) + return sim + + +def top_scores(sim: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, np.ndarray]: + # get indices of top_k records + top_k = min(top_k, sim.shape[0]) + idx = np.argpartition(sim, -top_k)[-top_k:] + scores = sim[idx] + + return scores, idx diff --git a/tests/functional/test_linear.py b/tests/functional/test_linear.py new file mode 100644 index 0000000000000000000000000000000000000000..210de6d200495cb37c3c46ab11c792efb8505dea --- /dev/null +++ b/tests/functional/test_linear.py @@ -0,0 +1,69 @@ +import numpy as np +import pytest + +from semantic_router.linear import similarity_matrix, top_scores + + +@pytest.fixture +def ident_vector(): + return np.identity(10)[0] + + +@pytest.fixture +def test_index(): + return np.array([[3, 0, 0], [2, 1, 0], [0, 1, 0]]) + + +def test_similarity_matrix__dimensionality(): + """Test that the similarity matrix is square.""" + xq = np.random.random((10,)) # 10-dimensional embedding vector + index = np.random.random((100, 10)) + S = similarity_matrix(xq, index) + assert S.shape == (100,) + + +def test_similarity_matrix__is_norm_max(ident_vector): + """ + Using identical vectors should yield a maximum similarity of 1 + """ + index = np.repeat(np.atleast_2d(ident_vector), 3, axis=0) + sim = similarity_matrix(ident_vector, index) + assert sim.max() == 1.0 + + +def test_similarity_matrix__is_norm_min(ident_vector): + """ + Using orthogonal vectors should yield a minimum similarity of 0 + """ + orth_v = np.roll(np.atleast_2d(ident_vector), 1) + index = np.repeat(orth_v, 3, axis=0) + sim = similarity_matrix(ident_vector, index) + assert sim.min() == 0.0 + + +def test_top_scores__is_sorted(test_index): + """ + Test that the top_scores function returns a sorted list of scores. + """ + + xq = test_index[0] # should have max similarity + + sim = similarity_matrix(xq, test_index) + _, idx = top_scores(sim, 3) + + # Scores and indexes should be sorted ascending + assert np.array_equal(idx, np.array([2, 1, 0])) + + +def test_top_scores__scores(test_index): + """ + Test that for a known vector and a known index, the top_scores function + returns exactly the expected scores. + """ + xq = test_index[0] # should have max similarity + + sim = similarity_matrix(xq, test_index) + scores, _ = top_scores(sim, 3) + + # Scores and indexes should be sorted ascending + assert np.allclose(scores, np.array([0.0, 0.89442719, 1.0])) diff --git a/tests/unit/encoders/test_base.py b/tests/unit/encoders/test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..d2c39645d6bb6091b2d17c21cad5bee0aafe5079 --- /dev/null +++ b/tests/unit/encoders/test_base.py @@ -0,0 +1,16 @@ +import pytest + +from semantic_router.encoders import BaseEncoder + + +class TestBaseEncoder: + @pytest.fixture + def base_encoder(self): + return BaseEncoder(name="TestEncoder") + + def test_base_encoder_initialization(self, base_encoder): + assert base_encoder.name == "TestEncoder", "Initialization of name failed" + + def test_base_encoder_call_method_not_implemented(self, base_encoder): + with pytest.raises(NotImplementedError): + base_encoder(["some", "texts"]) diff --git a/tests/unit/encoders/test_cohere.py b/tests/unit/encoders/test_cohere.py new file mode 100644 index 0000000000000000000000000000000000000000..7f7ddf281244132c130e027fedc99e00829770de --- /dev/null +++ b/tests/unit/encoders/test_cohere.py @@ -0,0 +1,41 @@ +import pytest + +from semantic_router.encoders import CohereEncoder + + +@pytest.fixture +def cohere_encoder(mocker): + mocker.patch("cohere.Client") + return CohereEncoder(cohere_api_key="test_api_key") + + +class TestCohereEncoder: + def test_initialization_with_api_key(self, cohere_encoder): + assert cohere_encoder.client is not None, "Client should be initialized" + assert ( + cohere_encoder.name == "embed-english-v3.0" + ), "Default name not set correctly" + + def test_initialization_without_api_key(self, mocker, monkeypatch): + monkeypatch.delenv("COHERE_API_KEY", raising=False) + mocker.patch("cohere.Client") + with pytest.raises(ValueError): + CohereEncoder() + + def test_call_method(self, cohere_encoder, mocker): + mock_embed = mocker.MagicMock() + mock_embed.embeddings = [[0.1, 0.2, 0.3]] + cohere_encoder.client.embed.return_value = mock_embed + + result = cohere_encoder(["test"]) + assert isinstance(result, list), "Result should be a list" + assert all( + isinstance(sublist, list) for sublist in result + ), "Each item in result should be a list" + cohere_encoder.client.embed.assert_called_once() + + def test_call_with_uninitialized_client(self, mocker): + mocker.patch("cohere.Client", return_value=None) + encoder = CohereEncoder(cohere_api_key="test_api_key") + with pytest.raises(ValueError): + encoder(["test"]) diff --git a/tests/unit/encoders/test_openai.py b/tests/unit/encoders/test_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..23883fcc3581896b765e771e7c67f3a016603a1f --- /dev/null +++ b/tests/unit/encoders/test_openai.py @@ -0,0 +1,122 @@ +import openai +import pytest +from openai.error import RateLimitError + +from semantic_router.encoders import OpenAIEncoder + + +@pytest.fixture +def openai_encoder(mocker): + mocker.patch("openai.Embedding.create") + return OpenAIEncoder(name="test-engine", openai_api_key="test_api_key") + + +class TestOpenAIEncoder: + def test_initialization_with_api_key(self, openai_encoder): + assert openai.api_key == "test_api_key", "API key should be set correctly" + assert openai_encoder.name == "test-engine", "Engine name not set correctly" + + def test_initialization_without_api_key(self, mocker, monkeypatch): + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + mocker.patch("openai.Embedding.create") + with pytest.raises(ValueError): + OpenAIEncoder(name="test-engine") + + def test_call_method_success(self, openai_encoder, mocker): + mocker.patch( + "openai.Embedding.create", + return_value={"data": [{"embedding": [0.1, 0.2, 0.3]}]}, + ) + + result = openai_encoder(["test"]) + assert isinstance(result, list), "Result should be a list" + assert len(result) == 1 and len(result[0]) == 3, "Result list size is incorrect" + + def test_call_method_rate_limit_error__raises_value_error_after_max_retries( + self, openai_encoder, mocker + ): + mocker.patch("semantic_router.encoders.openai.sleep") + mocker.patch( + "openai.Embedding.create", + side_effect=RateLimitError(message="rate limit exceeded", http_status=429), + ) + + with pytest.raises(ValueError): + openai_encoder(["test"]) + + def test_call_method_failure(self, openai_encoder, mocker): + mocker.patch("openai.Embedding.create", return_value={}) + + with pytest.raises(ValueError): + openai_encoder(["test"]) + + def test_call_method_rate_limit_error__exponential_backoff_single_retry( + self, openai_encoder, mocker + ): + mock_sleep = mocker.patch("semantic_router.encoders.openai.sleep") + mocker.patch( + "openai.Embedding.create", + side_effect=[ + RateLimitError("rate limit exceeded"), + {"data": [{"embedding": [1, 2, 3]}]}, + ], + ) + + openai_encoder(["sample text"]) + + mock_sleep.assert_called_once_with(1) # 2**0 + + def test_call_method_rate_limit_error__exponential_backoff_multiple_retries( + self, openai_encoder, mocker + ): + mock_sleep = mocker.patch("semantic_router.encoders.openai.sleep") + mocker.patch( + "openai.Embedding.create", + side_effect=[ + RateLimitError("rate limit exceeded"), + RateLimitError("rate limit exceeded"), + {"data": [{"embedding": [1, 2, 3]}]}, + ], + ) + + openai_encoder(["sample text"]) + + assert mock_sleep.call_count == 2 + mock_sleep.assert_any_call(1) # 2**0 + mock_sleep.assert_any_call(2) # 2**1 + + def test_call_method_rate_limit_error__exponential_backoff_max_retries_exceeded( + self, openai_encoder, mocker + ): + mock_sleep = mocker.patch("semantic_router.encoders.openai.sleep") + mocker.patch( + "openai.Embedding.create", side_effect=RateLimitError("rate limit exceeded") + ) + + with pytest.raises(ValueError): + openai_encoder(["sample text"]) + + assert mock_sleep.call_count == 5 # Assuming 5 retries + mock_sleep.assert_any_call(1) # 2**0 + mock_sleep.assert_any_call(2) # 2**1 + mock_sleep.assert_any_call(4) # 2**2 + mock_sleep.assert_any_call(8) # 2**3 + mock_sleep.assert_any_call(16) # 2**4 + + def test_call_method_rate_limit_error__exponential_backoff_successful( + self, openai_encoder, mocker + ): + mock_sleep = mocker.patch("semantic_router.encoders.openai.sleep") + mocker.patch( + "openai.Embedding.create", + side_effect=[ + RateLimitError("rate limit exceeded"), + RateLimitError("rate limit exceeded"), + {"data": [{"embedding": [1, 2, 3]}]}, + ], + ) + + embeddings = openai_encoder(["sample text"]) + + assert mock_sleep.call_count == 2 + assert embeddings == [[1, 2, 3]] diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..96e06a086469b78c03dde08ff4508c33ea548095 --- /dev/null +++ b/tests/unit/test_layer.py @@ -0,0 +1,115 @@ +import pytest + +from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder +from semantic_router.layer import DecisionLayer # Replace with the actual module name +from semantic_router.schema import Decision + + +def mock_encoder_call(utterances): + # Define a mapping of utterances to return values + mock_responses = { + "Hello": [0.1, 0.2, 0.3], + "Hi": [0.4, 0.5, 0.6], + "Goodbye": [0.7, 0.8, 0.9], + "Bye": [1.0, 1.1, 1.2], + "Au revoir": [1.3, 1.4, 1.5], + } + return [mock_responses.get(u, [0, 0, 0]) for u in utterances] + + +@pytest.fixture +def base_encoder(): + return BaseEncoder(name="test-encoder") + + +@pytest.fixture +def cohere_encoder(mocker): + mocker.patch.object(CohereEncoder, "__call__", side_effect=mock_encoder_call) + return CohereEncoder(name="test-cohere-encoder", cohere_api_key="test_api_key") + + +@pytest.fixture +def openai_encoder(mocker): + mocker.patch.object(OpenAIEncoder, "__call__", side_effect=mock_encoder_call) + return OpenAIEncoder(name="test-openai-encoder", openai_api_key="test_api_key") + + +@pytest.fixture +def decisions(): + return [ + Decision(name="Decision 1", utterances=["Hello", "Hi"]), + Decision(name="Decision 2", utterances=["Goodbye", "Bye", "Au revoir"]), + ] + + +class TestDecisionLayer: + def test_initialization(self, openai_encoder, decisions): + decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions) + assert decision_layer.similarity_threshold == 0.82 + assert len(decision_layer.index) == 5 + assert len(set(decision_layer.categories)) == 2 + + def test_initialization_different_encoders(self, cohere_encoder, openai_encoder): + decision_layer_cohere = DecisionLayer(encoder=cohere_encoder) + assert decision_layer_cohere.similarity_threshold == 0.3 + + decision_layer_openai = DecisionLayer(encoder=openai_encoder) + assert decision_layer_openai.similarity_threshold == 0.82 + + def test_add_decision(self, openai_encoder): + decision_layer = DecisionLayer(encoder=openai_encoder) + decision = Decision(name="Decision 3", utterances=["Yes", "No"]) + decision_layer.add(decision) + assert len(decision_layer.index) == 2 + assert len(set(decision_layer.categories)) == 1 + + def test_add_multiple_decisions(self, openai_encoder, decisions): + decision_layer = DecisionLayer(encoder=openai_encoder) + for decision in decisions: + decision_layer.add(decision) + assert len(decision_layer.index) == 5 + assert len(set(decision_layer.categories)) == 2 + + def test_query_and_classification(self, openai_encoder, decisions): + decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions) + query_result = decision_layer("Hello") + assert query_result in ["Decision 1", "Decision 2"] + + def test_query_with_no_index(self, openai_encoder): + decision_layer = DecisionLayer(encoder=openai_encoder) + assert decision_layer("Anything") is None + + def test_semantic_classify(self, openai_encoder, decisions): + decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions) + classification, score = decision_layer._semantic_classify( + [ + {"decision": "Decision 1", "score": 0.9}, + {"decision": "Decision 2", "score": 0.1}, + ] + ) + assert classification == "Decision 1" + assert score == [0.9] + + def test_semantic_classify_multiple_decisions(self, openai_encoder, decisions): + decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions) + classification, score = decision_layer._semantic_classify( + [ + {"decision": "Decision 1", "score": 0.9}, + {"decision": "Decision 2", "score": 0.1}, + {"decision": "Decision 1", "score": 0.8}, + ] + ) + assert classification == "Decision 1" + assert score == [0.9, 0.8] + + def test_pass_threshold(self, openai_encoder): + decision_layer = DecisionLayer(encoder=openai_encoder) + assert not decision_layer._pass_threshold([], 0.5) + assert decision_layer._pass_threshold([0.6, 0.7], 0.5) + + def test_failover_similarity_threshold(self, base_encoder): + decision_layer = DecisionLayer(encoder=base_encoder) + assert decision_layer.similarity_threshold == 0.82 + + +# Add more tests for edge cases and error handling as needed. diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..2563bf0bc21b7769f96d1119fdc29b6afab66d1a --- /dev/null +++ b/tests/unit/test_schema.py @@ -0,0 +1,61 @@ +import pytest + +from semantic_router.schema import ( + CohereEncoder, + Decision, + Encoder, + EncoderType, + OpenAIEncoder, + SemanticSpace, +) + + +class TestEncoderDataclass: + def test_encoder_initialization_openai(self, mocker): + mocker.patch.dict("os.environ", {"OPENAI_API_KEY": "test"}) + encoder = Encoder(type="openai", name="test-engine") + assert encoder.type == EncoderType.OPENAI + assert isinstance(encoder.model, OpenAIEncoder) + + def test_encoder_initialization_cohere(self, mocker): + mocker.patch.dict("os.environ", {"COHERE_API_KEY": "test"}) + encoder = Encoder(type="cohere", name="test-engine") + assert encoder.type == EncoderType.COHERE + assert isinstance(encoder.model, CohereEncoder) + + def test_encoder_initialization_unsupported_type(self): + with pytest.raises(ValueError): + Encoder(type="unsupported", name="test-engine") + + def test_encoder_initialization_huggingface(self): + with pytest.raises(NotImplementedError): + Encoder(type="huggingface", name="test-engine") + + def test_encoder_call_method(self, mocker): + mocker.patch.dict("os.environ", {"OPENAI_API_KEY": "test"}) + mocker.patch( + "semantic_router.encoders.openai.OpenAIEncoder.__call__", + return_value=[0.1, 0.2, 0.3], + ) + encoder = Encoder(type="openai", name="test-engine") + result = encoder(["test"]) + assert result == [0.1, 0.2, 0.3] + + +class TestSemanticSpaceDataclass: + def test_semanticspace_initialization(self): + semantic_space = SemanticSpace() + assert semantic_space.id == "" + assert semantic_space.decisions == [] + + def test_semanticspace_add_decision(self): + decision = Decision( + name="test", utterances=["hello", "hi"], description="greeting" + ) + semantic_space = SemanticSpace() + semantic_space.add(decision) + + assert len(semantic_space.decisions) == 1 + assert semantic_space.decisions[0].name == "test" + assert semantic_space.decisions[0].utterances == ["hello", "hi"] + assert semantic_space.decisions[0].description == "greeting"