diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 94bb0dd..d9f03fa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -24,6 +24,6 @@ repos: types: ["python"] - id: pip-audit name: pip-audit - entry: uv run pip-audit --ignore-vuln GHSA-4xh5-x5gv-qwph + entry: uv run pip-audit language: system pass_filenames: false \ No newline at end of file diff --git a/Makefile b/Makefile index 8c11fc0..b999f0a 100644 --- a/Makefile +++ b/Makefile @@ -57,7 +57,7 @@ ruff: # Run 'ruff' linter and print a preview of errors uv run ruff check . safety: # Check for security vulnerabilities - uv run pip-audit --ignore-vuln GHSA-4xh5-x5gv-qwph + uv run pip-audit lint-apply: black-apply ruff-apply # Apply changes with 'black' and resolve 'fixable errors' with 'ruff' diff --git a/embeddings/cli.py b/embeddings/cli.py index 11f203b..e761686 100644 --- a/embeddings/cli.py +++ b/embeddings/cli.py @@ -210,6 +210,7 @@ def create_embeddings( ) -> None: """Create embeddings for TIMDEX records.""" model: BaseEmbeddingModel = ctx.obj["model"] + model.load() # init TIMDEXDataset timdex_dataset = TIMDEXDataset(dataset_location) @@ -229,10 +230,10 @@ def create_embeddings( ) # create an iterator of EmbeddingInputs applying all requested strategies - input_records = create_embedding_inputs(timdex_records, list(strategy)) + embedding_inputs = create_embedding_inputs(timdex_records, list(strategy)) # create embeddings via the embedding model - embeddings = model.create_embeddings(input_records) + embeddings = model.create_embeddings(embedding_inputs) # if requested, write embeddings to a local JSONLines file if output_jsonl: diff --git a/embeddings/embedding.py b/embeddings/embedding.py index d0c8187..302c895 100644 --- a/embeddings/embedding.py +++ b/embeddings/embedding.py @@ -23,6 +23,12 @@ class EmbeddingInput: embedding_strategy: str text: str + def __repr__(self) -> str: # noqa: D105 + return ( + f"" + ) + @dataclass class Embedding: @@ -49,6 +55,12 @@ class Embedding: default_factory=lambda: datetime.datetime.now(datetime.UTC) ) + def __repr__(self) -> str: # noqa: D105 + return ( + f"" + ) + def to_dict(self) -> dict: """Marshal to dictionary.""" return asdict(self) diff --git a/embeddings/models/base.py b/embeddings/models/base.py index 79ae683..dc824ee 100644 --- a/embeddings/models/base.py +++ b/embeddings/models/base.py @@ -51,20 +51,20 @@ def load(self) -> None: """Load model from self.model_path.""" @abstractmethod - def create_embedding(self, input_record: EmbeddingInput) -> Embedding: + def create_embedding(self, embedding_input: EmbeddingInput) -> Embedding: """Create an Embedding for an EmbeddingInput. Args: - input_record: EmbeddingInput instance + embedding_input: EmbeddingInput instance """ def create_embeddings( - self, input_records: Iterator[EmbeddingInput] + self, embedding_inputs: Iterator[EmbeddingInput] ) -> Iterator[Embedding]: - """Yield Embeddings for an iterator of InputRecords. + """Yield Embeddings for a batch of EmbeddingInputs. Args: - input_records: iterator of InputRecords + embedding_inputs: iterator of EmbeddingInputs """ - for input_text in input_records: - yield self.create_embedding(input_text) + for embedding_input in embedding_inputs: + yield self.create_embedding(embedding_input) diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py index 8b7eee0..d1249c6 100644 --- a/embeddings/models/os_neural_sparse_doc_v3_gte.py +++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py @@ -8,6 +8,7 @@ from pathlib import Path from typing import TYPE_CHECKING +import torch from huggingface_hub import snapshot_download from transformers import AutoModelForMaskedLM, AutoTokenizer @@ -26,6 +27,9 @@ class OSNeuralSparseDocV3GTE(BaseEmbeddingModel): """OpenSearch Neural Sparse Encoding Doc v3 GTE model. + This model generates sparse embeddings for documents by using a masked language + model's logits to identify the most relevant tokens. + HuggingFace URI: opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte """ @@ -40,8 +44,8 @@ def __init__(self, model_path: str | Path) -> None: super().__init__(model_path) self._model: PreTrainedModel | None = None self._tokenizer: DistilBertTokenizerFast | None = None - self._special_token_ids: list | None = None - self._id_to_token: list | None = None + self._special_token_ids: list[int] | None = None + self._device: torch.device = torch.device("cpu") def download(self) -> Path: """Download and prepare model, saving to self.model_path. @@ -139,29 +143,205 @@ def load(self) -> None: if not self.model_path.exists(): raise FileNotFoundError(f"Model not found at path: {self.model_path}") - # load local model and tokenizer - self._model = AutoModelForMaskedLM.from_pretrained( + # setup device (use CUDA if available, otherwise CPU) + self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # load tokenizer + self._tokenizer = AutoTokenizer.from_pretrained( # type: ignore[no-untyped-call] self.model_path, - trust_remote_code=True, local_files_only=True, ) - self._tokenizer = AutoTokenizer.from_pretrained( # type: ignore[no-untyped-call] + + # load model as AutoModelForMaskedLM (required for sparse embeddings) + self._model = AutoModelForMaskedLM.from_pretrained( self.model_path, + trust_remote_code=True, local_files_only=True, ) + self._model.to(self._device) # type: ignore[arg-type] + self._model.eval() - # setup special tokens + # set special token IDs (following model card pattern) + # these will be zeroed out in the sparse vectors self._special_token_ids = [ - self._tokenizer.vocab[str(token)] + self._tokenizer.vocab[token] # type: ignore[index] for token in self._tokenizer.special_tokens_map.values() ] - # setup id_to_token mapping - self._id_to_token = ["" for _ in range(self._tokenizer.vocab_size)] - for token, token_id in self._tokenizer.vocab.items(): - self._id_to_token[token_id] = token + logger.info( + f"Model loaded successfully on {self._device}, " + f"{time.perf_counter() - start_time:.2f}s" + ) + + def create_embedding(self, embedding_input: EmbeddingInput) -> Embedding: + """Create sparse vector and decoded token weight embeddings for an input text. + + Args: + embedding_input: EmbeddingInput object with a .text attribute + """ + # generate the sparse embeddings + sparse_vector, decoded_tokens = self._encode_documents([embedding_input.text])[0] + + # coerce sparse vector tensor into list[float] + sparse_vector_list = sparse_vector.cpu().numpy().tolist() + + return Embedding( + timdex_record_id=embedding_input.timdex_record_id, + run_id=embedding_input.run_id, + run_record_offset=embedding_input.run_record_offset, + model_uri=self.model_uri, + embedding_strategy=embedding_input.embedding_strategy, + embedding_vector=sparse_vector_list, + embedding_token_weights=decoded_tokens, + ) + + def _encode_documents( + self, + texts: list[str], + ) -> list[tuple[torch.Tensor, dict[str, float]]]: + """Encode documents into sparse vectors and decoded token weights. + + This follows the pattern outlined on the HuggingFace model card for document + encoding. + + This method will accommodate MULTIPLE text inputs, and return a list of + embeddings, but the calling context of create_embedding() is a SINGULAR input + + output. This method keeps the ability to handle multiple inputs + outputs, in the + event we want something like a create_multiple_embeddings() method in the future, + but only returns a single result. + + At a very high level, the following is performed: + + 1. We tokenize the input text into "features" using the model's tokenizer. + + 2. The features are fed to the model returning model output logits. These logits + are "dense" in the sense there are few zeros, but they are not "dense vectors" + (embeddings) in the sense that they meaningfully represent the input document in + geometric space; two logit tensors cannot be compared with something like cosine + similarity. + + 3. The logits are then converted into a sparse vector, which is a numeric + array of floats with the same number of values as the model's vocabulary. Each + value's position in the sparse array corresponds to the token id in the + vocabulary, and the value itself is the "weight" of this token in the input text. + + 4. Lastly, we convert this sparse vector into a {token:weight} dictionary of the + actual token strings and their numerical weight. This dictionary may contain + tokens not present in the original text, but will be considerably shorter than + the model vocabulary length given all zero and low scoring tokens are dropped. + This is the final form that we will ultimately index into OpenSearch. + + Args: + texts: list of strings to create embeddings for + """ + if self._model is None or self._tokenizer is None: + raise RuntimeError("Model not loaded. Call load() before create_embedding.") + + # tokenize the input texts + features = self._tokenizer( + texts, + padding=True, + truncation=True, + return_tensors="pt", # returns PyTorch tensors instead of Python lists + return_token_type_ids=False, + ) + + # move to CPU or GPU device, depending on what's available + features = {k: v.to(self._device) for k, v in features.items()} + + # pass features to the model and receive model output logits as a tensor + with torch.no_grad(): + output = self._model(**features)[0] + + # generate sparse vectors from model logits tensor + sparse_vectors = self._get_sparse_vectors(features, output) + + # decode sparse vectors to token-weight dictionaries + decoded = self._decode_sparse_vectors(sparse_vectors) + + # return list of tuple(vector, decoded token weights) embedding results + return [(sparse_vectors[i], decoded[i]) for i in range(len(texts))] + + def _get_sparse_vectors( + self, features: dict[str, torch.Tensor], output: torch.Tensor + ) -> torch.Tensor: + """Convert model logits output to sparse vectors. + + This follows the HuggingFace model card exactly: https://huggingface.co/ + opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte#usage-huggingface + + This implements the get_sparse_vector function from the model card: + 1. Max pooling with attention mask + 2. log(1 + log(1 + relu())) transformation + 3. Zero out special tokens + + The end result is a sparse vector with a length of the model vocabulary, with each + position representing a token in the model vocabulary and each value representing + that token's weight relative to the input text. + + Args: + features: Tokenizer output with attention_mask + output: Model logits of shape (batch_size, seq_len, vocab_size) + + Returns: + Sparse vectors of shape (batch_size, vocab_size) + """ + # collapse sequence positions: take max logit for each vocab token across all + # positions (also masks out padding tokens) + values, _ = torch.max(output * features["attention_mask"].unsqueeze(-1), dim=1) + + # compress values to create sparsity: ReLU removes negatives, + # double-log shrinks large values + values = torch.log(1 + torch.log(1 + torch.relu(values))) + + # remove special tokens like [CLS], [SEP], [PAD] + values[:, self._special_token_ids] = 0 + + return values + + def _decode_sparse_vectors( + self, sparse_vectors: torch.Tensor + ) -> list[dict[str, float]]: + """Convert sparse vectors to token-weight dictionaries. + + Handles both single vectors and batches, returning a list of dictionaries mapping + token strings to their weights. + + Args: + sparse_vectors: Tensor of shape (batch_size, vocab_size) or (vocab_size,) + + Returns: + List of dictionaries with token-weight pairs + """ + if sparse_vectors.dim() == 1: + sparse_vectors = sparse_vectors.unsqueeze(0) + + # move to CPU for processing + sparse_vectors_cpu = sparse_vectors.cpu() + + results: list[dict] = [] + for vector in sparse_vectors_cpu: + + # find non-zero indices and values + nonzero_indices = torch.nonzero(vector, as_tuple=False).squeeze(-1) + + if nonzero_indices.numel() == 0: + results.append({}) + continue + + # get weights + weights = vector[nonzero_indices].tolist() + + # convert indices to token strings + token_ids = nonzero_indices.tolist() + tokens = self._tokenizer.convert_ids_to_tokens(token_ids) # type: ignore[union-attr] - logger.info(f"Model loaded successfully, {time.perf_counter()-start_time}s") + # create token:weight dictionary + token_dict = { + token: weight + for token, weight in zip(tokens, weights, strict=True) + if token is not None + } + results.append(token_dict) - def create_embedding(self, input_record: EmbeddingInput) -> Embedding: - raise NotImplementedError + return results diff --git a/pyproject.toml b/pyproject.toml index cec37ea..c311b0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,6 +70,7 @@ ignore = [ "D102", "D103", "D104", + "EM101", "EM102", "G004", "PLR0912", diff --git a/tests/conftest.py b/tests/conftest.py index 6f0d7df..fd449b4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -45,12 +45,12 @@ def download(self) -> Path: def load(self) -> None: logger.info("Model loaded successfully, 1.5s") - def create_embedding(self, input_record: EmbeddingInput) -> Embedding: + def create_embedding(self, embedding_input: EmbeddingInput) -> Embedding: return Embedding( - timdex_record_id=input_record.timdex_record_id, - run_id=input_record.run_id, - run_record_offset=input_record.run_record_offset, - embedding_strategy=input_record.embedding_strategy, + timdex_record_id=embedding_input.timdex_record_id, + run_id=embedding_input.run_id, + run_record_offset=embedding_input.run_record_offset, + embedding_strategy=embedding_input.embedding_strategy, model_uri=self.model_uri, embedding_vector=[0.1, 0.2, 0.3], embedding_token_weights={"coffee": 0.9, "seattle": 0.5}, @@ -71,35 +71,7 @@ def register_mock_model(monkeypatch): @pytest.fixture -def neural_sparse_doc_v3_gte_fake_model_directory(tmp_path): - """Create a fake downloaded model directory with required files.""" - model_dir = tmp_path / "fake_model" - model_dir.mkdir() - - # create config.json - config_json = { - "model_type": "distilbert", - "vocab_size": 30000, - "auto_map": { - "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", - "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", - }, - } - (model_dir / "config.json").write_text(json.dumps(config_json)) - - # create modeling.py and configuration.py - (model_dir / "modeling.py").write_text("# mock modeling code") - (model_dir / "configuration.py").write_text("# mock configuration code") - - # create tokenizer files - (model_dir / "tokenizer.json").write_text('{"version": "1.0"}') - (model_dir / "vocab.txt").write_text("word1\nword2\n") - - return model_dir - - -@pytest.fixture -def neural_sparse_doc_v3_gte_mock_huggingface_snapshot(monkeypatch, tmp_path): +def mock_snapshot_download(monkeypatch, tmp_path): """Mock snapshot_download to create fake model files locally.""" def mock_snapshot(repo_id, local_dir, **kwargs): @@ -132,53 +104,3 @@ def mock_snapshot(repo_id, local_dir, **kwargs): "embeddings.models.os_neural_sparse_doc_v3_gte.snapshot_download", mock_snapshot ) return mock_snapshot - - -@pytest.fixture -def neural_sparse_doc_v3_gte_mock_transformers_models(monkeypatch): - """Mock AutoModelForMaskedLM and AutoTokenizer.""" - - class MockTokenizer: - """Mock tokenizer with necessary attributes.""" - - def __init__(self, *args, **kwargs): # noqa: ARG002 - self.vocab = { - "[CLS]": 0, - "[SEP]": 1, - "[PAD]": 2, - "word1": 3, - "word2": 4, - } - self.vocab_size = len(self.vocab) - self.special_tokens_map = { - "cls_token": "[CLS]", - "sep_token": "[SEP]", - "pad_token": "[PAD]", - } - - class MockModel: - """Mock model with necessary attributes.""" - - def __init__(self, *args, **kwargs): # noqa: ARG002 - self.config = {"vocab_size": 30000} - - class MockAutoTokenizer: - @staticmethod - def from_pretrained(*args, **kwargs): # noqa: ARG004 - return MockTokenizer() - - class MockAutoModelForMaskedLM: - @staticmethod - def from_pretrained(*args, **kwargs): # noqa: ARG004 - return MockModel() - - monkeypatch.setattr( - "embeddings.models.os_neural_sparse_doc_v3_gte.AutoTokenizer", - MockAutoTokenizer, - ) - monkeypatch.setattr( - "embeddings.models.os_neural_sparse_doc_v3_gte.AutoModelForMaskedLM", - MockAutoModelForMaskedLM, - ) - - return {"tokenizer": MockTokenizer, "model": MockModel} diff --git a/tests/test_models.py b/tests/test_models.py index d8f1734..217f67c 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -35,14 +35,14 @@ def test_mock_model_load(caplog, mock_model): def test_mock_model_create_embedding(mock_model): - input_record = EmbeddingInput( + embedding_input = EmbeddingInput( timdex_record_id="test-id", run_id="test-run", run_record_offset=42, embedding_strategy="full_record", text="test text", ) - embedding = mock_model.create_embedding(input_record) + embedding = mock_model.create_embedding(embedding_input) assert embedding.timdex_record_id == "test-id" assert embedding.run_id == "test-run" @@ -87,7 +87,7 @@ class InvalidModel(BaseEmbeddingModel): def test_base_model_create_embeddings_calls_create_embedding(mock_model): - input_records = [ + embedding_inputs = [ EmbeddingInput( timdex_record_id="id-1", run_id="run-1", @@ -105,7 +105,7 @@ def test_base_model_create_embeddings_calls_create_embedding(mock_model): ] # create_embeddings should iterate and call create_embedding - embeddings = list(mock_model.create_embeddings(iter(input_records))) + embeddings = list(mock_model.create_embeddings(iter(embedding_inputs))) assert len(embeddings) == 2 # two input records assert embeddings[0].timdex_record_id == "id-1" diff --git a/tests/test_os_neural_sparse_doc_v3_gte.py b/tests/test_os_neural_sparse_doc_v3_gte.py index 462ae85..0e39bd5 100644 --- a/tests/test_os_neural_sparse_doc_v3_gte.py +++ b/tests/test_os_neural_sparse_doc_v3_gte.py @@ -6,17 +6,118 @@ from pathlib import Path import pytest +import torch +from embeddings.embedding import EmbeddingInput from embeddings.models.os_neural_sparse_doc_v3_gte import OSNeuralSparseDocV3GTE +@pytest.fixture +def os_neural_sparse_model_path(tmp_path): + """Create a fake model directory for OSNeuralSparseDocV3GTE.""" + model_dir = tmp_path / "fake_model" + model_dir.mkdir() + + # create config.json + config_json = { + "model_type": "distilbert", + "vocab_size": 30000, + "auto_map": { + "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", + "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", + }, + } + (model_dir / "config.json").write_text(json.dumps(config_json)) + + # create modeling.py and configuration.py + (model_dir / "modeling.py").write_text("# mock modeling code") + (model_dir / "configuration.py").write_text("# mock configuration code") + + # create tokenizer files + (model_dir / "tokenizer.json").write_text('{"version": "1.0"}') + (model_dir / "vocab.txt").write_text("word1\nword2\n") + + return model_dir + + +@pytest.fixture +def mock_os_neural_sparse_transformers(monkeypatch): + """Mock AutoTokenizer and AutoModelForMaskedLM for OSNeuralSparseDocV3GTE.""" + + class MockTokenizer: + """Mock tokenizer with necessary attributes.""" + + def __init__(self, *args, **kwargs): # noqa: ARG002 + self.vocab = { + "[CLS]": 0, + "[SEP]": 1, + "[PAD]": 2, + "coffee": 3, + "mountain": 4, + "seattle": 5, + } + self.vocab_size = len(self.vocab) + self.special_tokens_map = { + "cls_token": "[CLS]", + "sep_token": "[SEP]", + "pad_token": "[PAD]", + } + self.id_to_token = {v: k for k, v in self.vocab.items()} + + def convert_ids_to_tokens(self, token_ids): + """Convert token IDs to token strings.""" + if isinstance(token_ids, int): + return self.id_to_token.get(token_ids) + return [self.id_to_token.get(tid) for tid in token_ids] + + class MockModel: + """Mock model with necessary attributes.""" + + def __init__(self, *args, **kwargs): # noqa: ARG002 + self.config = {"vocab_size": 30000} + + def to(self, device): # noqa: ARG002 + """Mock device placement.""" + return self + + def eval(self): + """Mock evaluation mode.""" + return self + + class MockAutoTokenizer: + @staticmethod + def from_pretrained(*args, **kwargs): # noqa: ARG004 + return MockTokenizer() + + class MockAutoModelForMaskedLM: + @staticmethod + def from_pretrained(*args, **kwargs): # noqa: ARG004 + return MockModel() + + monkeypatch.setattr( + "embeddings.models.os_neural_sparse_doc_v3_gte.AutoTokenizer", + MockAutoTokenizer, + ) + monkeypatch.setattr( + "embeddings.models.os_neural_sparse_doc_v3_gte.AutoModelForMaskedLM", + MockAutoModelForMaskedLM, + ) + + +@pytest.fixture +def loaded_model(os_neural_sparse_model_path, mock_os_neural_sparse_transformers): + """Fixture providing a loaded OSNeuralSparseDocV3GTE instance.""" + model = OSNeuralSparseDocV3GTE(os_neural_sparse_model_path) + model.load() + return model + + def test_init(tmp_path): """Test model initialization.""" model = OSNeuralSparseDocV3GTE(tmp_path / "model") assert model._model is None assert model._tokenizer is None assert model._special_token_ids is None - assert model._id_to_token is None def test_model_uri(tmp_path): @@ -32,9 +133,7 @@ def test_model_uri(tmp_path): ) -def test_download_to_directory( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path -): +def test_download_to_directory(mock_snapshot_download, tmp_path): """Test download to directory (not zip).""" output_path = tmp_path / "model_output" model = OSNeuralSparseDocV3GTE(output_path) @@ -48,9 +147,7 @@ def test_download_to_directory( assert (output_path / "tokenizer.json").exists() -def test_download_to_zip_file( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path -): +def test_download_to_zip_file(mock_snapshot_download, tmp_path): """Test download creates zip when path ends in .zip.""" output_path = tmp_path / "model.zip" model = OSNeuralSparseDocV3GTE(output_path) @@ -62,9 +159,7 @@ def test_download_to_zip_file( assert output_path.suffix == ".zip" -def test_download_calls_patch_method( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path, monkeypatch -): +def test_download_calls_patch_method(mock_snapshot_download, tmp_path, monkeypatch): """Test that download calls the Alibaba patching method.""" output_path = tmp_path / "model_output" model = OSNeuralSparseDocV3GTE(output_path) @@ -82,9 +177,7 @@ def mock_patch(temp_path): assert patch_called -def test_download_returns_path( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path -): +def test_download_returns_path(mock_snapshot_download, tmp_path): """Test download returns the output path.""" output_path = tmp_path / "model_output" model = OSNeuralSparseDocV3GTE(output_path) @@ -95,9 +188,7 @@ def test_download_returns_path( assert isinstance(result, Path) -def test_patch_downloads_alibaba_model( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path -): +def test_patch_downloads_alibaba_model(mock_snapshot_download, tmp_path): """Test patch method downloads Alibaba-NLP/new-impl.""" model = OSNeuralSparseDocV3GTE(tmp_path / "model") model_temp_path = tmp_path / "temp_model" @@ -110,7 +201,7 @@ def test_patch_downloads_alibaba_model( assert (model_temp_path / "configuration.py").exists() -def test_patch_copies_files(neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path): +def test_patch_copies_files(mock_snapshot_download, tmp_path): """Test patch copies modeling.py and configuration.py.""" model = OSNeuralSparseDocV3GTE(tmp_path / "model") model_temp_path = tmp_path / "temp_model" @@ -126,9 +217,7 @@ def test_patch_copies_files(neural_sparse_doc_v3_gte_mock_huggingface_snapshot, assert "Alibaba configuration code" in config_content -def test_patch_updates_config_json( - neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path -): +def test_patch_updates_config_json(mock_snapshot_download, tmp_path): """Test patch updates auto_map in config.json.""" model = OSNeuralSparseDocV3GTE(tmp_path / "model") model_temp_path = tmp_path / "temp_model" @@ -147,11 +236,11 @@ def test_patch_updates_config_json( def test_load_success( - neural_sparse_doc_v3_gte_fake_model_directory, - neural_sparse_doc_v3_gte_mock_transformers_models, + os_neural_sparse_model_path, + mock_os_neural_sparse_transformers, ): """Test successful load from local path.""" - model = OSNeuralSparseDocV3GTE(neural_sparse_doc_v3_gte_fake_model_directory) + model = OSNeuralSparseDocV3GTE(os_neural_sparse_model_path) model.load() @@ -169,11 +258,11 @@ def test_load_file_not_found(): def test_load_initializes_model_and_tokenizer( - neural_sparse_doc_v3_gte_fake_model_directory, - neural_sparse_doc_v3_gte_mock_transformers_models, + os_neural_sparse_model_path, + mock_os_neural_sparse_transformers, ): """Test load initializes _model and _tokenizer attributes.""" - model = OSNeuralSparseDocV3GTE(neural_sparse_doc_v3_gte_fake_model_directory) + model = OSNeuralSparseDocV3GTE(os_neural_sparse_model_path) assert model._model is None assert model._tokenizer is None @@ -185,11 +274,11 @@ def test_load_initializes_model_and_tokenizer( def test_load_sets_up_special_token_ids( - neural_sparse_doc_v3_gte_fake_model_directory, - neural_sparse_doc_v3_gte_mock_transformers_models, + os_neural_sparse_model_path, + mock_os_neural_sparse_transformers, ): """Test load sets up _special_token_ids list.""" - model = OSNeuralSparseDocV3GTE(neural_sparse_doc_v3_gte_fake_model_directory) + model = OSNeuralSparseDocV3GTE(os_neural_sparse_model_path) model.load() @@ -201,20 +290,69 @@ def test_load_sets_up_special_token_ids( assert 2 in model._special_token_ids # [PAD] token id -def test_load_sets_up_id_to_token_mapping( - neural_sparse_doc_v3_gte_fake_model_directory, - neural_sparse_doc_v3_gte_mock_transformers_models, -): - """Test load creates _id_to_token mapping correctly.""" - model = OSNeuralSparseDocV3GTE(neural_sparse_doc_v3_gte_fake_model_directory) +def test_create_embedding_raises_error_if_model_not_loaded(tmp_path): + """Test create_embedding raises RuntimeError if model not loaded.""" + model = OSNeuralSparseDocV3GTE(tmp_path / "model") + embedding_input = EmbeddingInput( + timdex_record_id="test:123", + run_id="run-456", + run_record_offset=0, + embedding_strategy="title_only", + text="test document", + ) - model.load() + with pytest.raises(RuntimeError, match="Model not loaded"): + model.create_embedding(embedding_input) + + +def test_create_embedding_returns_embedding_object(tmp_path, monkeypatch): + """Test create_embedding returns an Embedding object with correct attributes.""" + model = OSNeuralSparseDocV3GTE(tmp_path / "model") + + # mock _encode_documents + def mock_encode_documents(texts): + sparse_vector = torch.tensor([0.0, 0.0, 0.0, 0.91, 0.73]) + decoded_tokens = {"coffee": 0.91, "mountain": 0.73} + return [(sparse_vector, decoded_tokens)] + + monkeypatch.setattr(model, "_encode_documents", mock_encode_documents) + + embedding_input = EmbeddingInput( + timdex_record_id="test:123", + run_id="run-456", + run_record_offset=42, + embedding_strategy="title_only", + text="test document", + ) + + embedding = model.create_embedding(embedding_input) + + assert embedding.timdex_record_id == "test:123" + assert embedding.run_id == "run-456" + assert embedding.run_record_offset == 42 + assert embedding.model_uri == model.model_uri + assert embedding.embedding_strategy == "title_only" + assert isinstance(embedding.embedding_vector, list) + assert embedding.embedding_vector == pytest.approx([0.0, 0.0, 0.0, 0.91, 0.73]) + assert embedding.embedding_token_weights == {"coffee": 0.91, "mountain": 0.73} + + +def test_decode_sparse_vectors_converts_to_token_weights(loaded_model): + """Test _decode_sparse_vectors converts sparse vector to token-weight dict.""" + # sparse vector with weights for "coffee" (index 3) and "mountain" (index 4) + sparse_vector = torch.tensor([0.0, 0.0, 0.0, 0.85, 0.62, 0.0]) + + result = loaded_model._decode_sparse_vectors(sparse_vector) + + assert len(result) == 1 + assert result[0] == {"coffee": pytest.approx(0.85), "mountain": pytest.approx(0.62)} + + +def test_decode_sparse_vectors_with_empty_vector(loaded_model): + """Test _decode_sparse_vectors returns empty dict for all-zero vector.""" + sparse_vector = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + + result = loaded_model._decode_sparse_vectors(sparse_vector) - assert model._id_to_token is not None - assert isinstance(model._id_to_token, list) - assert len(model._id_to_token) == 5 # vocab_size from mock - assert model._id_to_token[0] == "[CLS]" - assert model._id_to_token[1] == "[SEP]" - assert model._id_to_token[2] == "[PAD]" - assert model._id_to_token[3] == "word1" - assert model._id_to_token[4] == "word2" + assert len(result) == 1 + assert result[0] == {} diff --git a/uv.lock b/uv.lock index 1dfd0db..23f47f5 100644 --- a/uv.lock +++ b/uv.lock @@ -56,30 +56,30 @@ wheels = [ [[package]] name = "boto3" -version = "1.40.56" +version = "1.40.65" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "botocore" }, { name = "jmespath" }, { name = "s3transfer" }, ] -sdist = { url = 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