From c563d2aa92ae01c8193247148a652e091ca39850 Mon Sep 17 00:00:00 2001
From: Graham Hukill <ghukill@gmail.com>
Date: Tue, 4 Nov 2025 09:34:25 -0500
Subject: [PATCH 1/4] Update dependencies

---
 .pre-commit-config.yaml |   2 +-
 Makefile                |   2 +-
 pyproject.toml          |   1 +
 uv.lock                 | 340 ++++++++++++++++++++++------------------
 4 files changed, 190 insertions(+), 155 deletions(-)

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/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/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 = "https://files.pythonhosted.org/packages/9c/8d/70929dde76e24f252d6cf1fb3224ff5694ca96451d9e7023a43555fab760/boto3-1.40.56.tar.gz", hash = "sha256:c1afdb04dd27418fc58400434ab8e05998bb452b69c428168d9ada344fe6b93e", size = 111489, upload-time = "2025-10-21T20:31:01.013Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b1/04/619bf4f1191021aa54514a3da4fbe91f7a04bf827fad0c1d562f8b3db474/boto3-1.40.65.tar.gz", hash = "sha256:52e2715838d65e6b000e0077a942ce2d3e1a38f9764414ad01a602912eccf924", size = 111507, upload-time = "2025-11-03T21:56:57.12Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d5/a2/973a1346bb8d23ddda241342e126a26407e08648a556fb5022e0db457860/boto3-1.40.65-py3-none-any.whl", hash = "sha256:ab91d8d8ef0477997d35abebf67829e52e50bf807b02333affa384c70b33c86b", size = 139323, upload-time = "2025-11-03T21:56:55.125Z" },
 ]
 
 [[package]]
 name = "botocore"
-version = "1.40.56"
+version = "1.40.65"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jmespath" },
     { name = "python-dateutil" },
     { name = "urllib3" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/cf/a6/8e3209425650c96916b31324594db3ea9ec2eecc2b0acf6bbda0d2a6b1b2/botocore-1.40.65.tar.gz", hash = "sha256:cdbbf9d90a9e9c4a6000055013d98b92efc4ceb1bce0d9bcd70e14461dc22ab3", size = 14411821, upload-time = "2025-11-03T21:56:45.391Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/a6/00/ccd1612ee99865435ad04ef477168af9d3a8d2ec6a7a694a37af47143543/botocore-1.40.65-py3-none-any.whl", hash = "sha256:152f595321f5a2b712601286650e912c2e5ca3b109892ab4c0175ac58d8de10d", size = 14075618, upload-time = "2025-11-03T21:56:42.011Z" },
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 [[package]]
@@ -392,11 +392,11 @@ wheels = [
 
 [[package]]
 name = "fsspec"
-version = "2025.9.0"
+version = "2025.10.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/24/7f/2747c0d332b9acfa75dc84447a066fdf812b5a6b8d30472b74d309bfe8cb/fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59", size = 309285, upload-time = "2025-10-30T14:58:44.036Z" }
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+    { url = "https://files.pythonhosted.org/packages/eb/02/a6b21098b1d5d6249b7c5ab69dde30108a71e4e819d4a9778f1de1d5b70d/fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d", size = 200966, upload-time = "2025-10-30T14:58:42.53Z" },
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From fc0bdea2ecf765c67b5c8d05202c3bc5c3b153c3 Mon Sep 17 00:00:00 2001
From: Graham Hukill <ghukill@gmail.com>
Date: Tue, 4 Nov 2025 13:48:07 -0500
Subject: [PATCH 2/4] OSNeuralSparseDocV3GTE embedding business logic

Why these changes are being introduced:

Our first embedding class OSNeuralSparseDocV3GTE was ready for a real
create_embedding() method with the rest of the moving parts mostly complete at this time.

How this addresses that need:

The HuggingFace model card, https://huggingface.co/
opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte, contains a section of
example code for using this model with the transformers library. This logic was ported
over to our class, with the model already downloaded and loaded taken care of.

The biggest addition is the method _decode_sparse_vectors() which converts the numerical
sparse vector into a dictionary of token:weights.  This decoded token weight form is what
we'll pass directly to OpenSearch.

With the new functionality in place, the tests associated with this class were also
updated.  Fixtures were moved into the testing file, a pattern we could adopt for any
future models to keep them out of the shared conftest.py.

Side effects of this change:
* CLI is capable of producing embeddings for our first model OSNeuralSparseDocV3GTE

Relevant ticket(s):
* https://mitlibraries.atlassian.net/browse/USE-136
---
 embeddings/cli.py                             |   1 +
 embeddings/embedding.py                       |  12 +
 .../models/os_neural_sparse_doc_v3_gte.py     | 233 ++++++++++++++++--
 tests/conftest.py                             |  80 +-----
 tests/test_os_neural_sparse_doc_v3_gte.py     | 226 +++++++++++++----
 5 files changed, 414 insertions(+), 138 deletions(-)

diff --git a/embeddings/cli.py b/embeddings/cli.py
index 11f203b..e6be057 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)
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"<EmbeddingInput - record:'{self.timdex_record_id}', "
+            f"strategy:'{self.embedding_strategy}', text length:{len(self.text)}>"
+        )
+
 
 @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"<Embedding - record:'{self.timdex_record_id}', "
+            f"strategy:'{self.embedding_strategy}'>"
+        )
+
     def to_dict(self) -> dict:
         """Marshal to dictionary."""
         return asdict(self)
diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py
index 8b7eee0..cfe3dba 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 +143,228 @@ 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, {time.perf_counter()-start_time}s")
+        logger.info(
+            f"Model loaded successfully on {self._device}, "
+            f"{time.perf_counter() - start_time:.2f}s"
+        )
 
     def create_embedding(self, input_record: EmbeddingInput) -> Embedding:
-        raise NotImplementedError
+        """Create sparse embeddings for the input text (document encoding).
+
+        This method generates sparse document embeddings.
+
+        Process follows the model card exactly:
+        1. Tokenize the document
+        2. Pass through the masked language model to get logits
+        3. Convert logits to sparse vector
+        6. Return both raw sparse vector and decoded token-weight pairs
+
+        Args:
+            input_record: The input containing text to embed
+        """
+        # generate the sparse embeddings
+        sparse_vector, decoded_tokens = self._encode_documents([input_record.text])[0]
+
+        # coerce sparse vector tensor into list[float]
+        sparse_vector_list = sparse_vector.cpu().numpy().tolist()
+
+        return Embedding(
+            timdex_record_id=input_record.timdex_record_id,
+            run_id=input_record.run_id,
+            run_record_offset=input_record.run_record_offset,
+            model_uri=self.model_uri,
+            embedding_strategy=input_record.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 a list of text inputs, and return a list of
+        embeddings, but the calling base method create_embeddings() 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.
+
+        The following is a rough approximation of receiving logits back from the model
+        and converting this to a sparse vector which can then be decoded to token:weights:
+
+        ----------------------------------------------------------------------------------
+        Imagine your vocabulary is just 5 words: ["cat", "dog", "bird", "fish", "tree"]
+        Vocabulary indices:                      [  0,     1,      2,      3,       4]
+
+        1. MODEL RETURNS LOGITS
+        Let's say you input the text: "cat and dog"
+        After tokenization, you have 3 tokens at 3 sequence positions
+        The model outputs logits - a score for EVERY vocab word at EVERY position:
+
+        logits = [
+            # Position 0 (word "cat"):  scores for each vocab word at this position
+            [9.2,  1.1,  0.3,  0.5,  0.2],  # "cat" gets high score (9.2)
+
+            # Position 1 (word "and" - not in our toy vocab, but tokenized somehow):
+            [2.1,  1.8,  0.4,  0.3,  0.9],  # moderate scores everywhere
+
+            # Position 2 (word "dog"):
+            [0.8,  8.7,  0.2,  0.4,  0.1],  # "dog" gets high score (8.7)
+        ]
+        Shape: (3 positions, 5 vocab words)
+
+
+        2. PRODUCE SPARSE VECTORS FROM LOGITS
+        We collapse the sequence positions by taking the MAX score for each vocab word:
+
+        sparse_vector = [
+            max(9.2, 2.1, 0.8),  # "cat": take max across all 3 positions = 9.2
+            max(1.1, 1.8, 8.7),  # "dog": take max = 8.7
+            max(0.3, 0.4, 0.2),  # "bird": take max = 0.4
+            max(0.5, 0.3, 0.4),  # "fish": take max = 0.5
+            max(0.2, 0.9, 0.1),  # "tree": take max = 0.9
+        ]
+
+        Apply transformations (ReLU, double-log) to make it sparser:
+        sparse_vector = [5.1, 4.8, 0.0, 0.0, 0.0]  # smaller values become 0
+
+        Final result:
+        {"cat": 5.1, "dog": 4.8}  # Only the relevant words have non-zero weights
+        ----------------------------------------------------------------------------------
+
+        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()}
+
+        # get model logits output
+        with torch.no_grad():
+            output = self._model(**features)[0]
+
+        # generate sparse vectors from model logits
+        sparse_vectors = self._get_sparse_vectors(features, output)
+
+        # decode 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
+
+        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)
+        """
+        # max pooling with attention mask
+        values, _ = torch.max(output * features["attention_mask"].unsqueeze(-1), dim=1)
+
+        # apply the v3 model activation
+        values = torch.log(1 + torch.log(1 + torch.relu(values)))
+
+        # zero out special tokens
+        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]
+
+            # 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)
+
+        return results
diff --git a/tests/conftest.py b/tests/conftest.py
index 6f0d7df..6c82267 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -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_os_neural_sparse_doc_v3_gte.py b/tests/test_os_neural_sparse_doc_v3_gte.py
index 462ae85..02972a8 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")
+    input_record = 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(input_record)
+
+
+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)
+
+    input_record = 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(input_record)
+
+    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] == {}

From 296b87052ba641d0a0b5d69802dfc3cd442c2f0a Mon Sep 17 00:00:00 2001
From: Graham Hukill <ghukill@gmail.com>
Date: Wed, 5 Nov 2025 15:09:28 -0500
Subject: [PATCH 3/4] Update commentary around model document encoding

---
 embeddings/cli.py                             |   4 +-
 embeddings/models/base.py                     |  14 +--
 .../models/os_neural_sparse_doc_v3_gte.py     | 103 +++++++-----------
 tests/conftest.py                             |  10 +-
 tests/test_models.py                          |   8 +-
 tests/test_os_neural_sparse_doc_v3_gte.py     |   8 +-
 6 files changed, 62 insertions(+), 85 deletions(-)

diff --git a/embeddings/cli.py b/embeddings/cli.py
index e6be057..e761686 100644
--- a/embeddings/cli.py
+++ b/embeddings/cli.py
@@ -230,10 +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/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 cfe3dba..460f8fa 100644
--- a/embeddings/models/os_neural_sparse_doc_v3_gte.py
+++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py
@@ -173,32 +173,24 @@ def load(self) -> None:
             f"{time.perf_counter() - start_time:.2f}s"
         )
 
-    def create_embedding(self, input_record: EmbeddingInput) -> Embedding:
-        """Create sparse embeddings for the input text (document encoding).
-
-        This method generates sparse document embeddings.
-
-        Process follows the model card exactly:
-        1. Tokenize the document
-        2. Pass through the masked language model to get logits
-        3. Convert logits to sparse vector
-        6. Return both raw sparse vector and decoded token-weight pairs
+    def create_embedding(self, embedding_input: EmbeddingInput) -> Embedding:
+        """Create sparse vector and decoded token weight embeddings for an input text.
 
         Args:
-            input_record: The input containing text to embed
+            embedding_input: EmbeddingInput object with a .text attribute
         """
         # generate the sparse embeddings
-        sparse_vector, decoded_tokens = self._encode_documents([input_record.text])[0]
+        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=input_record.timdex_record_id,
-            run_id=input_record.run_id,
-            run_record_offset=input_record.run_record_offset,
+            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=input_record.embedding_strategy,
+            embedding_strategy=embedding_input.embedding_strategy,
             embedding_vector=sparse_vector_list,
             embedding_token_weights=decoded_tokens,
         )
@@ -212,53 +204,32 @@ def _encode_documents(
         This follows the pattern outlined on the HuggingFace model card for document
         encoding.
 
-        This method will accommodate a list of text inputs, and return a list of
-        embeddings, but the calling base method create_embeddings() is a singular input +
+        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.
-
-        The following is a rough approximation of receiving logits back from the model
-        and converting this to a sparse vector which can then be decoded to token:weights:
+        event we want something like a create_multiple_embeddings() method in the future,
+        but only returns a single result.
 
-        ----------------------------------------------------------------------------------
-        Imagine your vocabulary is just 5 words: ["cat", "dog", "bird", "fish", "tree"]
-        Vocabulary indices:                      [  0,     1,      2,      3,       4]
+        At a very high level, the following is performed:
 
-        1. MODEL RETURNS LOGITS
-        Let's say you input the text: "cat and dog"
-        After tokenization, you have 3 tokens at 3 sequence positions
-        The model outputs logits - a score for EVERY vocab word at EVERY position:
+        1. We tokenize the input text into "features" using the model's tokenizer.
 
-        logits = [
-            # Position 0 (word "cat"):  scores for each vocab word at this position
-            [9.2,  1.1,  0.3,  0.5,  0.2],  # "cat" gets high score (9.2)
-
-            # Position 1 (word "and" - not in our toy vocab, but tokenized somehow):
-            [2.1,  1.8,  0.4,  0.3,  0.9],  # moderate scores everywhere
-
-            # Position 2 (word "dog"):
-            [0.8,  8.7,  0.2,  0.4,  0.1],  # "dog" gets high score (8.7)
-        ]
-        Shape: (3 positions, 5 vocab words)
+        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.
 
-        2. PRODUCE SPARSE VECTORS FROM LOGITS
-        We collapse the sequence positions by taking the MAX score for each vocab word:
-
-        sparse_vector = [
-            max(9.2, 2.1, 0.8),  # "cat": take max across all 3 positions = 9.2
-            max(1.1, 1.8, 8.7),  # "dog": take max = 8.7
-            max(0.3, 0.4, 0.2),  # "bird": take max = 0.4
-            max(0.5, 0.3, 0.4),  # "fish": take max = 0.5
-            max(0.2, 0.9, 0.1),  # "tree": take max = 0.9
-        ]
-
-        Apply transformations (ReLU, double-log) to make it sparser:
-        sparse_vector = [5.1, 4.8, 0.0, 0.0, 0.0]  # smaller values become 0
-
-        Final result:
-        {"cat": 5.1, "dog": 4.8}  # Only the relevant words have non-zero weights
-        ----------------------------------------------------------------------------------
+        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
@@ -278,14 +249,14 @@ def _encode_documents(
         # move to CPU or GPU device, depending on what's available
         features = {k: v.to(self._device) for k, v in features.items()}
 
-        # get model logits output
+        # 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
+        # generate sparse vectors from model logits tensor
         sparse_vectors = self._get_sparse_vectors(features, output)
 
-        # decode to token-weight dictionaries
+        # decode sparse vectors to token-weight dictionaries
         decoded = self._decode_sparse_vectors(sparse_vectors)
 
         # return list of tuple(vector, decoded token weights) embedding results
@@ -304,6 +275,10 @@ def _get_sparse_vectors(
             2. log(1 + log(1 + relu())) transformation
             3. Zero out special tokens
 
+        The end resul 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)
@@ -311,13 +286,15 @@ def _get_sparse_vectors(
         Returns:
             Sparse vectors of shape (batch_size, vocab_size)
         """
-        # max pooling with attention mask
+        # 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)
 
-        # apply the v3 model activation
+        # compress values to create sparsity: ReLU removes negatives,
+        # double-log shrinks large values
         values = torch.log(1 + torch.log(1 + torch.relu(values)))
 
-        # zero out special tokens
+        # remove special tokens like [CLS], [SEP], [PAD]
         values[:, self._special_token_ids] = 0
 
         return values
diff --git a/tests/conftest.py b/tests/conftest.py
index 6c82267..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},
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 02972a8..0e39bd5 100644
--- a/tests/test_os_neural_sparse_doc_v3_gte.py
+++ b/tests/test_os_neural_sparse_doc_v3_gte.py
@@ -293,7 +293,7 @@ def test_load_sets_up_special_token_ids(
 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")
-    input_record = EmbeddingInput(
+    embedding_input = EmbeddingInput(
         timdex_record_id="test:123",
         run_id="run-456",
         run_record_offset=0,
@@ -302,7 +302,7 @@ def test_create_embedding_raises_error_if_model_not_loaded(tmp_path):
     )
 
     with pytest.raises(RuntimeError, match="Model not loaded"):
-        model.create_embedding(input_record)
+        model.create_embedding(embedding_input)
 
 
 def test_create_embedding_returns_embedding_object(tmp_path, monkeypatch):
@@ -317,7 +317,7 @@ def mock_encode_documents(texts):
 
     monkeypatch.setattr(model, "_encode_documents", mock_encode_documents)
 
-    input_record = EmbeddingInput(
+    embedding_input = EmbeddingInput(
         timdex_record_id="test:123",
         run_id="run-456",
         run_record_offset=42,
@@ -325,7 +325,7 @@ def mock_encode_documents(texts):
         text="test document",
     )
 
-    embedding = model.create_embedding(input_record)
+    embedding = model.create_embedding(embedding_input)
 
     assert embedding.timdex_record_id == "test:123"
     assert embedding.run_id == "run-456"

From 4a0cb4d2723606cf7f8151a04b05b6a744acfb8c Mon Sep 17 00:00:00 2001
From: Graham Hukill <ghukill@gmail.com>
Date: Thu, 6 Nov 2025 11:08:02 -0500
Subject: [PATCH 4/4] Fix typo

---
 embeddings/models/os_neural_sparse_doc_v3_gte.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py
index 460f8fa..d1249c6 100644
--- a/embeddings/models/os_neural_sparse_doc_v3_gte.py
+++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py
@@ -275,7 +275,7 @@ def _get_sparse_vectors(
             2. log(1 + log(1 + relu())) transformation
             3. Zero out special tokens
 
-        The end resul is a sparse vector with a length of the model vocabulary, with each
+        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.