diff --git a/aligned/compiler/feature_factory.py b/aligned/compiler/feature_factory.py
index d604d993..805a5575 100644
--- a/aligned/compiler/feature_factory.py
+++ b/aligned/compiler/feature_factory.py
@@ -1237,6 +1237,13 @@ def split(self, pattern: str) -> String:
         feature.transformation = Split(pattern, self)
         return feature
 
+    def format_string(self, features: list[FeatureFactory], format: str) -> String:
+        from aligned.compiler.transformation_factory import FormatString
+
+        feature = String()
+        feature.transformation = FormatString(format, features)
+        return feature
+
     def replace(self, values: dict[str, str]) -> String:
         from aligned.compiler.transformation_factory import ReplaceFactory
 
diff --git a/aligned/compiler/transformation_factory.py b/aligned/compiler/transformation_factory.py
index e7924933..82f04ce4 100644
--- a/aligned/compiler/transformation_factory.py
+++ b/aligned/compiler/transformation_factory.py
@@ -1030,3 +1030,19 @@ def compile(self) -> Transformation:
         return LoadFeature(
             {key: value.name for key, value in self.entities.items()}, self.feature, explode_key
         )
+
+
+@dataclass
+class FormatString(TransformationFactory):
+
+    format: str
+    features: list[FeatureFactory]
+
+    @property
+    def using_features(self) -> list[FeatureFactory]:
+        return self.features
+
+    def compile(self) -> Transformation:
+        from aligned.schemas.transformation import FormatStringTransformation
+
+        return FormatStringTransformation(self.format, [feature.name for feature in self.features])
diff --git a/aligned/exposed_model/interface.py b/aligned/exposed_model/interface.py
index 034866bb..7782579f 100644
--- a/aligned/exposed_model/interface.py
+++ b/aligned/exposed_model/interface.py
@@ -24,6 +24,7 @@ class PredictorFactory:
     def __init__(self) -> None:
         from aligned.exposed_model.mlflow import MLFlowServer, InMemMLFlowAlias
         from aligned.exposed_model.ollama import OllamaGeneratePredictor, OllamaEmbeddingPredictor
+        from aligned.exposed_model.sentence_transformer import SentenceTransformerPredictor
 
         self.supported_predictors = {}
 
@@ -36,6 +37,7 @@ def __init__(self) -> None:
             ShadowModel,
             ABTestModel,
             DillPredictor,
+            SentenceTransformerPredictor,
         ]
         for predictor in types:
             self.supported_predictors[predictor.model_type] = predictor
diff --git a/aligned/exposed_model/sentence_transformer.py b/aligned/exposed_model/sentence_transformer.py
new file mode 100644
index 00000000..5abfc2a9
--- /dev/null
+++ b/aligned/exposed_model/sentence_transformer.py
@@ -0,0 +1,269 @@
+import logging
+import polars as pl
+from dataclasses import dataclass
+from datetime import datetime, timezone, timedelta
+
+from aligned.compiler.feature_factory import (
+    CouldBeModelVersion,
+    Embedding,
+    Entity,
+    EventTimestamp,
+    FeatureFactory,
+    FeatureReferencable,
+    ModelVersion,
+    String,
+)
+from aligned.data_source.batch_data_source import CodableBatchDataSource
+from aligned.feature_store import ModelFeatureStore
+
+from aligned.exposed_model.interface import ExposedModel, PromptModel
+from aligned.retrival_job import RetrivalJob
+from aligned.schemas.feature import Feature, FeatureReference
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SentenceTransformerPredictor(ExposedModel, PromptModel):
+
+    model_name: str
+    embedding_name: str
+
+    prompt_template: str
+    features_to_load: list[FeatureReference]
+
+    precomputed_prompt_key_overwrite: str
+
+    model_type: str = 'sent_tran'
+
+    @property
+    def precomputed_prompt_key(self) -> str | None:
+        return self.precomputed_prompt_key_overwrite
+
+    @property
+    def exposed_at_url(self) -> str | None:
+        return None
+
+    def prompt_template_hash(self) -> str:
+        from hashlib import sha256
+
+        return sha256(self.prompt_template.encode(), usedforsecurity=False).hexdigest()
+
+    async def potential_drift_from_model(self, old_model: ExposedModel) -> str | None:
+        """
+        Checks if a change in model can lead to a potential distribution shift.
+
+        Returns:
+            str: A message explaining the potential drift.
+        """
+        if not isinstance(old_model, SentenceTransformerPredictor):
+            return None
+
+        changes = ''
+        if old_model.model_name != self.model_name:
+            changes += f"Model name changed from `{old_model.model_name}` to `{self.model_name}`.\n"
+
+        if old_model.prompt_template != self.prompt_template:
+            changes += (
+                f"Prompt template changed from `{old_model.prompt_template}` to `{self.prompt_template}`.\n"
+            )
+
+        if changes:
+            return changes
+        else:
+            return None
+
+    @property
+    def as_markdown(self) -> str:
+        return f"""Creating an embedding through a sentence transformer.
+
+This will use the model: `{self.model_name}` to generate the embeddings."""
+
+    async def needed_features(self, store: ModelFeatureStore) -> list[FeatureReference]:
+        return self.features_to_load
+
+    async def needed_entities(self, store: ModelFeatureStore) -> set[Feature]:
+        return store.store.requests_for_features(self.features_to_load).request_result.entities
+
+    async def run_polars(self, values: RetrivalJob, store: ModelFeatureStore) -> pl.DataFrame:
+        import polars as pl
+        from sentence_transformers import SentenceTransformer
+
+        model = SentenceTransformer(model_name_or_path=self.model_name)
+        entities = await values.to_polars()
+
+        prompts: list[str] = []
+
+        if self.precomputed_prompt_key_overwrite in entities.columns:
+            prompts = entities[self.precomputed_prompt_key_overwrite].to_list()
+        else:
+            missing_cols = {feat.name for feat in self.features_to_load} - set(entities.columns)
+            if missing_cols:
+                entities = await store.store.features_for(values, self.features_to_load).to_polars()
+
+            for index, value in enumerate(entities.iter_rows(named=True)):
+                logger.info(f"Processing row {index + 1}/{len(prompts)}")
+
+                prompt = self.prompt_template.format(**value)
+                prompts.append(prompt)
+
+            entities = entities.with_columns(
+                pl.Series(name=self.precomputed_prompt_key_overwrite, values=prompts)
+            )
+
+        responses = model.encode(prompts)
+
+        pred_view = store.model.predictions_view
+        if pred_view.model_version_column:
+            model_version = f"{self.prompt_template_hash()} -> {self.model_name}"
+            model_version_name = pred_view.model_version_column.name
+            entities = entities.with_columns(pl.lit(model_version).alias(model_version_name))
+
+        if pred_view.event_timestamp:
+            new_et = pred_view.event_timestamp.name
+            existing_et = values.request_result.event_timestamp
+            need_to_add_et = new_et not in entities.columns
+
+            if existing_et and need_to_add_et and existing_et in entities.columns:
+                logger.info(f"Using existing event timestamp `{existing_et}` as new timestamp.")
+                entities = entities.with_columns(pl.col(existing_et).alias(new_et))
+            elif need_to_add_et:
+                logger.info('No event timestamp using now as the timestamp.')
+                entities = entities.with_columns(
+                    pl.lit(datetime.now(tz=timezone.utc)).alias(pred_view.event_timestamp.name)
+                )
+
+        return entities.hstack([pl.Series(name=self.embedding_name, values=responses)])
+
+
+def sentence_transform_feature(
+    feature: FeatureReferencable,
+    model: str,
+    output_name: str,
+    precomputed_prompt_key: str = 'full_prompt',
+) -> 'SentenceTransformerPredictor':
+
+    return SentenceTransformerPredictor(
+        model_name=model,
+        embedding_name=output_name,
+        prompt_template=f"{{{feature.feature_reference().name}}}",
+        features_to_load=[feature.feature_reference()],
+        precomputed_prompt_key_overwrite=precomputed_prompt_key,
+    )
+
+
+def sentence_transform_prompt(
+    model: str,
+    features: list[FeatureReferencable],
+    prompt_template: str,
+    output_name: str,
+    precomputed_prompt_key: str = 'full_prompt',
+) -> 'SentenceTransformerPredictor':
+
+    return SentenceTransformerPredictor(
+        model_name=model,
+        embedding_name=output_name,
+        prompt_template=prompt_template,
+        features_to_load=[feature.feature_reference() for feature in features],
+        precomputed_prompt_key_overwrite=precomputed_prompt_key,
+    )
+
+
+def sentence_transformer_contract(
+    input: FeatureReferencable | list[FeatureReferencable],
+    contract_name: str,
+    model: str,
+    entities: list[FeatureFactory] | FeatureFactory,
+    embedding_size: int,
+    prompt_template: str | None = None,
+    output_source: CodableBatchDataSource | None = None,
+    contacts: list[str] | None = None,
+    tags: list[str] | None = None,
+    precomputed_prompt_key: str = 'full_prompt',
+    model_version_field: FeatureFactory | None = None,
+    additional_metadata: list[FeatureFactory] | None = None,
+    acceptable_freshness: timedelta | None = None,
+    unacceptable_freshness: timedelta | None = None,
+):
+    from aligned import model_contract, FeatureInputVersions
+
+    if isinstance(input, FeatureReferencable) and prompt_template is None:
+        ref = input.feature_reference()
+        prompt_template = f"{{{ref.name}}}"
+
+    if prompt_template is None:
+        raise ValueError('prompt_template must be provided if input is a list')
+
+    if not isinstance(input, list):
+        input = [input]
+
+    @model_contract(
+        name=contract_name,
+        description=f'Contract for generating embeddings using the {model} through Ollama',
+        input_features=FeatureInputVersions(
+            default_version='default', versions={'default': input}  # type: ignore
+        ),
+        exposed_model=sentence_transform_prompt(
+            model=model,
+            features=input,
+            prompt_template=prompt_template,
+            output_name='embedding',
+            precomputed_prompt_key=precomputed_prompt_key,
+        ),
+        output_source=output_source,
+        contacts=contacts,
+        tags=tags,
+        acceptable_freshness=acceptable_freshness,
+        unacceptable_freshness=unacceptable_freshness,
+    )
+    class ScentenceTransformerContract:
+        updated_at = EventTimestamp()
+        embedding = Embedding(embedding_size=embedding_size)
+        full_prompt = String().with_name(precomputed_prompt_key)
+
+    if not isinstance(entities, list):
+        entities = [entities]
+
+    for entity in entities:
+        if isinstance(entity, Entity):
+            feature = entity._dtype.copy_type()
+        else:
+            feature = entity.copy_type()
+
+        new_entity = Entity(feature)
+
+        feature._name = entity.name
+        new_entity._name = entity.name
+
+        setattr(ScentenceTransformerContract.contract, entity.name, new_entity)
+
+    def add_feature(feature: FeatureFactory) -> None:
+
+        assert feature._name, (
+            'Trying to add a feature without any name. '
+            'Consider using the `.with_name(...)` to manually set it.'
+        )
+        if feature._location is None:
+            setattr(ScentenceTransformerContract.contract, feature.name, feature)
+            return
+
+        feature_copy = feature.copy_type()
+        feature_copy._name = feature._name
+        feature_copy.constraints = feature.constraints.copy() if feature.constraints else None
+        setattr(ScentenceTransformerContract.contract, feature_copy.name, feature_copy)
+
+    if model_version_field is not None:
+        if isinstance(model_version_field, ModelVersion):
+            add_feature(model_version_field)
+        elif isinstance(model_version_field, CouldBeModelVersion):
+            add_feature(model_version_field.as_model_version().with_name(model_version_field.name))
+        else:
+            raise ValueError(f"Feature {model_version_field} can not be a model version.")
+    else:
+        add_feature(String().as_model_version().with_name('model_version'))
+
+    for feature in additional_metadata or []:
+        add_feature(feature)
+
+    return ScentenceTransformerContract  # type: ignore
diff --git a/aligned/jobs/tests/test_combined_job.py b/aligned/jobs/tests/test_combined_job.py
index d7b63a60..2feeca5f 100644
--- a/aligned/jobs/tests/test_combined_job.py
+++ b/aligned/jobs/tests/test_combined_job.py
@@ -52,6 +52,8 @@ def append_someting(self, df: pd.DataFrame, store: ContractStore) -> pd.Series:
     def using_row(self, row: dict, store: ContractStore) -> str:
         return row['query'] + ' something'
 
+    new_format = String().format_string([query, contains_mr], format='Hello {contains_mr} - {query}')
+
     @transform_row(using_features=[embedding], return_type=List(String()))
     async def related_entities(self, row: dict, store: ContractStore) -> list[str]:
         df = (
@@ -59,7 +61,6 @@ async def related_entities(self, row: dict, store: ContractStore) -> list[str]:
             .nearest_n_to(entities=[row], number_of_records=2)
             .to_polars()
         )
-        print(df)
         return df['vec_id'].to_list()
 
     not_contains = contains_something.not_equals(True)
@@ -75,7 +76,6 @@ async def test_feature_view_without_entity():
         {'query': ['Hello', 'Hello mr'], 'embedding': [[1, 3], [0, 10]]}
     )
     df = await job.to_polars()
-    print(df)
 
     assert df['contains_mr'].sum() == 1
 
diff --git a/aligned/lazy_imports.py b/aligned/lazy_imports.py
index c7e987e4..867a2a2b 100644
--- a/aligned/lazy_imports.py
+++ b/aligned/lazy_imports.py
@@ -132,10 +132,13 @@ def _lazy_import(module_name: str) -> tuple[ModuleType, bool]:
 
 _PANDAS_AVAILABLE = True
 _PANDERA_AVAILABLE = True
+_SENTENCE_TRANSFORMERS = True
 
 if TYPE_CHECKING:
     import pandas
     import pandera
+    import sentence_transformers
 else:
     pandas, _PANDAS_AVAILABLE = _lazy_import('pandas')
     pandera, _PANDERA_AVAILABLE = _lazy_import('pandera')
+    sentence_transformers, _SENTENCE_TRANSFORMERS = _lazy_import('sentence_transformers')
diff --git a/aligned/schemas/transformation.py b/aligned/schemas/transformation.py
index e028dd69..2f489eec 100644
--- a/aligned/schemas/transformation.py
+++ b/aligned/schemas/transformation.py
@@ -243,6 +243,8 @@ def __init__(self) -> None:
             ArrayAtIndex,
             OllamaEmbedding,
             PolarsMapRowTransformation,
+            LoadFeature,
+            FormatStringTransformation,
         ]:
             self.add(tran_type)
 
@@ -2535,6 +2537,7 @@ class Split(Transformation):
 
     key: str
     separator: str
+    name = 'split'
 
     async def transform_pandas(self, df: pd.DataFrame, store: ContractStore) -> pd.Series:
         return df[self.key].str.split(self.separator)
@@ -2551,6 +2554,7 @@ class LoadFeature(Transformation):
     entities: dict[str, str]
     feature: FeatureReference
     explode_key: str | None
+    name = 'load_feature'
 
     async def transform_pandas(self, df: pd.DataFrame, store: ContractStore) -> pd.Series:
 
@@ -2589,3 +2593,31 @@ async def transform_polars(
         values = values.select(pl.col(self.feature.name).alias(alias))
 
         return pl.concat([df, values.lazy()], how='horizontal')
+
+
+@dataclass
+class FormatStringTransformation(Transformation):
+
+    format: str
+    keys: list[str]
+    name = 'format_string'
+
+    async def transform_pandas(self, df: pd.DataFrame, store: ContractStore) -> pd.Series:
+        values = []
+        for row in df[self.keys].to_dict(orient='records'):  # type: ignore
+            values.append(self.format.format(**row))
+
+        return pd.Series(values)
+
+    async def transform_polars(
+        self, df: pl.LazyFrame, alias: str, store: ContractStore
+    ) -> pl.LazyFrame | pl.Expr:
+
+        polars_df = df.collect()
+        new_rows = []
+
+        for row in polars_df.to_dicts():
+            row[alias] = self.format.format(**row)
+            new_rows.append(row)
+
+        return pl.DataFrame(new_rows).lazy()
diff --git a/poetry.lock b/poetry.lock
index 64f76b11..9b5e4b8a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -610,7 +610,7 @@ pycparser = "*"
 name = "charset-normalizer"
 version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-optional = true
+optional = false
 python-versions = ">=3.7.0"
 files = [
     {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
@@ -1092,6 +1092,22 @@ typing-extensions = ">=4.5.0"
 [package.extras]
 all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
 
+[[package]]
+name = "filelock"
+version = "3.16.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
+    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
+]
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
 [[package]]
 name = "flask"
 version = "3.0.3"
@@ -1283,7 +1299,7 @@ files = [
 name = "fsspec"
 version = "2024.9.0"
 description = "File-system specification"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
@@ -1575,6 +1591,40 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.25.2"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "huggingface_hub-0.25.2-py3-none-any.whl", hash = "sha256:1897caf88ce7f97fe0110603d8f66ac264e3ba6accdf30cd66cc0fed5282ad25"},
+    {file = "huggingface_hub-0.25.2.tar.gz", hash = "sha256:a1014ea111a5f40ccd23f7f7ba8ac46e20fa3b658ced1f86a00c75c06ec6423c"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.8"
@@ -1659,7 +1709,7 @@ files = [
 name = "jinja2"
 version = "3.1.4"
 description = "A very fast and expressive template engine."
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
     {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
@@ -1676,7 +1726,7 @@ i18n = ["Babel (>=2.7)"]
 name = "joblib"
 version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
@@ -1894,7 +1944,7 @@ testing = ["coverage", "pyyaml"]
 name = "markupsafe"
 version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
@@ -2122,6 +2172,23 @@ langchain = ["langchain (>=0.1.0,<=0.2.15)"]
 sqlserver = ["mlflow-dbstore"]
 xethub = ["mlflow-xethub"]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "msal"
 version = "1.31.0"
@@ -2282,6 +2349,25 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.4.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+files = [
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+    {file = "networkx-3.4.1.tar.gz", hash = "sha256:f9df45e85b78f5bd010993e897b4f1fdb242c11e015b101bd951e5c0e29982d8"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "nodeenv"
 version = "1.9.1"
@@ -2338,6 +2424,150 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.1.3.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.1.105"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.1.105"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.1.105"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
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+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "9.1.0.70"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.0.2.54"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.2.106"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
+[[package]]
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+version = "11.4.5.107"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+]
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+[package.dependencies]
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+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.1.0.106"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.20.5"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
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+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.6.77"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvjitlink_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:3bf10d85bb1801e9c894c6e197e44dd137d2a0a9e43f8450e9ad13f2df0dd52d"},
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+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.1.105"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+]
+
 [[package]]
 name = "ollama"
 version = "0.1.9"
@@ -2541,7 +2771,7 @@ files = [
 name = "pillow"
 version = "9.5.0"
 description = "Python Imaging Library (Fork)"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
     {file = "Pillow-9.5.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:ace6ca218308447b9077c14ea4ef381ba0b67ee78d64046b3f19cf4e1139ad16"},
@@ -3227,7 +3457,7 @@ files = [
 name = "pyyaml"
 version = "6.0.2"
 description = "YAML parser and emitter for Python"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
@@ -3317,11 +3547,114 @@ async-timeout = {version = ">=4.0.2", markers = "python_full_version <= \"3.11.2
 hiredis = ["hiredis (>=1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
 
+[[package]]
+name = "regex"
+version = "2024.9.11"
+description = "Alternative regular expression module, to replace re."
+optional = false
+python-versions = ">=3.8"
+files = [
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+version = "3.2.0"
+description = "State-of-the-Art Text Embeddings"
+optional = false
+python-versions = ">=3.8"
+files = [
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+scipy = "*"
+torch = ">=1.11.0"
+tqdm = "*"
+transformers = ">=4.41.0,<5.0.0"
+
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+onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.0)"]
+openvino = ["optimum-intel[openvino] (>=1.20.0)"]
+train = ["accelerate (>=0.20.3)", "datasets"]
+
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+name = "setuptools"
+version = "75.1.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
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+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.11.*)", "pytest-mypy"]
+
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 version = "1.16.0"
@@ -3640,17 +4152,151 @@ anyio = ">=3.4.0,<5"
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
 
+[[package]]
+name = "sympy"
+version = "1.13.3"
+description = "Computer algebra system (CAS) in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.5.0"
 description = "threadpoolctl"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+ruff = ["ruff (==0.5.1)"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
+timm = ["timm (<=0.9.16)"]
+tokenizers = ["tokenizers (>=0.20,<0.21)"]
+torch = ["accelerate (>=0.26.0)", "torch"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "3.0.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+    {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
+    {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
+    {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb"},
+    {file = "triton-3.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bcbf3b1c48af6a28011a5c40a5b3b9b5330530c3827716b5fbf6d7adcc1e53e9"},
+    {file = "triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6e5727202f7078c56f91ff13ad0c1abab14a0e7f2c87e91b12b6f64f3e8ae609"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typeguard"
 version = "4.3.0"
@@ -3796,7 +4587,7 @@ files = [
 name = "urllib3"
 version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
@@ -4082,4 +4873,4 @@ sql = ["sqlglot"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "27393c56c557fd6269c65bc698e0823f6077ce0109e84e48bdcfe349d9558149"
+content-hash = "3ce14fd29f7cfbc778f15c6db761b73c0b0f54788c0319fc297ddf50ffd537b3"
diff --git a/pyproject.toml b/pyproject.toml
index d6daa084..ed9a7d16 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "aligned"
-version = "0.0.108"
+version = "0.0.109"
 description = "A data managment and lineage tool for ML applications."
 authors = ["Mats E. Mollestad <mats@mollestad.no>"]
 license = "Apache-2.0"
@@ -72,6 +72,7 @@ mlflow = { version = "^2.11.3", optional = true }
 adlfs = { version = "^2024.4.1", optional = true }
 lancedb = { version = "^0.8.2", optional = true }
 deltalake = { version = "^0.18.1", optional = true }
+sentence-transformers = { version = "^3.2.0", optional = true }
 
 [tool.poetry.extras]
 aws = ["aioaws", "connectorx"]
@@ -88,6 +89,7 @@ azure = ["adlfs"]
 deltalake = ["deltalake"]
 lancedb = ["lancedb"]
 pandas = ["pandas"]
+sentence-transformers = ["sentence-transformers"]
 
 [tool.poetry.group.dev.dependencies]
 types-redis = "^4.2.6"