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Relation Extraction

The RelationExtraction task performs joint entity and relation extraction, identifying relationships between entities in text.

Usage

relations = {
    "works_for": "A person works for a company or organization.",
    "located_in": "A place or organization is located in a city, country, or region.",
    "founded": "A person founded a company or organization.",
}

fewshot_examples = [
    relation_extraction.FewshotExample(
        text="Clara Barton founded the American Red Cross.",
        triplets=[
            RelationTriplet(
                head=RelationEntity(text="Clara Barton", entity_type="PERSON"),
                relation="founded",
                tail=RelationEntity(text="American Red Cross", entity_type="ORGANIZATION"),
            )
        ],
    ),
    relation_extraction.FewshotExample(
        text="Irving Stowe founded Greenpeace.",
        triplets=[
            RelationTriplet(
                head=RelationEntity(text="Irving Stowe", entity_type="PERSON"),
                relation="founded",
                tail=RelationEntity(text="Greenpeace", entity_type="ORGANIZATION"),
            )
        ],
    ),
]

fewshot_args = {"fewshot_examples": fewshot_examples} if fewshot else {}

task = relation_extraction.RelationExtraction(
    relations=relations,
    model=batch_runtime.model,
    model_settings=batch_runtime.model_settings,
    batch_size=batch_runtime.batch_size,
    entity_types=["PERSON", "ORGANIZATION", "LOCATION"],
    **fewshot_args
)

pipe = Pipeline(task)
docs = list(pipe(relation_extraction_docs))

Results

The RelationExtraction task returns a unified Result object containing a list of RelationTriplet objects.

class Result(pydantic.BaseModel):
    """Result of a relation extraction task.

    Attributes:
        triplets: List of extracted relation triplets.
    """

    triplets: list[RelationTriplet]

Each RelationTriplet consists of: - head: A RelationEntity representing the subject. - relation: The string identifier of the relationship. - tail: A RelationEntity representing the object.

A RelationEntity includes the surface text, entity_type, and character start/end offsets.

Relation extraction predictive task.

RelationExtraction

Bases: PredictiveTask[TaskPromptSignature, TaskResult, _TaskBridge]

Extract relations between entities in text.

Source code in sieves/tasks/predictive/relation_extraction/core.py 
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class RelationExtraction(PredictiveTask[TaskPromptSignature, TaskResult, _TaskBridge]):
    """Extract relations between entities in text."""

    def __init__(
        self,
        relations: Sequence[str] | dict[str, str],
        model: TaskModel,
        entity_types: Sequence[str] | dict[str, str] | None = None,
        task_id: str | None = None,
        include_meta: bool = True,
        batch_size: int = -1,
        prompt_instructions: str | None = None,
        fewshot_examples: Sequence[FewshotExample] = (),
        model_settings: ModelSettings = ModelSettings(),
        condition: Callable[[Doc], bool] | None = None,
    ) -> None:
        """Initialize RelationExtraction task.

        :param relations: Relations to extract. Can be a list of relation types or a dict mapping types to descriptions.
        :param model: Model to use.
        :param entity_types: Optional constraints on entity types involved in relations.
        :param task_id: Task ID.
        :param include_meta: Whether to include meta information generated by the task.
        :param batch_size: Batch size to use for inference. Use -1 to process all documents at once.
        :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
        :param fewshot_examples: Few-shot examples.
        :param model_settings: Settings for structured generation.
        :param condition: Optional callable that determines whether to process each document.
        """
        self._relations = relations
        self._entity_types = entity_types

        super().__init__(
            model=model,
            task_id=task_id,
            include_meta=include_meta,
            batch_size=batch_size,
            overwrite=False,
            prompt_instructions=prompt_instructions,
            fewshot_examples=fewshot_examples,
            model_settings=model_settings,
            condition=condition,
        )

    @override
    def _init_bridge(self, model_type: ModelType) -> _TaskBridge:
        if model_type == ModelType.gliner:
            if self._entity_types is not None:
                warnings.warn(
                    "GliNER2 backend does not support entity type constraints for relation extraction. "
                    "The `entity_types` parameter will be ignored.",
                )

            return GliNERBridge(
                task_id=self._task_id,
                prompt_instructions=self._custom_prompt_instructions,
                prompt_signature=gliner2.inference.engine.Schema().relations(
                    relation_types=self._relations,
                ),
                model_settings=self._model_settings,
                inference_mode=gliner_.InferenceMode.relations,
            )

        bridge_types: dict[
            ModelType, type[DSPyRelationExtraction | LangChainRelationExtraction | OutlinesRelationExtraction]
        ] = {
            ModelType.dspy: DSPyRelationExtraction,
            ModelType.langchain: LangChainRelationExtraction,
            ModelType.outlines: OutlinesRelationExtraction,
        }

        try:
            return bridge_types[model_type](
                task_id=self._task_id,
                relations=self._relations,
                entity_types=self._entity_types,
                prompt_instructions=self._custom_prompt_instructions,
                model_settings=self._model_settings,
            )
        except KeyError as err:
            raise KeyError(f"Model type {model_type} is not supported by {self.__class__.__name__}.") from err

    @staticmethod
    @override
    def supports() -> set[ModelType]:
        return {
            ModelType.dspy,
            ModelType.gliner,
            ModelType.langchain,
            ModelType.outlines,
        }

    @override
    @property
    def _state(self) -> dict[str, Any]:
        return {
            **super()._state,
            "relations": self._relations,
            "entity_types": self._entity_types,
        }

    @override
    def to_hf_dataset(self, docs: Iterable[Doc], threshold: float = 0.5) -> datasets.Dataset:
        # Define metadata and features.
        entity_feature = datasets.Features(
            {
                "text": datasets.Value("string"),
                "entity_type": datasets.Value("string"),
            }
        )
        triplet_feature = datasets.Features(
            {
                "head": entity_feature,
                "relation": datasets.Value("string"),
                "tail": entity_feature,
            }
        )
        features = datasets.Features({"text": datasets.Value("string"), "triplets": datasets.Sequence(triplet_feature)})
        info = datasets.DatasetInfo(
            description=f"Relation extraction dataset. Generated with sieves v{Config.get_version()}.",
            features=features,
        )

        # Fetch data used for generating dataset.
        try:
            data: list[dict[str, Any]] = []
            for doc in docs:
                triplets: list[dict[str, Any]] = []
                for triplet in doc.results[self._task_id].triplets:
                    triplets.append(
                        {
                            "head": triplet.head.model_dump(),
                            "relation": triplet.relation,
                            "tail": triplet.tail.model_dump(),
                        }
                    )
                data.append({"text": doc.text, "triplets": triplets})
        except KeyError as err:
            raise KeyError(f"Not all documents have results for this task with ID {self._task_id}") from err

        # Create dataset.
        return datasets.Dataset.from_list(data, features=features, info=info)

    @override
    def distill(
        self,
        base_model_id: str,
        framework: DistillationFramework,
        data: datasets.Dataset | Sequence[Doc],
        output_path: Path | str,
        val_frac: float,
        init_kwargs: dict[str, Any] | None = None,
        train_kwargs: dict[str, Any] | None = None,
        seed: int | None = None,
    ) -> None:
        raise NotImplementedError

fewshot_examples property

Return few-shot examples.

Returns:

Type Description
Sequence[FewshotExample]

Few-shot examples.

id property

Return task ID.

Used by pipeline for results and dependency management.

Returns:

Type Description
str

Task ID.

prompt_signature_description property

Return prompt signature description.

Returns:

Type Description
str | None

Prompt signature description.

prompt_template property

Return prompt template.

Returns:

Type Description
str

Prompt template.

__add__(other)

Chain this task with another task or pipeline using the + operator.

This returns a new Pipeline that executes this task first, followed by the task(s) in other. The original task(s)/pipeline are not mutated.

Cache semantics: - If other is a Pipeline, the resulting pipeline adopts other's use_cache setting (because the left-hand side is a single task). - If other is a Task, the resulting pipeline defaults to use_cache=True.

Parameters:

Name Type Description Default
other Task | Pipeline

A Task or Pipeline to execute after this task.

 required

Returns:

Type Description
Pipeline

A new Pipeline representing the chained execution.

Raises:

Type Description
TypeError

If other is not a Task or Pipeline.
Source code in sieves/tasks/core.py 
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def __add__(self, other: Task | Pipeline) -> Pipeline:
    """Chain this task with another task or pipeline using the ``+`` operator.

    This returns a new ``Pipeline`` that executes this task first, followed by the
    task(s) in ``other``. The original task(s)/pipeline are not mutated.

    Cache semantics:
    - If ``other`` is a ``Pipeline``, the resulting pipeline adopts ``other``'s
      ``use_cache`` setting (because the left-hand side is a single task).
    - If ``other`` is a ``Task``, the resulting pipeline defaults to ``use_cache=True``.

    :param other: A ``Task`` or ``Pipeline`` to execute after this task.
    :return: A new ``Pipeline`` representing the chained execution.
    :raises TypeError: If ``other`` is not a ``Task`` or ``Pipeline``.
    """
    # Lazy import to avoid circular dependency at module import time.
    from sieves.pipeline import Pipeline

    if isinstance(other, Pipeline):
        return Pipeline(tasks=[self, *other.tasks], use_cache=other.use_cache)

    if isinstance(other, Task):
        return Pipeline(tasks=[self, other])

    raise TypeError(f"Cannot chain Task with {type(other).__name__}")

__call__(docs)

Execute task with conditional logic.

Checks the condition for each document without materializing all docs upfront. Passes all documents that pass the condition to _call() for proper batching. Documents that fail the condition have results[task_id] set to None.

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to process.

 required

Returns:

Type Description
Iterable[Doc]

Processed docs (in original order).
Source code in sieves/tasks/core.py 
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def __call__(self, docs: Iterable[Doc]) -> Iterable[Doc]:
    """Execute task with conditional logic.

    Checks the condition for each document without materializing all docs upfront.
    Passes all documents that pass the condition to _call() for proper batching.
    Documents that fail the condition have results[task_id] set to None.

    :param docs: Docs to process.
    :return: Processed docs (in original order).
    """
    # Create three independent iterators:
    #   1. Check which docs pass condition.
    #   2. Yield only passing docs to _call().
    #   3. Iterate and yield results in order.
    docs_iters = itertools.tee(docs, 3)

    # First pass: determine which docs pass the condition by index
    passing_indices: set[int] = set()

    for idx, doc in enumerate(docs_iters[0]):
        if self._condition is None or self._condition(doc):
            passing_indices.add(idx)

    # Process all passing docs together.
    processed = self._call(d for i, d in enumerate(docs_iters[1]) if i in passing_indices)
    processed_iter = iter(processed) if not isinstance(processed, Iterator) else processed

    # Iterate through original docs in order and yield results
    for idx, doc in enumerate(docs_iters[2]):
        if idx in passing_indices:
            # Doc passed condition - use processed result.
            yield next(processed_iter)
        else:
            # Doc failed condition - set None result and yield original.
            doc.results[self.id] = None
            yield doc

__init__(relations, model, entity_types=None, task_id=None, include_meta=True, batch_size=-1, prompt_instructions=None, fewshot_examples=(), model_settings=ModelSettings(), condition=None)

Initialize RelationExtraction task.

Parameters:

Name Type Description Default
relations Sequence[str] | dict[str, str]

Relations to extract. Can be a list of relation types or a dict mapping types to descriptions.

 required
model TaskModel

Model to use.

 required
entity_types Sequence[str] | dict[str, str] | None

Optional constraints on entity types involved in relations.

 None
task_id str | None

Task ID.

 None
include_meta bool

Whether to include meta information generated by the task.

 True
batch_size int

Batch size to use for inference. Use -1 to process all documents at once.

 -1
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 None
fewshot_examples Sequence[FewshotExample]

Few-shot examples.

 ()
model_settings ModelSettings

Settings for structured generation.

 ModelSettings()
condition Callable[[Doc], bool] | None

Optional callable that determines whether to process each document.

 None
Source code in sieves/tasks/predictive/relation_extraction/core.py 
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def __init__(
    self,
    relations: Sequence[str] | dict[str, str],
    model: TaskModel,
    entity_types: Sequence[str] | dict[str, str] | None = None,
    task_id: str | None = None,
    include_meta: bool = True,
    batch_size: int = -1,
    prompt_instructions: str | None = None,
    fewshot_examples: Sequence[FewshotExample] = (),
    model_settings: ModelSettings = ModelSettings(),
    condition: Callable[[Doc], bool] | None = None,
) -> None:
    """Initialize RelationExtraction task.

    :param relations: Relations to extract. Can be a list of relation types or a dict mapping types to descriptions.
    :param model: Model to use.
    :param entity_types: Optional constraints on entity types involved in relations.
    :param task_id: Task ID.
    :param include_meta: Whether to include meta information generated by the task.
    :param batch_size: Batch size to use for inference. Use -1 to process all documents at once.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param fewshot_examples: Few-shot examples.
    :param model_settings: Settings for structured generation.
    :param condition: Optional callable that determines whether to process each document.
    """
    self._relations = relations
    self._entity_types = entity_types

    super().__init__(
        model=model,
        task_id=task_id,
        include_meta=include_meta,
        batch_size=batch_size,
        overwrite=False,
        prompt_instructions=prompt_instructions,
        fewshot_examples=fewshot_examples,
        model_settings=model_settings,
        condition=condition,
    )

deserialize(config, **kwargs) classmethod

Generate PredictiveTask instance from config.

Parameters:

Name Type Description Default
config Config

Config to generate instance from.

 required
kwargs dict[str, Any]

Values to inject into loaded config.

 {}

Returns:

Type Description
PredictiveTask[TaskPromptSignature, TaskResult, TaskBridge]

Deserialized PredictiveTask instance.
Source code in sieves/tasks/predictive/core.py 
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@classmethod
def deserialize(
    cls, config: Config, **kwargs: dict[str, Any]
) -> PredictiveTask[TaskPromptSignature, TaskResult, TaskBridge]:
    """Generate PredictiveTask instance from config.

    :param config: Config to generate instance from.
    :param kwargs: Values to inject into loaded config.
    :return PredictiveTask[TaskPromptSignature, TaskResult, _TaskBridge]: Deserialized PredictiveTask instance.
    """
    init_dict = config.to_init_dict(cls, **kwargs)
    init_dict["model_settings"] = ModelSettings.model_validate(init_dict["model_settings"])

    return cls(**init_dict)

optimize(optimizer, verbose=True)

Optimize task prompt and few-shot examples with the available optimization config.

Updates task to use best prompt and few-shot examples found by the optimizer.

Parameters:

Name Type Description Default
optimizer Optimizer

Optimizer to run.

 required
verbose bool

Whether to suppress output. DSPy produces a good amount of logs, so this can be useful to not pollute your terminal. Only warnings and errors will be printed.

 True

Returns:

Type Description
tuple[str, Sequence[FewshotExample]]

Best found prompt and few-shot examples.
Source code in sieves/tasks/predictive/core.py 
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def optimize(self, optimizer: optimization.Optimizer, verbose: bool = True) -> tuple[str, Sequence[FewshotExample]]:
    """Optimize task prompt and few-shot examples with the available optimization config.

    Updates task to use best prompt and few-shot examples found by the optimizer.

    :param optimizer: Optimizer to run.
    :param verbose: Whether to suppress output. DSPy produces a good amount of logs, so this can be useful to
        not pollute your terminal. Only warnings and errors will be printed.

    :return tuple[str, Sequence[FewshotExample]]: Best found prompt and few-shot examples.
    """
    assert len(self._fewshot_examples) > 1, "At least two few-shot examples need to be provided to optimize."

    # Run optimizer to get best prompt and few-shot examples.
    signature = self._get_task_signature()
    dspy_examples = [ex.to_dspy() for ex in self._fewshot_examples]

    def _pred_eval(truth: dspy.Example, pred: dspy.Prediction, trace: Any | None = None) -> float:
        """Wrap optimization evaluation, inject model.

        :param truth: Ground truth.
        :param pred: Predicted value.
        :param trace: Optional trace information.
        :return: Metric value between 0.0 and 1.0.
        :raises KeyError: If target fields are missing from truth or prediction.
        :raises ValueError: If similarity score cannot be parsed from LLM response.
        """
        return self._evaluate_optimization_example(truth, pred, trace, model=optimizer.model)

    if verbose:
        best_prompt, best_examples = optimizer(signature, dspy_examples, _pred_eval, verbose=verbose)
    else:
        # Temporarily suppress DSPy logs.
        dspy_logger = logging.getLogger("dspy")
        optuna_logger = logging.getLogger("optuna")
        original_dspy_level = dspy_logger.level
        original_optuna_level = optuna_logger.level

        try:
            dspy_logger.setLevel(logging.ERROR)
            optuna_logger.setLevel(logging.ERROR)
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                best_prompt, best_examples = optimizer(signature, dspy_examples, _pred_eval, verbose=verbose)
        finally:
            dspy_logger.setLevel(original_dspy_level)
            optuna_logger.setLevel(original_optuna_level)

    # Update few-shot examples and prompt instructions.
    fewshot_example_cls = self._fewshot_examples[0].__class__
    self._fewshot_examples = [fewshot_example_cls.from_dspy(ex) for ex in best_examples]
    self._validate_fewshot_examples()
    self._custom_prompt_instructions = best_prompt

    # Reinitialize bridge to use new prompt and few-shot examples.
    self._bridge = self._init_bridge(ModelType.get_model_type(self._model_wrapper))

    return best_prompt, self._fewshot_examples

serialize()

Serialize task.

Returns:

Type Description
Config

Config instance.
Source code in sieves/tasks/core.py 
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def serialize(self) -> Config:
    """Serialize task.

    :return: Config instance.
    """
    return Config.create(self.__class__, {k: Attribute(value=v) for k, v in self._state.items()})

Bridges for relation extraction task.

DSPyRelationExtraction

Bases: RelationExtractionBridge[PromptSignature, Result, InferenceMode]

DSPy bridge for relation extraction.

Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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class DSPyRelationExtraction(RelationExtractionBridge[dspy_.PromptSignature, dspy_.Result, dspy_.InferenceMode]):
    """DSPy bridge for relation extraction."""

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return f"""
        Extract relations between entities in the text.
        Relations to look for: {self._relations}
        {self._get_relation_descriptions()}
        Entity types to consider: {self._entity_types or "Unbounded"}
        {self._get_entity_type_descriptions()}

        For each triplet:
        - head: the subject entity (text, type)
        - relation: the type of relation
        - tail: the object entity (text, type)
        """

    @override
    @property
    def _prompt_example_template(self) -> str | None:
        return None

    @override
    @property
    def _prompt_conclusion(self) -> str | None:
        return None

    @override
    @cached_property
    def prompt_signature(self) -> type[dspy_.PromptSignature]:
        _RelationTripletWithContext = self._get_dynamic_relation_triple_model()

        class RelationExtraction(dspy.Signature):
            text: str = dspy.InputField()
            triplets: list[_RelationTripletWithContext] = dspy.OutputField()  # type: ignore[valid-type]

        RelationExtraction.__doc__ = jinja2.Template(self._prompt_instructions).render()
        RelationExtraction.model_rebuild()

        return RelationExtraction

    @override
    @property
    def inference_mode(self) -> dspy_.InferenceMode:
        return self._model_settings.inference_mode or dspy_.InferenceMode.predict

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different model have different expectations as to how a prompt should look like. E.g. outlines supports the Jinja 2 templating format for insertion of values and few-shot examples, whereas DSPy integrates these things in a different value in the workflow and hence expects the prompt not to include these things. Mind model-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by model wrapper.

__init__(task_id, relations, entity_types, prompt_instructions, model_settings)

Initialize RelationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
relations list[str] | dict[str, str]

Relation types to extract.

 required
entity_types list[str] | dict[str, str] | None

Entity types to consider.

 required
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 required
model_settings ModelSettings

Model settings.

 required
Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    relations: list[str] | dict[str, str],
    entity_types: list[str] | dict[str, str] | None,
    prompt_instructions: str | None,
    model_settings: ModelSettings,
):
    """Initialize RelationExtractionBridge.

    :param task_id: Task ID.
    :param relations: Relation types to extract.
    :param entity_types: Entity types to consider.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param model_settings: Model settings.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
    )

    if isinstance(relations, dict):
        self._relations: list[str] = list(relations.keys())
        self._relation_descriptions: dict[str, str] = relations
    else:
        self._relations: list[str] = relations
        self._relation_descriptions: dict[str, str] = {}

    self._entity_types: list[str] | None = None
    self._entity_type_descriptions: dict[str, str] = {}

    if isinstance(entity_types, dict):
        self._entity_types = list(entity_types.keys())
        self._entity_type_descriptions = entity_types
    elif entity_types is not None:
        self._entity_types = entity_types
        self._entity_type_descriptions: dict[str, str] = {}

extract(docs)

Extract all values from doc instances that are to be injected into the prompts.

Parameters:

Name Type Description Default
docs Sequence[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Sequence[dict[str, Any]]

All values from doc instances that are to be injected into the prompts as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Sequence[Doc]) -> Sequence[dict[str, Any]]:
    """Extract all values from doc instances that are to be injected into the prompts.

    :param docs: Docs to extract values from.
    :return: All values from doc instances that are to be injected into the prompts as a sequence.
    """
    return [{"text": doc.text if doc.text else None} for doc in docs]

LangChainRelationExtraction

Bases: PydanticBasedRelationExtraction[InferenceMode]

LangChain bridge for relation extraction.

Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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class LangChainRelationExtraction(PydanticBasedRelationExtraction[langchain_.InferenceMode]):
    """LangChain bridge for relation extraction."""

    @override
    @property
    def inference_mode(self) -> langchain_.InferenceMode:
        return self._model_settings.inference_mode or langchain_.InferenceMode.structured

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different model have different expectations as to how a prompt should look like. E.g. outlines supports the Jinja 2 templating format for insertion of values and few-shot examples, whereas DSPy integrates these things in a different value in the workflow and hence expects the prompt not to include these things. Mind model-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by model wrapper.

__init__(task_id, relations, entity_types, prompt_instructions, model_settings)

Initialize RelationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
relations list[str] | dict[str, str]

Relation types to extract.

 required
entity_types list[str] | dict[str, str] | None

Entity types to consider.

 required
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 required
model_settings ModelSettings

Model settings.

 required
Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    relations: list[str] | dict[str, str],
    entity_types: list[str] | dict[str, str] | None,
    prompt_instructions: str | None,
    model_settings: ModelSettings,
):
    """Initialize RelationExtractionBridge.

    :param task_id: Task ID.
    :param relations: Relation types to extract.
    :param entity_types: Entity types to consider.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param model_settings: Model settings.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
    )

    if isinstance(relations, dict):
        self._relations: list[str] = list(relations.keys())
        self._relation_descriptions: dict[str, str] = relations
    else:
        self._relations: list[str] = relations
        self._relation_descriptions: dict[str, str] = {}

    self._entity_types: list[str] | None = None
    self._entity_type_descriptions: dict[str, str] = {}

    if isinstance(entity_types, dict):
        self._entity_types = list(entity_types.keys())
        self._entity_type_descriptions = entity_types
    elif entity_types is not None:
        self._entity_types = entity_types
        self._entity_type_descriptions: dict[str, str] = {}

extract(docs)

Extract all values from doc instances that are to be injected into the prompts.

Parameters:

Name Type Description Default
docs Sequence[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Sequence[dict[str, Any]]

All values from doc instances that are to be injected into the prompts as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Sequence[Doc]) -> Sequence[dict[str, Any]]:
    """Extract all values from doc instances that are to be injected into the prompts.

    :param docs: Docs to extract values from.
    :return: All values from doc instances that are to be injected into the prompts as a sequence.
    """
    return [{"text": doc.text if doc.text else None} for doc in docs]

OutlinesRelationExtraction

Bases: PydanticBasedRelationExtraction[InferenceMode]

Outlines bridge for relation extraction.

Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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class OutlinesRelationExtraction(PydanticBasedRelationExtraction[outlines_.InferenceMode]):
    """Outlines bridge for relation extraction."""

    @override
    @property
    def inference_mode(self) -> outlines_.InferenceMode:
        return self._model_settings.inference_mode or outlines_.InferenceMode.json

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different model have different expectations as to how a prompt should look like. E.g. outlines supports the Jinja 2 templating format for insertion of values and few-shot examples, whereas DSPy integrates these things in a different value in the workflow and hence expects the prompt not to include these things. Mind model-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by model wrapper.

__init__(task_id, relations, entity_types, prompt_instructions, model_settings)

Initialize RelationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
relations list[str] | dict[str, str]

Relation types to extract.

 required
entity_types list[str] | dict[str, str] | None

Entity types to consider.

 required
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 required
model_settings ModelSettings

Model settings.

 required
Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    relations: list[str] | dict[str, str],
    entity_types: list[str] | dict[str, str] | None,
    prompt_instructions: str | None,
    model_settings: ModelSettings,
):
    """Initialize RelationExtractionBridge.

    :param task_id: Task ID.
    :param relations: Relation types to extract.
    :param entity_types: Entity types to consider.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param model_settings: Model settings.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
    )

    if isinstance(relations, dict):
        self._relations: list[str] = list(relations.keys())
        self._relation_descriptions: dict[str, str] = relations
    else:
        self._relations: list[str] = relations
        self._relation_descriptions: dict[str, str] = {}

    self._entity_types: list[str] | None = None
    self._entity_type_descriptions: dict[str, str] = {}

    if isinstance(entity_types, dict):
        self._entity_types = list(entity_types.keys())
        self._entity_type_descriptions = entity_types
    elif entity_types is not None:
        self._entity_types = entity_types
        self._entity_type_descriptions: dict[str, str] = {}

extract(docs)

Extract all values from doc instances that are to be injected into the prompts.

Parameters:

Name Type Description Default
docs Sequence[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Sequence[dict[str, Any]]

All values from doc instances that are to be injected into the prompts as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Sequence[Doc]) -> Sequence[dict[str, Any]]:
    """Extract all values from doc instances that are to be injected into the prompts.

    :param docs: Docs to extract values from.
    :return: All values from doc instances that are to be injected into the prompts as a sequence.
    """
    return [{"text": doc.text if doc.text else None} for doc in docs]

PydanticBasedRelationExtraction

Bases: RelationExtractionBridge[BaseModel, BaseModel | list[Any], ModelWrapperInferenceMode], ABC

Base class for Pydantic-based relation extraction bridges.

Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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class PydanticBasedRelationExtraction(
    RelationExtractionBridge[pydantic.BaseModel, pydantic.BaseModel | list[Any], ModelWrapperInferenceMode], abc.ABC
):
    """Base class for Pydantic-based relation extraction bridges."""

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return f"""
        Extract relations between entities in the text.
        Relations: {self._relations}
        Entity Types: {self._entity_types or "Any"}
        Return a list of triplets with head, relation, and tail.
        """

    @override
    @property
    def _prompt_example_template(self) -> str | None:
        return """
        {% if examples|length > 0 -%}
        <examples>
        {%- for example in examples %}
            <example>
                <text>{{ example.text }}</text>
                <output>{{ example.triplets }}</output>
            </example>
        {% endfor -%}
        </examples>
        {% endif %}
        """

    @override
    @property
    def _prompt_conclusion(self) -> str | None:
        return """
        ========
        <text>{{ text }}</text>
        <output>
        """

    @override
    @cached_property
    def prompt_signature(self) -> type[pydantic.BaseModel]:
        _RelationTripletWithContext = self._get_dynamic_relation_triple_model()

        class RelationExtraction(pydantic.BaseModel):
            triplets: list[_RelationTripletWithContext]  # type: ignore[valid-type]

        return RelationExtraction

inference_mode abstractmethod property

Return inference mode.

Returns:

Type Description
ModelWrapperInferenceMode

Inference mode.

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different model have different expectations as to how a prompt should look like. E.g. outlines supports the Jinja 2 templating format for insertion of values and few-shot examples, whereas DSPy integrates these things in a different value in the workflow and hence expects the prompt not to include these things. Mind model-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by model wrapper.

__init__(task_id, relations, entity_types, prompt_instructions, model_settings)

Initialize RelationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
relations list[str] | dict[str, str]

Relation types to extract.

 required
entity_types list[str] | dict[str, str] | None

Entity types to consider.

 required
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 required
model_settings ModelSettings

Model settings.

 required
Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    relations: list[str] | dict[str, str],
    entity_types: list[str] | dict[str, str] | None,
    prompt_instructions: str | None,
    model_settings: ModelSettings,
):
    """Initialize RelationExtractionBridge.

    :param task_id: Task ID.
    :param relations: Relation types to extract.
    :param entity_types: Entity types to consider.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param model_settings: Model settings.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
    )

    if isinstance(relations, dict):
        self._relations: list[str] = list(relations.keys())
        self._relation_descriptions: dict[str, str] = relations
    else:
        self._relations: list[str] = relations
        self._relation_descriptions: dict[str, str] = {}

    self._entity_types: list[str] | None = None
    self._entity_type_descriptions: dict[str, str] = {}

    if isinstance(entity_types, dict):
        self._entity_types = list(entity_types.keys())
        self._entity_type_descriptions = entity_types
    elif entity_types is not None:
        self._entity_types = entity_types
        self._entity_type_descriptions: dict[str, str] = {}

extract(docs)

Extract all values from doc instances that are to be injected into the prompts.

Parameters:

Name Type Description Default
docs Sequence[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Sequence[dict[str, Any]]

All values from doc instances that are to be injected into the prompts as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Sequence[Doc]) -> Sequence[dict[str, Any]]:
    """Extract all values from doc instances that are to be injected into the prompts.

    :param docs: Docs to extract values from.
    :return: All values from doc instances that are to be injected into the prompts as a sequence.
    """
    return [{"text": doc.text if doc.text else None} for doc in docs]

RelationExtractionBridge

Bases: Bridge[_BridgePromptSignature, _BridgeResult, ModelWrapperInferenceMode], ABC

Abstract base class for relation extraction bridges.

Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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class RelationExtractionBridge(Bridge[_BridgePromptSignature, _BridgeResult, ModelWrapperInferenceMode], abc.ABC):
    """Abstract base class for relation extraction bridges."""

    def __init__(
        self,
        task_id: str,
        relations: list[str] | dict[str, str],
        entity_types: list[str] | dict[str, str] | None,
        prompt_instructions: str | None,
        model_settings: ModelSettings,
    ):
        """Initialize RelationExtractionBridge.

        :param task_id: Task ID.
        :param relations: Relation types to extract.
        :param entity_types: Entity types to consider.
        :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
        :param model_settings: Model settings.
        """
        super().__init__(
            task_id=task_id,
            prompt_instructions=prompt_instructions,
            overwrite=False,
            model_settings=model_settings,
        )

        if isinstance(relations, dict):
            self._relations: list[str] = list(relations.keys())
            self._relation_descriptions: dict[str, str] = relations
        else:
            self._relations: list[str] = relations
            self._relation_descriptions: dict[str, str] = {}

        self._entity_types: list[str] | None = None
        self._entity_type_descriptions: dict[str, str] = {}

        if isinstance(entity_types, dict):
            self._entity_types = list(entity_types.keys())
            self._entity_type_descriptions = entity_types
        elif entity_types is not None:
            self._entity_types = entity_types
            self._entity_type_descriptions: dict[str, str] = {}

    def _get_relation_descriptions(self) -> str:
        """Return relation descriptions as a string.

        :return: Relation descriptions.
        """
        descs: list[str] = []
        for rel in self._relations:
            if rel in self._relation_descriptions:
                descs.append(
                    f"<relation_description><relation>{rel}</relation><description>"
                    f"{self._relation_descriptions[rel]}</description></relation_description>"
                )
            else:
                descs.append(rel)
        return "\n\t\t\t".join(descs)

    def _get_entity_type_descriptions(self) -> str:
        """Return entity type descriptions as a string.

        :return: Entity type descriptions.
        """
        if self._entity_types is None:
            return "Unbounded"

        descs: list[str] = []
        for et in self._entity_types:
            if et in self._entity_type_descriptions:
                descs.append(
                    f"<entity_type_description><type>{et}</type><description>"
                    f"{self._entity_type_descriptions[et]}</description></entity_type_description>"
                )
            else:
                descs.append(et)

        return "\n\t\t\t".join(descs)

    def _get_dynamic_relation_triple_model(self) -> type[pydantic.BaseModel]:
        """Create dynamic model for triplets with strict type constraints.

        :return: Triplet model.
        """
        AllowedEntityType = Literal[*self._entity_types] if self._entity_types else str  # type: ignore[valid-type]
        AllowedRelationType = Literal[*self._relations] if self._relations else str  # type: ignore[valid-type]

        class _RelationEntityWithContext(pydantic.BaseModel):
            text: str
            context: str | None = None
            entity_type: AllowedEntityType

        class _RelationTripletWithContext(pydantic.BaseModel):
            head: _RelationEntityWithContext
            relation: AllowedRelationType
            tail: _RelationEntityWithContext

        _RelationEntityWithContext.__doc__ = RelationEntityWithContext.__doc__
        _RelationTripletWithContext.__doc__ = RelationTripletWithContext.__doc__

        return _RelationTripletWithContext

    def _process_triplets(self, raw_triplets: list[Any]) -> list[RelationTriplet]:
        """Convert raw triplets from model to RelationTriplet objects.

        :param raw_triplets: Raw triplets from the model.
        :return: Processed RelationTriplet objects.
        """
        processed: list[RelationTriplet] = []
        for raw in raw_triplets:
            head_text = getattr(raw.head, "text", "")
            head_type = getattr(raw.head, "entity_type", "")

            tail_text = getattr(raw.tail, "text", "")
            tail_type = getattr(raw.tail, "entity_type", "")

            processed.append(
                RelationTriplet(
                    head=RelationEntity(text=head_text, entity_type=head_type),
                    relation=getattr(raw, "relation", ""),
                    tail=RelationEntity(text=tail_text, entity_type=tail_type),
                )
            )
        return processed

    @override
    def integrate(self, results: Sequence[_BridgeResult | list[Any]], docs: list[Doc]) -> list[Doc]:
        for doc, result in zip(docs, results):
            # Handle both model result objects and raw lists from consolidation.
            raw_triplets = result if isinstance(result, list) else getattr(result, "triplets", [])
            doc.results[self._task_id] = Result(triplets=self._process_triplets(raw_triplets))
        return docs

    @override
    def consolidate(
        self,
        results: Sequence[_BridgeResult],
        docs_offsets: list[tuple[int, int]],
    ) -> Sequence[list[Any]]:
        consolidated: list[list[Any]] = []

        for start, end in docs_offsets:
            doc_results = results[start:end]
            all_triplets: list[Any] = []
            seen: set[tuple[str, str, str]] = set()

            for res in doc_results:
                if res and hasattr(res, "triplets"):
                    for triplet in res.triplets:
                        # Use a simple key for deduplication within the bridge's internal format.
                        key = (getattr(triplet.head, "text", ""), triplet.relation, getattr(triplet.tail, "text", ""))
                        if key not in seen:
                            all_triplets.append(triplet)
                            seen.add(key)

            consolidated.append(all_triplets)

        return consolidated

inference_mode abstractmethod property

Return inference mode.

Returns:

Type Description
ModelWrapperInferenceMode

Inference mode.

prompt_signature abstractmethod property

Create output signature.

E.g.: Signature in DSPy, Pydantic objects in outlines, JSON schema in jsonformers. This is model type-specific.

Returns:

Type Description
type[TaskPromptSignature] | TaskPromptSignature

Output signature object. This can be an instance (e.g. a regex string) or a class (e.g. a Pydantic class).

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different model have different expectations as to how a prompt should look like. E.g. outlines supports the Jinja 2 templating format for insertion of values and few-shot examples, whereas DSPy integrates these things in a different value in the workflow and hence expects the prompt not to include these things. Mind model-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by model wrapper.

__init__(task_id, relations, entity_types, prompt_instructions, model_settings)

Initialize RelationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
relations list[str] | dict[str, str]

Relation types to extract.

 required
entity_types list[str] | dict[str, str] | None

Entity types to consider.

 required
prompt_instructions str | None

Custom prompt instructions. If None, default instructions are used.

 required
model_settings ModelSettings

Model settings.

 required
Source code in sieves/tasks/predictive/relation_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    relations: list[str] | dict[str, str],
    entity_types: list[str] | dict[str, str] | None,
    prompt_instructions: str | None,
    model_settings: ModelSettings,
):
    """Initialize RelationExtractionBridge.

    :param task_id: Task ID.
    :param relations: Relation types to extract.
    :param entity_types: Entity types to consider.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param model_settings: Model settings.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
    )

    if isinstance(relations, dict):
        self._relations: list[str] = list(relations.keys())
        self._relation_descriptions: dict[str, str] = relations
    else:
        self._relations: list[str] = relations
        self._relation_descriptions: dict[str, str] = {}

    self._entity_types: list[str] | None = None
    self._entity_type_descriptions: dict[str, str] = {}

    if isinstance(entity_types, dict):
        self._entity_types = list(entity_types.keys())
        self._entity_type_descriptions = entity_types
    elif entity_types is not None:
        self._entity_types = entity_types
        self._entity_type_descriptions: dict[str, str] = {}

extract(docs)

Extract all values from doc instances that are to be injected into the prompts.

Parameters:

Name Type Description Default
docs Sequence[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Sequence[dict[str, Any]]

All values from doc instances that are to be injected into the prompts as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Sequence[Doc]) -> Sequence[dict[str, Any]]:
    """Extract all values from doc instances that are to be injected into the prompts.

    :param docs: Docs to extract values from.
    :return: All values from doc instances that are to be injected into the prompts as a sequence.
    """
    return [{"text": doc.text if doc.text else None} for doc in docs]

Schemas for relation extraction task.

FewshotExample

Bases: FewshotExample

Few-shot example for relation extraction.

Attributes: text: Input text. triplets: Expected relation triplets.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class FewshotExample(BaseFewshotExample):
    """Few-shot example for relation extraction.

    Attributes:
        text: Input text.
        triplets: Expected relation triplets.
    """

    triplets: list[RelationTriplet]

    @property
    def target_fields(self) -> tuple[str, ...]:
        """Return target fields.

        :return: Target fields.
        """
        return ("triplets",)

input_fields property

Defines which fields are inputs.

Returns:

Type Description
Sequence[str]

Sequence of field names.

target_fields property

Return target fields.

Returns:

Type Description
tuple[str, ...]

Target fields.

from_dspy(example) classmethod

Convert from dspy.Example.

Parameters:

Name Type Description Default
example Example

Example as dspy.Example.

 required

Returns:

Type Description
Self

Example as FewshotExample.
Source code in sieves/tasks/predictive/schemas/core.py 
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@classmethod
def from_dspy(cls, example: dspy.Example) -> Self:
    """Convert from `dspy.Example`.

    :param example: Example as `dspy.Example`.
    :returns: Example as `FewshotExample`.
    """
    return cls(**example)

to_dspy()

Convert to dspy.Example.

Returns:

Type Description
Example

Example as dspy.Example.
Source code in sieves/tasks/predictive/schemas/core.py 
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def to_dspy(self) -> dspy.Example:
    """Convert to `dspy.Example`.

    :returns: Example as `dspy.Example`.
    """
    return dspy.Example(**ModelWrapper.convert_fewshot_examples([self])[0]).with_inputs(*self.input_fields)

RelationEntity

Bases: BaseModel

Entity involved in a relation.

Attributes: text: Surface text of the entity. entity_type: Type of the entity.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class RelationEntity(pydantic.BaseModel, frozen=True):
    """Entity involved in a relation.

    Attributes:
        text: Surface text of the entity.
        entity_type: Type of the entity.
    """

    text: str
    entity_type: str

RelationEntityWithContext

Bases: BaseModel

Entity mention with text span, type, and context for span discovery.

Attributes: text: Surface text of the entity. context: Short context around the entity. entity_type: Type of the entity.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class RelationEntityWithContext(pydantic.BaseModel):
    """Entity mention with text span, type, and context for span discovery.

    Attributes:
        text: Surface text of the entity.
        context: Short context around the entity.
        entity_type: Type of the entity.
    """

    text: str
    context: str
    entity_type: str

RelationTriplet

Bases: BaseModel

Triplet representing a relation between two entities.

Attributes: head: The subject entity. relation: The type of relation. tail: The object entity.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class RelationTriplet(pydantic.BaseModel, frozen=True):
    """Triplet representing a relation between two entities.

    Attributes:
        head: The subject entity.
        relation: The type of relation.
        tail: The object entity.
    """

    head: RelationEntity
    relation: str
    tail: RelationEntity

RelationTripletWithContext

Bases: BaseModel

Triplet with context for span discovery.

Attributes: head: The head entity with context. relation: The relation type. tail: The tail entity with context.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class RelationTripletWithContext(pydantic.BaseModel):
    """Triplet with context for span discovery.

    Attributes:
        head: The head entity with context.
        relation: The relation type.
        tail: The tail entity with context.
    """

    head: RelationEntityWithContext
    relation: str
    tail: RelationEntityWithContext

Result

Bases: BaseModel

Result of a relation extraction task.

Attributes: triplets: List of extracted relation triplets.

Source code in sieves/tasks/predictive/schemas/relation_extraction.py 
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class Result(pydantic.BaseModel):
    """Result of a relation extraction task.

    Attributes:
        triplets: List of extracted relation triplets.
    """

    triplets: list[RelationTriplet]