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Information Extraction

Information extraction.

FewshotExample

Bases: FewshotExample

Few-shot example.

Source code in sieves/tasks/predictive/information_extraction/core.py 
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class FewshotExample(BaseFewshotExample):
    """Few-shot example."""

    reasoning: str
    entities: list[pydantic.BaseModel]

    @override
    @property
    def target_fields(self) -> Sequence[str]:
        return ("entities",)

input_fields property

Defines which fields are inputs.

Returns:

Type Description
Sequence[str]

Sequence of field names.

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/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/core.py 
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def to_dspy(self) -> dspy.Example:
    """Convert to `dspy.Example`.

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

InformationExtraction

Bases: PredictiveTask[_TaskPromptSignature, _TaskResult, _TaskBridge]

Information extraction task.

Source code in sieves/tasks/predictive/information_extraction/core.py 
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class InformationExtraction(PredictiveTask[_TaskPromptSignature, _TaskResult, _TaskBridge]):
    """Information extraction task."""

    def __init__(
        self,
        entity_type: type[pydantic.BaseModel],
        model: _TaskModel,
        task_id: str | None = None,
        include_meta: bool = True,
        batch_size: int = -1,
        prompt_instructions: str | None = None,
        fewshot_examples: Sequence[FewshotExample] = (),
        generation_settings: GenerationSettings = GenerationSettings(),
    ) -> None:
        """Initialize new PredictiveTask.

        :param entity_type: Object type to extract.
        :param model: Model to use.
        :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 generation_settings: Settings for structured generation.
        """
        self._entity_type = entity_type

        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,
            generation_settings=generation_settings,
        )

        if not self._entity_type.model_config.get("frozen", False):
            warnings.warn(
                f"Entity type provided to task {self._task_id} isn't frozen, which means that entities can't "
                f"be deduplicated. Modify entity_type to be frozen=True."
            )

    @override
    def _init_bridge(self, engine_type: EngineType) -> _TaskBridge:
        """Initialize bridge.

        :param engine_type: Type of engine to initialize bridge for.
        :return _TaskBridge: Engine task bridge.
        :raises ValueError: If engine type is not supported.
        """
        bridge_types: dict[EngineType, type[_TaskBridge]] = {
            EngineType.dspy: DSPyInformationExtraction,
            EngineType.langchain: LangChainInformationExtraction,
            EngineType.outlines: OutlinesInformationExtraction,
        }

        try:
            bridge = bridge_types[engine_type](
                task_id=self._task_id,
                prompt_instructions=self._custom_prompt_instructions,
                entity_type=self._entity_type,
            )
        except KeyError as err:
            raise KeyError(f"Engine type {engine_type} is not supported by {self.__class__.__name__}.") from err

        return bridge

    @override
    @property
    def supports(self) -> set[EngineType]:
        return {
            EngineType.dspy,
            EngineType.langchain,
            EngineType.outlines,
        }

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

    @override
    def to_hf_dataset(self, docs: Iterable[Doc], threshold: float = 0.5) -> datasets.Dataset:
        # Define metadata.
        features = datasets.Features(
            {
                "text": datasets.Value("string"),
                "entities": datasets.Sequence(PydanticToHFDatasets.model_cls_to_features(self._entity_type)),
            }
        )
        info = datasets.DatasetInfo(
            description=f"Information extraction dataset for entity type {self._entity_type.__class__.__name__}. "
            f"Generated with sieves v{Config.get_version()}.",
            features=features,
        )

        # Fetch data used for generating dataset.
        try:
            data = [
                (doc.text, [PydanticToHFDatasets.model_to_dict(res) for res in doc.results[self._task_id]])
                for doc in docs
            ]
        except KeyError as err:
            raise KeyError(f"Not all documents have results for this task with ID {self._task_id}") from err

        def generate_data() -> Iterable[dict[str, Any]]:
            """Yield results as dicts.

            :return: Results as dicts.
            """
            for text, entities in data:
                yield {"text": text, "entities": entities}

        # Create dataset.
        return datasets.Dataset.from_generator(generate_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

    @override
    def _evaluate_optimization_example(
        self, truth: dspy.Example, pred: dspy.Prediction, model: dspy.LM, trace: Any | None = None
    ) -> float:
        def entity_to_tuple(entity: dict) -> tuple:
            """Convert entity dict to hashable tuple for comparison.

            Converts nested structures (lists, dicts) to strings to make them hashable.

            :param entity: Entity dictionary to convert.
            :return: Hashable tuple representation of the entity.
            """
            items = sorted(entity.items())
            return tuple((k, v if not (isinstance(v, list) or isinstance(v, dict)) else str(v)) for k, v in items)

        # Compute set-based F1 score for entity extraction
        true_entities = {entity_to_tuple(e) for e in truth["entities"]}
        pred_entities = {entity_to_tuple(e) for e in pred.get("entities", [])}

        if not true_entities:
            return 1.0 if not pred_entities else 0.0

        precision = len(true_entities & pred_entities) / len(pred_entities) if pred_entities else 0
        recall = len(true_entities & pred_entities) / len(true_entities)
        return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

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 the task on a set of documents.

Parameters:

Name Type Description Default
docs Iterable[Doc]

Documents to process.

 required

Returns:

Type Description
Iterable[Doc]

Processed documents.
Source code in sieves/tasks/predictive/core.py 
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def __call__(self, docs: Iterable[Doc]) -> Iterable[Doc]:
    """Execute the task on a set of documents.

    :param docs: Documents to process.
    :return Iterable[Doc]: Processed documents.
    """
    # 1. Compile expected prompt signatures.
    signature = self._bridge.prompt_signature

    # 2. Build executable.
    executable = self._engine.build_executable(
        inference_mode=self._bridge.inference_mode,
        prompt_template=self.prompt_template,
        prompt_signature=signature,
        fewshot_examples=self._fewshot_examples,
    )

    # Compute batch-wise results.
    batch_size = self._batch_size if self._batch_size > 0 else sys.maxsize
    while docs_batch := [doc for doc in itertools.islice(docs, batch_size)]:
        if len(docs_batch) == 0:
            break

        # 3. Extract values from docs to inject/render those into prompt templates.
        docs_values = list(self._bridge.extract(docs_batch))
        assert len(docs_values) == len(docs_batch)

        # 4. Map extracted docs values onto chunks.
        docs_chunks_offsets: list[tuple[int, int]] = []
        docs_chunks: list[dict[str, Any]] = []
        for doc, doc_values in zip(docs_batch, docs_values):
            assert doc.text
            doc_chunks_values = [doc_values | {"text": chunk} for chunk in (doc.chunks or [doc.text])]
            docs_chunks_offsets.append((len(docs_chunks), len(docs_chunks) + len(doc_chunks_values)))
            docs_chunks.extend(doc_chunks_values)

        # 5. Execute prompts per chunk.
        results = list(executable(docs_chunks))
        assert len(results) == len(docs_chunks)

        # 6. Consolidate chunk results.
        results = list(self._bridge.consolidate(results, docs_chunks_offsets))
        assert len(results) == len(docs_batch)

        # 7. Integrate results into docs.
        docs_batch = self._bridge.integrate(results, docs_batch)

        yield from docs_batch

__init__(entity_type, model, task_id=None, include_meta=True, batch_size=-1, prompt_instructions=None, fewshot_examples=(), generation_settings=GenerationSettings())

Initialize new PredictiveTask.

Parameters:

Name Type Description Default
entity_type type[BaseModel]

Object type to extract.

 required
model _TaskModel

Model to use.

 required
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.

 ()
generation_settings GenerationSettings

Settings for structured generation.

 GenerationSettings()
Source code in sieves/tasks/predictive/information_extraction/core.py 
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def __init__(
    self,
    entity_type: type[pydantic.BaseModel],
    model: _TaskModel,
    task_id: str | None = None,
    include_meta: bool = True,
    batch_size: int = -1,
    prompt_instructions: str | None = None,
    fewshot_examples: Sequence[FewshotExample] = (),
    generation_settings: GenerationSettings = GenerationSettings(),
) -> None:
    """Initialize new PredictiveTask.

    :param entity_type: Object type to extract.
    :param model: Model to use.
    :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 generation_settings: Settings for structured generation.
    """
    self._entity_type = entity_type

    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,
        generation_settings=generation_settings,
    )

    if not self._entity_type.model_config.get("frozen", False):
        warnings.warn(
            f"Entity type provided to task {self._task_id} isn't frozen, which means that entities can't "
            f"be deduplicated. Modify entity_type to be frozen=True."
        )

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["generation_settings"] = GenerationSettings.model_validate(init_dict["generation_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]
    pred_eval = functools.partial(self._evaluate_optimization_example, 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(EngineType.get_engine_type(self._engine))

    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 information extraction task.

DSPyInformationExtraction

Bases: InformationExtractionBridge[PromptSignature, Result, InferenceMode]

DSPy bridge for information extraction.

Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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class DSPyInformationExtraction(InformationExtractionBridge[dspy_.PromptSignature, dspy_.Result, dspy_.InferenceMode]):
    """DSPy bridge for information extraction."""

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return "Find all occurences of this kind of entitity within the text."

    @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]:
        extraction_type = self._entity_type

        class Entities(dspy.Signature):  # type: ignore[misc]
            text: str = dspy.InputField(description="Text to extract entities from.")
            entities: list[extraction_type] = dspy.OutputField(description="Entities to extract from text.")  # type: ignore[valid-type]

        Entities.__doc__ = jinja2.Template(self._prompt_instructions).render()

        return Entities

    @override
    @property
    def inference_mode(self) -> dspy_.InferenceMode:
        return dspy_.InferenceMode.chain_of_thought

    @override
    def integrate(self, results: Iterable[dspy_.Result], docs: Iterable[Doc]) -> Iterable[Doc]:
        for doc, result in zip(docs, results):
            assert len(result.completions.entities) == 1
            doc.results[self._task_id] = result.completions.entities[0]
        return docs

    @override
    def consolidate(
        self, results: Iterable[dspy_.Result], docs_offsets: list[tuple[int, int]]
    ) -> Iterable[dspy_.Result]:
        results = list(results)
        entity_type = self._entity_type
        entity_type_is_frozen = entity_type.model_config.get("frozen", False)

        # Merge all found entities.
        for doc_offset in docs_offsets:
            reasonings: list[str] = []
            entities: list[entity_type] = []  # type: ignore[valid-type]
            seen_entities: set[entity_type] = set()  # type: ignore[valid-type]

            for res in results[doc_offset[0] : doc_offset[1]]:
                if res is None:
                    continue
                reasonings.append(res.reasoning)
                assert len(res.completions.entities) == 1
                if entity_type_is_frozen:
                    # Ensure not to add duplicate entities.
                    for entity in res.completions.entities[0]:
                        if entity not in seen_entities:
                            entities.append(entity)
                            seen_entities.add(entity)
                else:
                    entities.extend(res.completions.entities[0])

            yield dspy.Prediction.from_completions(
                {"entities": [entities], "reasoning": [str(reasonings)]},
                signature=self.prompt_signature,
            )

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different engines have different expectations as 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 engine-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by engine.

__init__(task_id, prompt_instructions, entity_type)

Initialize InformationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
prompt_instructions str | None

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

 required
entity_type type[BaseModel]

Type to extract.

 required
Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    entity_type: type[pydantic.BaseModel],
):
    """Initialize InformationExtractionBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param entity_type: Type to extract.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._entity_type = entity_type

extract(docs)

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

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Iterable[dict[str, Any]]

All values from doc instances that are to be injected into the prompts
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Iterable[Doc]) -> Iterable[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 Iterable[dict[str, Any]]: All values from doc instances that are to be injected into the prompts
    """
    return ({"text": doc.text if doc.text else None} for doc in docs)

InformationExtractionBridge

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

Abstract base class for information extraction bridges.

Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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class InformationExtractionBridge(
    Bridge[_BridgePromptSignature, _BridgeResult, EngineInferenceMode],
    abc.ABC,
):
    """Abstract base class for information extraction bridges."""

    def __init__(
        self,
        task_id: str,
        prompt_instructions: str | None,
        entity_type: type[pydantic.BaseModel],
    ):
        """Initialize InformationExtractionBridge.

        :param task_id: Task ID.
        :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
        :param entity_type: Type to extract.
        """
        super().__init__(
            task_id=task_id,
            prompt_instructions=prompt_instructions,
            overwrite=False,
        )
        self._entity_type = entity_type

inference_mode abstractmethod property

Return inference mode.

Returns:

Type Description
EngineInferenceMode

Inference mode.

prompt_signature abstractmethod property

Create output signature.

E.g.: Signature in DSPy, Pydantic objects in outlines, JSON schema in jsonformers. This is engine-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 engines have different expectations as 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 engine-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by engine.

__init__(task_id, prompt_instructions, entity_type)

Initialize InformationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
prompt_instructions str | None

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

 required
entity_type type[BaseModel]

Type to extract.

 required
Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    entity_type: type[pydantic.BaseModel],
):
    """Initialize InformationExtractionBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param entity_type: Type to extract.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._entity_type = entity_type

consolidate(results, docs_offsets) abstractmethod

Consolidate results for document chunks into document results.

Parameters:

Name Type Description Default
results Iterable[TaskResult]

Results per document chunk.

 required
docs_offsets list[tuple[int, int]]

Chunk offsets per document. Chunks per document can be obtained with results[docs_chunk_offsets[i][0]:docs_chunk_offsets[i][1]].

 required

Returns:

Type Description
Iterable[TaskResult]

Results per document.
Source code in sieves/tasks/predictive/bridges.py 
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@abc.abstractmethod
def consolidate(self, results: Iterable[TaskResult], docs_offsets: list[tuple[int, int]]) -> Iterable[TaskResult]:
    """Consolidate results for document chunks into document results.

    :param results: Results per document chunk.
    :param docs_offsets: Chunk offsets per document. Chunks per document can be obtained with
        `results[docs_chunk_offsets[i][0]:docs_chunk_offsets[i][1]]`.
    :return Iterable[_TaskResult]: Results per document.
    """

extract(docs)

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

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Iterable[dict[str, Any]]

All values from doc instances that are to be injected into the prompts
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Iterable[Doc]) -> Iterable[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 Iterable[dict[str, Any]]: All values from doc instances that are to be injected into the prompts
    """
    return ({"text": doc.text if doc.text else None} for doc in docs)

integrate(results, docs) abstractmethod

Integrate results into Doc instances.

Parameters:

Name Type Description Default
results Iterable[TaskResult]

Results from prompt executable.

 required
docs Iterable[Doc]

Doc instances to update.

 required

Returns:

Type Description
Iterable[Doc]

Updated doc instances.
Source code in sieves/tasks/predictive/bridges.py 
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@abc.abstractmethod
def integrate(self, results: Iterable[TaskResult], docs: Iterable[Doc]) -> Iterable[Doc]:
    """Integrate results into Doc instances.

    :param results: Results from prompt executable.
    :param docs: Doc instances to update.
    :return Iterable[Doc]: Updated doc instances.
    """

LangChainInformationExtraction

Bases: PydanticBasedInformationExtraction[InferenceMode]

LangChain bridge for information extraction.

Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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class LangChainInformationExtraction(PydanticBasedInformationExtraction[langchain_.InferenceMode]):
    """LangChain bridge for information extraction."""

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

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different engines have different expectations as 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 engine-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by engine.

__init__(task_id, prompt_instructions, entity_type)

Initialize InformationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
prompt_instructions str | None

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

 required
entity_type type[BaseModel]

Type to extract.

 required
Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    entity_type: type[pydantic.BaseModel],
):
    """Initialize InformationExtractionBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param entity_type: Type to extract.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._entity_type = entity_type

extract(docs)

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

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Iterable[dict[str, Any]]

All values from doc instances that are to be injected into the prompts
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Iterable[Doc]) -> Iterable[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 Iterable[dict[str, Any]]: All values from doc instances that are to be injected into the prompts
    """
    return ({"text": doc.text if doc.text else None} for doc in docs)

OutlinesInformationExtraction

Bases: PydanticBasedInformationExtraction[InferenceMode]

Outlines bridge for information extraction.

Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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class OutlinesInformationExtraction(PydanticBasedInformationExtraction[outlines_.InferenceMode]):
    """Outlines bridge for information extraction."""

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

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different engines have different expectations as 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 engine-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by engine.

__init__(task_id, prompt_instructions, entity_type)

Initialize InformationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
prompt_instructions str | None

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

 required
entity_type type[BaseModel]

Type to extract.

 required
Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    entity_type: type[pydantic.BaseModel],
):
    """Initialize InformationExtractionBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param entity_type: Type to extract.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._entity_type = entity_type

extract(docs)

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

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Iterable[dict[str, Any]]

All values from doc instances that are to be injected into the prompts
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Iterable[Doc]) -> Iterable[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 Iterable[dict[str, Any]]: All values from doc instances that are to be injected into the prompts
    """
    return ({"text": doc.text if doc.text else None} for doc in docs)

PydanticBasedInformationExtraction

Bases: InformationExtractionBridge[BaseModel, BaseModel, EngineInferenceMode], ABC

Base class for Pydantic-based information extraction bridges.

Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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class PydanticBasedInformationExtraction(
    InformationExtractionBridge[pydantic.BaseModel, pydantic.BaseModel, EngineInferenceMode],
    abc.ABC,
):
    """Base class for Pydantic-based information extraction bridges."""

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return """
        Find all occurences of this kind of entitity within the text. Keep your reasoning concise - don't
        exhaustively list all identified entities in your reasoning.
        """

    @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>
                        <reasoning>{{ example.reasoning }}</reasoning>
                        <entities>{{ example.entities }}</entities>
                    </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]:
        entity_type = self._entity_type

        class Entity(pydantic.BaseModel, frozen=True):
            """Entity to extract from text."""

            reasoning: str
            entities: list[entity_type]  # type: ignore[valid-type]

        return Entity

    @override
    def integrate(self, results: Iterable[pydantic.BaseModel], docs: Iterable[Doc]) -> Iterable[Doc]:
        for doc, result in zip(docs, results):
            assert hasattr(result, "entities")
            doc.results[self._task_id] = result.entities
        return docs

    @override
    def consolidate(
        self, results: Iterable[pydantic.BaseModel], docs_offsets: list[tuple[int, int]]
    ) -> Iterable[pydantic.BaseModel]:
        results = list(results)
        entity_type = self._entity_type
        entity_type_is_frozen = entity_type.model_config.get("frozen", False)

        # Determine label scores for chunks per document.
        for doc_offset in docs_offsets:
            reasonings: list[str] = []
            entities: list[entity_type] = []  # type: ignore[valid-type]
            seen_entities: set[entity_type] = set()  # type: ignore[valid-type]

            for res in results[doc_offset[0] : doc_offset[1]]:
                if res is None:
                    continue  # type: ignore[unreachable]

                assert hasattr(res, "reasoning")
                reasonings.append(res.reasoning)

                assert hasattr(res, "entities")
                if entity_type_is_frozen:
                    # Ensure not to add duplicate entities.
                    for entity in res.entities:
                        if entity not in seen_entities:
                            entities.append(entity)
                            seen_entities.add(entity)
                else:
                    entities.extend(res.entities)

            yield self.prompt_signature(entities=entities, reasoning=str(reasonings))

inference_mode abstractmethod property

Return inference mode.

Returns:

Type Description
EngineInferenceMode

Inference mode.

prompt_template property

Return prompt template.

Chains _prompt_instructions, _prompt_example_template and _prompt_conclusion.

Note: different engines have different expectations as 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 engine-specific expectations when creating a prompt template.

Returns:

Type Description
str

Prompt template as string. None if not used by engine.

__init__(task_id, prompt_instructions, entity_type)

Initialize InformationExtractionBridge.

Parameters:

Name Type Description Default
task_id str

Task ID.

 required
prompt_instructions str | None

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

 required
entity_type type[BaseModel]

Type to extract.

 required
Source code in sieves/tasks/predictive/information_extraction/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    entity_type: type[pydantic.BaseModel],
):
    """Initialize InformationExtractionBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param entity_type: Type to extract.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._entity_type = entity_type

extract(docs)

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

Parameters:

Name Type Description Default
docs Iterable[Doc]

Docs to extract values from.

 required

Returns:

Type Description
Iterable[dict[str, Any]]

All values from doc instances that are to be injected into the prompts
Source code in sieves/tasks/predictive/bridges.py 
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def extract(self, docs: Iterable[Doc]) -> Iterable[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 Iterable[dict[str, Any]]: All values from doc instances that are to be injected into the prompts
    """
    return ({"text": doc.text if doc.text else None} for doc in docs)