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Sentiment Analysis

The SentimentAnalysis task determines the sentiment of the text (e.g., positive, negative, neutral).

Usage

from sieves import tasks

task = tasks.SentimentAnalysis(
    model=model,
)

Results

The SentimentAnalysis task returns a unified Result object containing a sentiment_per_aspect dictionary and an overall confidence score.

Confidence scores are self-reported by LLMs and may be None.

Evaluation

Performance of sentiment analysis can be measured using the .evaluate() method.

  • Metric: Macro-averaged F1 Score (F1 (Macro)). This is calculated per aspect across the corpus and then averaged. Continuous sentiment scores (0.0-1.0) are discretized (0 vs 1) for F1 calculation.
  • Requirement: Each document must have ground-truth sentiment scores stored in doc.gold[task_id].
report = task.evaluate(docs)
print(f"Sentiment Score: {report.metrics['F1 (Macro)']}")

Ground Truth Formats

Ground truth has to be specified in doc.meta using Result instances.

Aspect-based sentiment analysis predictive task.

SentimentAnalysis

Bases: PredictiveTask[TaskPromptSignature, TaskResult, _TaskBridge]

Estimate per‑aspect and overall sentiment for a document.

Source code in sieves/tasks/predictive/sentiment_analysis/core.py 
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class SentimentAnalysis(PredictiveTask[TaskPromptSignature, TaskResult, _TaskBridge]):
    """Estimate per‑aspect and overall sentiment for a document."""

    THRESHOLD: float = 0.5

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

        :param model: Model to use.
        :param model_settings: Settings for structured generation.
        :param aspects: Aspects to consider in sentiment analysis. Overall sentiment will always be determined. If
            empty, only overall sentiment will be determined.
        :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 condition: Optional callable that determines whether to process each document.
        """
        self._aspects = tuple(sorted(set(aspects) | {"overall"}))
        super().__init__(
            model=model,
            model_settings=model_settings,
            task_id=task_id,
            include_meta=include_meta,
            batch_size=batch_size,
            overwrite=False,
            prompt_instructions=prompt_instructions,
            fewshot_examples=fewshot_examples,
            condition=condition,
        )

    @property
    @override
    def fewshot_example_type(self) -> type[FewshotExample]:
        """Return few-shot example type.

        :return: Few-shot example type.
        """
        return FewshotExample

    @property
    @override
    def prompt_signature(self) -> type[pydantic.BaseModel]:
        """Return the unified Pydantic prompt signature for this task.

        :return: Unified Pydantic prompt signature.
        """
        fields = {
            aspect: (
                float,
                pydantic.Field(
                    ...,
                    description=f"Sentiment score for the '{aspect}' aspect, ranging from 0 (Negative) to 1 "
                    f"(Positive).",
                    ge=0,
                    le=1,
                ),
            )
            for aspect in self._aspects
        }
        fields["score"] = (
            float | None,
            pydantic.Field(
                default=None,
                description="Provide an overall confidence score for the sentiment analysis, ranging from 0 to 1.",
            ),
        )

        return pydantic.create_model(
            "SentimentAnalysisOutput",
            __base__=pydantic.BaseModel,
            __doc__="Result of aspect-based sentiment analysis.",
            **fields,
        )  # type: ignore[no-matching-overload]

    @property
    @override
    def metric(self) -> str:
        return "F1 (Macro)"

    @override
    def _compute_metrics(self, truths: list[Any], preds: list[Any], judge: dspy.LM | None = None) -> dict[str, float]:
        """Compute corpus-level metrics.

        Computes F1 per aspect, then averages over aspects. Sentiments are discretized to 0 (Negative) or 1 (Positive)
        for F1.

        :param truths: List of ground truths.
        :param preds: List of predictions.
        :param judge: Optional DSPy LM instance to use as judge for generative tasks.
        :return: Dictionary of metrics.
        """
        aspects_list = list(self._aspects)
        # We will compute F1 per aspect, then average.
        # Sentiments are 0.0 to 1.0. We discretize them to 0 (Negative) or 1 (Positive) for F1.

        scores: list[float] = []

        for aspect in aspects_list:
            y_true: list[int] = []
            y_pred: list[int] = []

            for gold, pred in zip(truths, preds):
                if gold is not None:
                    assert isinstance(gold, TaskResult)
                    gold_val = gold.sentiment_per_aspect.get(aspect, 0.0)
                    y_true.append(1 if gold_val >= self.THRESHOLD else 0)
                else:
                    y_true.append(-1)

                if pred is not None:
                    assert isinstance(pred, TaskResult)
                    pred_val = pred.sentiment_per_aspect.get(aspect, 0.0)
                    y_pred.append(1 if pred_val >= self.THRESHOLD else 0)
                else:
                    y_pred.append(-1)

            # Compute Macro F1 for this aspect.
            if y_true:
                scores.append(sklearn.metrics.f1_score(y_true, y_pred, average="macro", zero_division=0))

        avg_score = sum(scores) / len(scores) if scores else 0.0

        return {self.metric: avg_score}

    @override
    def _init_bridge(self, model_type: ModelType) -> _TaskBridge:
        bridge_types: dict[ModelType, type[DSPySentimentAnalysis | PydanticSentimentAnalysis]] = {
            ModelType.dspy: DSPySentimentAnalysis,
            ModelType.outlines: PydanticSentimentAnalysis,
            ModelType.langchain: PydanticSentimentAnalysis,
        }

        try:
            bridge_type = bridge_types[model_type]

            return bridge_type(
                task_id=self._task_id,
                prompt_instructions=self._custom_prompt_instructions,
                aspects=self._aspects,
                model_settings=self._model_settings,
                prompt_signature=self.prompt_signature,
                model_type=model_type,
                fewshot_examples=self._fewshot_examples,
            )
        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.langchain,
            ModelType.outlines,
        }

    @override
    def _validate_fewshot_examples(self) -> None:
        for fs_example in self._fewshot_examples:
            assert isinstance(fs_example, FewshotExample)
            if any([aspect not in self._aspects for aspect in fs_example.sentiment_per_aspect]) or not all(
                [label in fs_example.sentiment_per_aspect for label in self._aspects]
            ):
                raise ValueError(
                    f"Aspect mismatch: {self._task_id} has aspects {self._aspects}. Few-shot examples have "
                    f"aspects {fs_example.sentiment_per_aspect.keys()}."
                )

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

    @override
    def to_hf_dataset(self, docs: Iterable[Doc], threshold: float | None = None) -> datasets.Dataset:
        threshold = threshold or self.THRESHOLD

        # Define metadata.
        features = datasets.Features(
            {
                "text": datasets.Value("string"),
                "aspect": datasets.Sequence(datasets.Value("float32")),
                "score": datasets.Value("float32"),
            }
        )
        info = datasets.DatasetInfo(
            description=f"Aspect-based sentiment analysis dataset with aspects {self._aspects}. Generated with sieves "
            f"v{Config.get_version()}.",
            features=features,
        )

        # Fetch data used for generating dataset.
        aspects = self._aspects
        try:
            data = [
                (
                    doc.text,
                    doc.results[self._task_id].sentiment_per_aspect,
                    doc.results[self._task_id].score,
                )
                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, scores, score in data:
                yield {"text": text, "aspect": [scores[aspect] for aspect in aspects], "score": score}

        # 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_dspy_example(self, truth: dspy.Example, pred: dspy.Prediction, trace: Any, model: dspy.LM) -> float:
        # Compute per-aspect accuracy as 1 - abs(true_sentiment - pred_sentiment)
        # Average across all aspects (same approach as multi-label classification)
        accuracy = 0
        for aspect, sentiment in truth["sentiment_per_aspect"].items():
            if aspect in pred["sentiment_per_aspect"]:
                pred_sentiment = max(min(pred["sentiment_per_aspect"][aspect], 1), 0)
                accuracy += 1 - abs(sentiment - pred_sentiment)

        base_accuracy = accuracy / len(truth["sentiment_per_aspect"])

        # If score is available in both truth and pred, incorporate it into the metric.
        if "score" in truth and truth["score"] is not None and "score" in pred and pred["score"] is not None:
            score_accuracy = 1 - abs(truth["score"] - max(min(pred["score"], 1), 0))
            return (base_accuracy + score_accuracy) / 2

        return base_accuracy

fewshot_example_type property

Return few-shot example type.

Returns:

Type Description
type[FewshotExample]

Few-shot example type.

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 property

Return the unified Pydantic prompt signature for this task.

Returns:

Type Description
type[BaseModel]

Unified Pydantic prompt signature.

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).
    """
    docs = iter(docs) if not isinstance(docs, Iterator) else docs

    # Materialize docs in batches. This doesn't incur additional memory overhead, as docs are materialized in
    # batches downstream anyway.
    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)]:
        # First pass: determine which docs pass the condition by index.
        passing_indices: set[int] = {
            idx for idx, doc in enumerate(docs_batch) if self._condition is None or self._condition(doc)
        }

        # Process all passing docs in one batch.
        processed = self._call(d for i, d in enumerate(docs_batch) 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_batch):
            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__(model, model_settings=ModelSettings(), aspects=tuple(), task_id=None, include_meta=True, batch_size=-1, prompt_instructions=None, fewshot_examples=(), condition=None)

Initialize SentimentAnalysis task.

Parameters:

Name Type Description Default
model TaskModel

Model to use.

 required
model_settings ModelSettings

Settings for structured generation.

 ModelSettings()
aspects tuple[str, ...]

Aspects to consider in sentiment analysis. Overall sentiment will always be determined. If empty, only overall sentiment will be determined.

 tuple()
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.

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

Optional callable that determines whether to process each document.

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

    :param model: Model to use.
    :param model_settings: Settings for structured generation.
    :param aspects: Aspects to consider in sentiment analysis. Overall sentiment will always be determined. If
        empty, only overall sentiment will be determined.
    :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 condition: Optional callable that determines whether to process each document.
    """
    self._aspects = tuple(sorted(set(aspects) | {"overall"}))
    super().__init__(
        model=model,
        model_settings=model_settings,
        task_id=task_id,
        include_meta=include_meta,
        batch_size=batch_size,
        overwrite=False,
        prompt_instructions=prompt_instructions,
        fewshot_examples=fewshot_examples,
        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)

evaluate(docs, judge=None, failure_threshold=0.5)

Evaluate task performance using DSPy-based evaluation.

Parameters:

Name Type Description Default
docs Iterable[Doc]

Documents to evaluate.

 required
judge LM | None

Optional DSPy LM instance to use as judge for generative tasks.

 None
failure_threshold float

Decision threshold for whether to mark predicitions as failures.

 0.5

Returns:

Type Description
TaskEvaluationReport

Evaluation report.
Source code in sieves/tasks/predictive/core.py 
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def evaluate(
    self, docs: Iterable[Doc], judge: dspy.LM | None = None, failure_threshold: float = 0.5
) -> TaskEvaluationReport:
    """Evaluate task performance using DSPy-based evaluation.

    :param docs: Documents to evaluate.
    :param judge: Optional DSPy LM instance to use as judge for generative tasks.
    :param failure_threshold: Decision threshold for whether to mark predicitions as failures.
    :return: Evaluation report.
    """
    truths: list[Any] = []
    preds: list[Any] = []
    failures: list[Doc] = []

    # Evaluate each doc individually to identify failed predictions.
    for doc in docs:
        if self.id not in doc.results:
            continue

        pred = doc.results[self.id]
        gold = doc.gold.get(self.id, None)

        # Accumulate for corpus-level metrics.
        truths.append(gold)
        preds.append(pred)

        # If gold or prediction is None: we cannot do proper evalution, so we just check whether they're both None
        # to compute score for failure analysis.
        if gold is None or pred is None:
            if gold is not None or pred is not None:
                failures.append(doc)
        else:
            # Convert result and gold to DSPy representation.
            truth = dspy.Example(**self._task_result_to_dspy_dict(gold))
            pred_dspy = dspy.Prediction(**self._task_result_to_dspy_dict(pred))

            # Call internal evaluation logic for per-doc failure analysis.
            score = self._evaluate_dspy_example(truth, pred_dspy, trace=None, model=judge)

            if score < failure_threshold:
                failures.append(doc)

    # Evaluate on corpus level to obtain representative metrics.
    metrics = self._compute_metrics(truths, preds, judge=judge)

    return TaskEvaluationReport(
        metrics=metrics,
        task_id=self.id,
        failures=failures,
    )

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_dspy_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 sentiment analysis task.

DSPySentimentAnalysis

Bases: SentimentAnalysisBridge[PromptSignature, Result, InferenceMode]

DSPy bridge for sentiment analysis.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class DSPySentimentAnalysis(SentimentAnalysisBridge[dspy_.PromptSignature, dspy_.Result, dspy_.InferenceMode]):
    """DSPy bridge for sentiment analysis."""

    @override
    def _validate(self) -> None:
        assert self._model_type == ModelType.dspy

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return ""

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

    @property
    @override
    def _chunk_extractor(self) -> Callable[[Any], tuple[dict[str, float], float | None]]:
        def extractor(res: Any) -> tuple[dict[str, float], float | None]:
            m_scores = {aspect: float(getattr(res, aspect)) for aspect in self._aspects}
            return m_scores, getattr(res, "score", None)

        return extractor

    @override
    def integrate(self, results: Sequence[dspy_.Result], docs: list[Doc]) -> list[Doc]:
        for doc, result in zip(docs, results):
            # Take the first completion.
            prediction = result.completions[0]
            label_scores = {aspect: float(getattr(prediction, aspect)) for aspect in self._aspects}
            doc.results[self._task_id] = Result(
                sentiment_per_aspect=label_scores,
                score=getattr(prediction, "score", None),
            )
        return docs

    @override
    def consolidate(
        self, results: Sequence[dspy_.Result], docs_offsets: list[tuple[int, int]]
    ) -> Sequence[dspy_.Result]:
        consolidated_results_clean = self._consolidation_strategy.consolidate(results, docs_offsets)

        # Wrap back into dspy.Prediction.
        consolidated_results: list[dspy_.Result] = []
        for aspect_scores, overall_score in consolidated_results_clean:
            data = {**aspect_scores, "score": overall_score}
            consolidated_results.append(
                dspy.Prediction.from_completions(
                    [data],
                    signature=self.prompt_signature,
                )
            )
        return consolidated_results

model_settings property

Return model settings.

Returns:

Type Description
ModelSettings

Model settings.

model_type property

Return model type.

Returns:

Type Description
ModelType

Model type.

prompt_signature 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_xml 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, prompt_instructions, aspects, model_settings, prompt_signature, model_type, fewshot_examples=())

Initialize sentiment analysis bridge.

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
aspects tuple[str, ...]

Aspects to analyze.

 required
model_settings ModelSettings

Settings for structured generation.

 required
prompt_signature type[BaseModel]

Unified Pydantic prompt signature.

 required
model_type ModelType

Model type.

 required
fewshot_examples Sequence[BaseModel]

Few-shot examples.

 ()
Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    aspects: tuple[str, ...],
    model_settings: ModelSettings,
    prompt_signature: type[pydantic.BaseModel],
    model_type: ModelType,
    fewshot_examples: Sequence[pydantic.BaseModel] = (),
):
    """Initialize sentiment analysis bridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to analyze.
    :param model_settings: Settings for structured generation.
    :param prompt_signature: Unified Pydantic prompt signature.
    :param model_type: Model type.
    :param fewshot_examples: Few-shot examples.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
        prompt_signature=prompt_signature,
        model_type=model_type,
        fewshot_examples=fewshot_examples,
    )
    self._aspects = aspects
    self._consolidation_strategy = MapScoreConsolidation(extractor=self._chunk_extractor, keys=list(self._aspects))

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]

PydanticSentimentAnalysis

Bases: SentimentAnalysisBridge[BaseModel, BaseModel, ModelWrapperInferenceMode]

Pydantic-based sentiment analysis bridge.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class PydanticSentimentAnalysis(
    SentimentAnalysisBridge[pydantic.BaseModel, pydantic.BaseModel, ModelWrapperInferenceMode]
):
    """Pydantic-based sentiment analysis bridge."""

    @override
    def _validate(self) -> None:
        assert self._model_type in {ModelType.langchain, ModelType.outlines}

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        aspects_str = ",".join(self._aspects)
        return (
            f"Perform aspect-based sentiment analysis of the provided text given the provided aspects: {aspects_str}.\n"
            "For each aspect, provide the sentiment in the provided text with respect to this aspect.\n"
            "The 'overall' aspect should reflect the sentiment in the text overall.\n"
            "A score of 1.0 means that the sentiment in the text with respect to this aspect is extremely positive.\n"
            "0 means the opposite, 0.5 means neutral.\n"
            "The sentiment score per aspect should ALWAYS be between 0 and 1.\n"
            "Also provide an overall confidence score between 0.0 and 1.0 for the sentiment analysis."
        )

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

    @property
    @override
    def _chunk_extractor(self) -> Callable[[Any], tuple[dict[str, float], float | None]]:
        def extractor(res: Any) -> tuple[dict[str, float], float | None]:
            m_scores = {aspect: float(getattr(res, aspect)) for aspect in self._aspects}
            return m_scores, getattr(res, "score", None)

        return extractor

    @override
    def integrate(self, results: Sequence[pydantic.BaseModel], docs: list[Doc]) -> list[Doc]:
        for doc, result in zip(docs, results):
            label_scores = {k: v for k, v in result.model_dump().items() if k not in ["score"]}
            doc.results[self._task_id] = Result(sentiment_per_aspect=label_scores, score=getattr(result, "score", None))
        return docs

    @override
    def consolidate(
        self, results: Sequence[pydantic.BaseModel], docs_offsets: list[tuple[int, int]]
    ) -> Sequence[pydantic.BaseModel]:
        assert issubclass(self.prompt_signature, pydantic.BaseModel)

        consolidated_results_clean = self._consolidation_strategy.consolidate(results, docs_offsets)
        consolidated_results: list[pydantic.BaseModel] = []

        for aspect_scores, overall_score in consolidated_results_clean:
            consolidated_results.append(
                self.prompt_signature(
                    **aspect_scores,
                    score=overall_score,
                )
            )

        return consolidated_results

    @override
    @property
    def inference_mode(self) -> outlines_.InferenceMode | langchain_.InferenceMode:
        if self._model_type == ModelType.outlines:
            return self._model_settings.inference_mode or outlines_.InferenceMode.json
        elif self._model_type == ModelType.langchain:
            return self._model_settings.inference_mode or langchain_.InferenceMode.structured

        raise ValueError(f"Unsupported model type: {self._model_type}")

model_settings property

Return model settings.

Returns:

Type Description
ModelSettings

Model settings.

model_type property

Return model type.

Returns:

Type Description
ModelType

Model type.

prompt_signature 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_xml 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, prompt_instructions, aspects, model_settings, prompt_signature, model_type, fewshot_examples=())

Initialize sentiment analysis bridge.

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
aspects tuple[str, ...]

Aspects to analyze.

 required
model_settings ModelSettings

Settings for structured generation.

 required
prompt_signature type[BaseModel]

Unified Pydantic prompt signature.

 required
model_type ModelType

Model type.

 required
fewshot_examples Sequence[BaseModel]

Few-shot examples.

 ()
Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    aspects: tuple[str, ...],
    model_settings: ModelSettings,
    prompt_signature: type[pydantic.BaseModel],
    model_type: ModelType,
    fewshot_examples: Sequence[pydantic.BaseModel] = (),
):
    """Initialize sentiment analysis bridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to analyze.
    :param model_settings: Settings for structured generation.
    :param prompt_signature: Unified Pydantic prompt signature.
    :param model_type: Model type.
    :param fewshot_examples: Few-shot examples.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
        prompt_signature=prompt_signature,
        model_type=model_type,
        fewshot_examples=fewshot_examples,
    )
    self._aspects = aspects
    self._consolidation_strategy = MapScoreConsolidation(extractor=self._chunk_extractor, keys=list(self._aspects))

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]

SentimentAnalysisBridge

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

Abstract base class for sentiment analysis bridges.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class SentimentAnalysisBridge(Bridge[_BridgePromptSignature, _BridgeResult, ModelWrapperInferenceMode], abc.ABC):
    """Abstract base class for sentiment analysis bridges."""

    def __init__(
        self,
        task_id: str,
        prompt_instructions: str | None,
        aspects: tuple[str, ...],
        model_settings: ModelSettings,
        prompt_signature: type[pydantic.BaseModel],
        model_type: ModelType,
        fewshot_examples: Sequence[pydantic.BaseModel] = (),
    ):
        """Initialize sentiment analysis bridge.

        :param task_id: Task ID.
        :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
        :param aspects: Aspects to analyze.
        :param model_settings: Settings for structured generation.
        :param prompt_signature: Unified Pydantic prompt signature.
        :param model_type: Model type.
        :param fewshot_examples: Few-shot examples.
        """
        super().__init__(
            task_id=task_id,
            prompt_instructions=prompt_instructions,
            overwrite=False,
            model_settings=model_settings,
            prompt_signature=prompt_signature,
            model_type=model_type,
            fewshot_examples=fewshot_examples,
        )
        self._aspects = aspects
        self._consolidation_strategy = MapScoreConsolidation(extractor=self._chunk_extractor, keys=list(self._aspects))

    @property
    @abc.abstractmethod
    def _chunk_extractor(self) -> Callable[[Any], tuple[dict[str, float], float | None]]:
        """Return a callable that extracts (map_scores, overall_score) from a raw chunk result.

        :return: Extractor callable.
        """

inference_mode abstractmethod property

Return inference mode.

Returns:

Type Description
ModelWrapperInferenceMode

Inference mode.

model_settings property

Return model settings.

Returns:

Type Description
ModelSettings

Model settings.

model_type property

Return model type.

Returns:

Type Description
ModelType

Model type.

prompt_signature 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_xml 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, prompt_instructions, aspects, model_settings, prompt_signature, model_type, fewshot_examples=())

Initialize sentiment analysis bridge.

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
aspects tuple[str, ...]

Aspects to analyze.

 required
model_settings ModelSettings

Settings for structured generation.

 required
prompt_signature type[BaseModel]

Unified Pydantic prompt signature.

 required
model_type ModelType

Model type.

 required
fewshot_examples Sequence[BaseModel]

Few-shot examples.

 ()
Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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def __init__(
    self,
    task_id: str,
    prompt_instructions: str | None,
    aspects: tuple[str, ...],
    model_settings: ModelSettings,
    prompt_signature: type[pydantic.BaseModel],
    model_type: ModelType,
    fewshot_examples: Sequence[pydantic.BaseModel] = (),
):
    """Initialize sentiment analysis bridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to analyze.
    :param model_settings: Settings for structured generation.
    :param prompt_signature: Unified Pydantic prompt signature.
    :param model_type: Model type.
    :param fewshot_examples: Few-shot examples.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
        model_settings=model_settings,
        prompt_signature=prompt_signature,
        model_type=model_type,
        fewshot_examples=fewshot_examples,
    )
    self._aspects = aspects
    self._consolidation_strategy = MapScoreConsolidation(extractor=self._chunk_extractor, keys=list(self._aspects))

consolidate(results, docs_offsets) abstractmethod

Consolidate results for document chunks into document results.

Parameters:

Name Type Description Default
results Sequence[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
Sequence[TaskResult]

Results per document as a sequence.
Source code in sieves/tasks/predictive/bridges.py 
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@abc.abstractmethod
def consolidate(self, results: Sequence[TaskResult], docs_offsets: list[tuple[int, int]]) -> Sequence[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: Results per document as a sequence.
    """

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]

integrate(results, docs) abstractmethod

Integrate results into Doc instances.

Parameters:

Name Type Description Default
results Sequence[TaskResult]

Results from prompt executable.

 required
docs list[Doc]

Doc instances to update.

 required

Returns:

Type Description
list[Doc]

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

    :param results: Results from prompt executable.
    :param docs: Doc instances to update.
    :return: Updated doc instances as a list.
    """

Schemas for sentiment analysis task.

FewshotExample

Bases: FewshotExample

Example for sentiment analysis few-shot prompting.

Attributes: text: Input text. sentiment_per_aspect: Mapping of aspects to sentiments. score: Confidence score for the sentiment assessment.

Source code in sieves/tasks/predictive/schemas/sentiment_analysis.py 
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class FewshotExample(BaseFewshotExample):
    """Example for sentiment analysis few-shot prompting.

    Attributes:
        text: Input text.
        sentiment_per_aspect: Mapping of aspects to sentiments.
        score: Confidence score for the sentiment assessment.
    """

    sentiment_per_aspect: dict[str, float]
    score: float | None = None

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

        :return: Target fields.
        """
        return ("sentiment_per_aspect", "score")

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)

Result

Bases: BaseModel

Result of an aspect-based sentiment analysis task.

Attributes: sentiment_per_aspect: Mapping of aspects to sentiment scores. score: Overall confidence score for the sentiment analysis.

Source code in sieves/tasks/predictive/schemas/sentiment_analysis.py 
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class Result(pydantic.BaseModel):
    """Result of an aspect-based sentiment analysis task.

    Attributes:
        sentiment_per_aspect: Mapping of aspects to sentiment scores.
        score: Overall confidence score for the sentiment analysis.
    """

    sentiment_per_aspect: dict[str, float] = pydantic.Field(
        description="A mapping where keys are aspect names (e.g., 'overall', 'quality') and values are sentiment "
        "scores between 0 (Negative) and 1 (Positive)."
    )
    score: float | None = pydantic.Field(
        default=None, description="Provide an overall confidence score for the sentiment analysis, between 0 and 1."
    )