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Sentiment Analysis

Aspect-based sentiment analysis predictive task.

FewshotExample

Bases: FewshotExample

Few-shot example with per-aspect sentiment scores.

Source code in sieves/tasks/predictive/sentiment_analysis/core.py 
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class FewshotExample(BaseFewshotExample):
    """Few-shot example with per-aspect sentiment scores."""

    reasoning: str
    sentiment_per_aspect: dict[str, float]

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

    @pydantic.model_validator(mode="after")
    def check_confidence(self) -> FewshotExample:
        """Validate that 'overall' exists and all scores are in [0, 1]."""
        assert "overall" in self.sentiment_per_aspect, ValueError(
            "'overall' score has to be given in `sentiment_per_aspect` dict."
        )
        if any([conf for conf in self.sentiment_per_aspect.values() if not 0 <= conf <= 1]):
            raise ValueError("Sentiment score has to be between 0 and 1.")
        return self

input_fields property

Defines which fields are inputs.

Returns:

Type Description
Sequence[str]

Sequence of field names.

check_confidence()

Validate that 'overall' exists and all scores are in [0, 1].

Source code in sieves/tasks/predictive/sentiment_analysis/core.py 
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@pydantic.model_validator(mode="after")
def check_confidence(self) -> FewshotExample:
    """Validate that 'overall' exists and all scores are in [0, 1]."""
    assert "overall" in self.sentiment_per_aspect, ValueError(
        "'overall' score has to be given in `sentiment_per_aspect` dict."
    )
    if any([conf for conf in self.sentiment_per_aspect.values() if not 0 <= conf <= 1]):
        raise ValueError("Sentiment score has to be between 0 and 1.")
    return self

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)

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."""

    def __init__(
        self,
        model: _TaskModel,
        generation_settings: GenerationSettings = GenerationSettings(),
        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] = (),
    ) -> None:
        """
        Initialize SentimentAnalysis task.

        :param model: Model to use.
        :param generation_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.
        """
        self._aspects = tuple(sorted(set(aspects) | {"overall"}))
        super().__init__(
            model=model,
            generation_settings=generation_settings,
            task_id=task_id,
            include_meta=include_meta,
            batch_size=batch_size,
            overwrite=False,
            prompt_instructions=prompt_instructions,
            fewshot_examples=fewshot_examples,
        )
        self._fewshot_examples: Sequence[FewshotExample]

    @override
    def _init_bridge(self, engine_type: EngineType) -> _TaskBridge:
        bridge_types: dict[EngineType, type[_TaskBridge]] = {
            EngineType.dspy: DSPySentimentAnalysis,
            EngineType.outlines: OutlinesSentimentAnalysis,
            EngineType.langchain: LangChainSentimentAnalysis,
        }

        try:
            bridge_type = bridge_types[engine_type]

            return bridge_type(
                task_id=self._task_id,
                prompt_instructions=self._custom_prompt_instructions,
                aspects=self._aspects,
            )
        except KeyError as err:
            raise KeyError(f"Engine type {engine_type} is not supported by {self.__class__.__name__}.") from err

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

    @override
    def _validate_fewshot_examples(self) -> None:
        for fs_example in self._fewshot_examples or []:
            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 = 0.5) -> datasets.Dataset:
        # Define metadata.
        features = datasets.Features(
            {"text": datasets.Value("string"), "aspect": datasets.Sequence(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]) 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, result in data:
                scores = {sent_score[0]: sent_score[1] for sent_score in result}
                yield {"text": text, "aspect": [scores[aspect] for aspect in aspects]}

        # 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:
        # 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)

        return accuracy / len(truth["sentiment_per_aspect"])

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__(model, generation_settings=GenerationSettings(), aspects=tuple(), task_id=None, include_meta=True, batch_size=-1, prompt_instructions=None, fewshot_examples=())

Initialize SentimentAnalysis task.

Parameters:

Name Type Description Default
model _TaskModel

Model to use.

 required
generation_settings GenerationSettings

Settings for structured generation.

 GenerationSettings()
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.

 ()
Source code in sieves/tasks/predictive/sentiment_analysis/core.py 
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def __init__(
    self,
    model: _TaskModel,
    generation_settings: GenerationSettings = GenerationSettings(),
    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] = (),
) -> None:
    """
    Initialize SentimentAnalysis task.

    :param model: Model to use.
    :param generation_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.
    """
    self._aspects = tuple(sorted(set(aspects) | {"overall"}))
    super().__init__(
        model=model,
        generation_settings=generation_settings,
        task_id=task_id,
        include_meta=include_meta,
        batch_size=batch_size,
        overwrite=False,
        prompt_instructions=prompt_instructions,
        fewshot_examples=fewshot_examples,
    )
    self._fewshot_examples: Sequence[FewshotExample]

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 sentiment analysis task.

DSPySentimentAnalysis

Bases: SentAnalysisBridge[PromptSignature, Result, InferenceMode]

DSPy bridge for sentiment analysis.

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

    @override
    @property
    def _default_prompt_instructions(self) -> str:
        return """
        Aspect-based sentiment analysis of the provided text given the provided aspects.
        For each aspect, provide the sentiment score with which you reflects the sentiment in the provided text with
        respect to this aspect.
        The "overall" aspect should reflect the sentiment in the text overall.
        A score of 1.0 means that the sentiment in the text with respect to this aspect is extremely positive.
        0 means the opposite, 0.5 means neutral.
        Sentiment per aspect should always be between 0 and 1.
        """

    @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]:
        aspects = self._aspects
        # Dynamically create Literal as output type.
        AspectType = Literal[*aspects]  # type: ignore[valid-type]

        class SentimentAnalysis(dspy.Signature):  # type: ignore[misc]
            text: str = dspy.InputField(description="Text to determine sentiments for.")
            sentiment_per_aspect: dict[AspectType, float] = dspy.OutputField(
                description="Sentiment in this text with respect to the corresponding aspect."
            )

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

        return SentimentAnalysis

    @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.sentiment_per_aspect) == 1
            sorted_preds = sorted(
                ((aspect, score) for aspect, score in result.completions.sentiment_per_aspect[0].items()),
                key=lambda x: x[1],
                reverse=True,
            )
            doc.results[self._task_id] = sorted_preds
        return docs

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

        # Determine label scores for chunks per document.
        for doc_offset in docs_offsets:
            aspect_scores: dict[str, float] = {label: 0.0 for label in self._aspects}
            doc_results = results[doc_offset[0] : doc_offset[1]]

            for res in doc_results:
                assert len(res.completions.sentiment_per_aspect) == 1
                for label, score in res.completions.sentiment_per_aspect[0].items():
                    # Clamp score to range between 0 and 1. Alternatively we could force this in the prompt signature,
                    # but this fails occasionally with some models and feels too strict (maybe a strict mode would be
                    # useful?).
                    aspect_scores[label] += max(0, min(score, 1))

            sorted_aspect_scores: list[dict[str, str | float]] = sorted(
                (
                    {"aspect": aspect, "score": score / (doc_offset[1] - doc_offset[0])}
                    for aspect, score in aspect_scores.items()
                ),
                key=lambda x: x["score"],
                reverse=True,
            )

            yield dspy.Prediction.from_completions(
                {
                    "sentiment_per_aspect": [{sls["aspect"]: sls["score"] for sls in sorted_aspect_scores}],
                    "reasoning": [str([res.reasoning for res in doc_results])],
                },
                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, aspects)

Initialize SentAnalysisBridge.

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 consider.

 required
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, ...]):
    """Initialize SentAnalysisBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to consider.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._aspects = aspects

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)

LangChainSentimentAnalysis

Bases: PydanticBasedSentAnalysis[InferenceMode]

LangChain bridge for sentiment analysis.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class LangChainSentimentAnalysis(PydanticBasedSentAnalysis[langchain_.InferenceMode]):
    """LangChain bridge for sentiment analysis."""

    @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, aspects)

Initialize SentAnalysisBridge.

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 consider.

 required
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, ...]):
    """Initialize SentAnalysisBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to consider.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._aspects = aspects

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)

OutlinesSentimentAnalysis

Bases: PydanticBasedSentAnalysis[InferenceMode]

Outlines bridge for sentiment analysis.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class OutlinesSentimentAnalysis(PydanticBasedSentAnalysis[outlines_.InferenceMode]):
    """Outlines bridge for sentiment analysis."""

    @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, aspects)

Initialize SentAnalysisBridge.

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 consider.

 required
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, ...]):
    """Initialize SentAnalysisBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to consider.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._aspects = aspects

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)

PydanticBasedSentAnalysis

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

Base class for Pydantic-based sentiment analysis bridges.

Source code in sieves/tasks/predictive/sentiment_analysis/bridges.py 
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class PydanticBasedSentAnalysis(
    SentAnalysisBridge[pydantic.BaseModel, pydantic.BaseModel, EngineInferenceMode], abc.ABC
):
    """Base class for Pydantic-based sentiment analysis bridges."""

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

        The output for two aspects ASPECT_1 and ASPECT_2 should look like this:
        <output>
            <reasoning>REASONING</reasoning>
            <aspect_sentiments>
                <aspect_sentiment>
                    <aspect>ASPECT_1</aspect>
                    <sentiment>SENTIMENT_SCORE_1</sentiment>
                <aspect_sentiment>
                <aspect_sentiment>
                    <aspect>ASPECT_2</aspect>
                    <sentiment>SENTIMENT_SCORE_2</sentiment>
                <aspect_sentiment>
            </aspect_sentiments>
        </output>
        """
        )

    @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>
                        <aspect_sentiments>
                        {%- for a, s in example.sentiment_per_aspect.items() %}
                            <aspect_sentiment>
                                <aspect>{{ a }}</aspect>
                                <sentiment>{{ s }}</sentiment>
                            </aspect_sentiment>
                        {% endfor -%}
                        </aspect_sentiments>
                    </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]:
        prompt_sig = pydantic.create_model(  # type: ignore[call-overload]
            "SentimentAnalysis",
            __base__=pydantic.BaseModel,
            __doc__="Sentiment analysis of specified text.",
            reasoning=(str, ...),
            **{aspect: (float, ...) for aspect in self._aspects},
        )

        assert isinstance(prompt_sig, type) and issubclass(prompt_sig, pydantic.BaseModel)
        return prompt_sig

    @override
    def integrate(self, results: Iterable[pydantic.BaseModel], docs: Iterable[Doc]) -> Iterable[Doc]:
        for doc, result in zip(docs, results):
            label_scores = {k: v for k, v in result.model_dump().items() if k != "reasoning"}
            doc.results[self._task_id] = sorted(
                ((aspect, score) for aspect, score in label_scores.items()), key=lambda x: x[1], reverse=True
            )
        return docs

    @override
    def consolidate(
        self, results: Iterable[pydantic.BaseModel], docs_offsets: list[tuple[int, int]]
    ) -> Iterable[pydantic.BaseModel]:
        results = list(results)

        # Determine label scores for chunks per document.
        reasonings: list[str] = []
        for doc_offset in docs_offsets:
            aspect_scores: dict[str, float] = {label: 0.0 for label in self._aspects}
            doc_results = results[doc_offset[0] : doc_offset[1]]

            for rec in doc_results:
                if rec is None:
                    continue  # type: ignore[unreachable]

                assert hasattr(rec, "reasoning")
                reasonings.append(rec.reasoning)
                for aspect in self._aspects:
                    # Clamp score to range between 0 and 1. Alternatively we could force this in the prompt signature,
                    # but this fails occasionally with some models and feels too strict (maybe a strict mode would be
                    # useful?).
                    aspect_scores[aspect] += max(0, min(getattr(rec, aspect), 1))

            yield self.prompt_signature(
                reasoning=str(reasonings),
                **{aspect: score / (doc_offset[1] - doc_offset[0]) for aspect, score in aspect_scores.items()},
            )

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, aspects)

Initialize SentAnalysisBridge.

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 consider.

 required
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, ...]):
    """Initialize SentAnalysisBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to consider.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._aspects = aspects

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)

SentAnalysisBridge

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

Abstract base class for sentiment analysis bridges.

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

    def __init__(self, task_id: str, prompt_instructions: str | None, aspects: tuple[str, ...]):
        """Initialize SentAnalysisBridge.

        :param task_id: Task ID.
        :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
        :param aspects: Aspects to consider.
        """
        super().__init__(
            task_id=task_id,
            prompt_instructions=prompt_instructions,
            overwrite=False,
        )
        self._aspects = aspects

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, aspects)

Initialize SentAnalysisBridge.

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 consider.

 required
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, ...]):
    """Initialize SentAnalysisBridge.

    :param task_id: Task ID.
    :param prompt_instructions: Custom prompt instructions. If None, default instructions are used.
    :param aspects: Aspects to consider.
    """
    super().__init__(
        task_id=task_id,
        prompt_instructions=prompt_instructions,
        overwrite=False,
    )
    self._aspects = aspects

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.
    """