cuery.tools.abs#

Higher-level API for extracting entities from texts using one-shot prompts.

Some examples of LLM-based methods:

Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models https://arxiv.org/abs/2412.12564
Structured Sentiment Analysis with Large Language Models: A Winning Solution for RuOpinionNE-2024 https://dialogue-conf.org/wp-content/uploads/2025/04/VatolinA.104.pdf
https://simmering.dev/blog/absa-with-dspy/ psimm/website

Attributes#

`ABS_PROMPT_SYSTEM`
`ABS_PROMPT_USER`

`AspectEntity`	Represents an aspect with its sentiment and qualified phrase.
`AspectEntities`	Represents a collection of entities with their sentiments and reasons for assignment.
`AspectSentimentExtractor`	Extract entities with sentiments from texts.

class cuery.tools.abs.AspectEntity(/, **data)#

Represents an aspect with its sentiment and qualified phrase.

entity: str#: The specific, normalized aspect being evaluated (e.g., ‘ride queues’, ‘food prices’).

sentiment: Literal['positive', 'negative']#: The sentiment associated with the aspect (positive or negative).

reason: str#

‘<standardized_adjective> <entity>’ (e.g., ‘long ride queues’).

category: str | None = None#: Optional category the entity belongs to (e.g., ‘food’, ‘service’, ‘pricing’).

class cuery.tools.abs.AspectEntities(/, **data)#

Represents a collection of entities with their sentiments and reasons for assignment.

entities: list[AspectEntity]#: A list of entities with their sentiments and reasons.

class cuery.tools.abs.AspectSentimentExtractor(/, **data)#

Extract entities with sentiments from texts.

texts: collections.abc.Iterable[str | float | None]#: The texts to extract entities from.

instructions: str = ''#: Further instructions from the user for the entity extraction task.

aspect_categories: list[str] | None = None#: Optional list of aspect categories to map entities to (e.g., [‘food’, ‘service’, ‘pricing’]).

response_model: ClassVar[cuery.ResponseClass]#: Defines the response model for this tool (ClassVar or property).

classmethod _coerce_na(v)#: Convert pandas NA/NaN values to None so Pydantic accepts them.

property prompt: cuery.Prompt#

Defines the prompt for this tool (ClassVar or property).

property context: cuery.AnyContext#