Parse the text into perceptions Args: - text: list[str]: The list of texts to be parsed - batch_size: int: T

Initializes async objects within the correct event loop.

Get the sentence embeddings of the input texts. Args: input_texts (list[str]): A list of input texts.

Returns the score of the value

Evaluates multiple perceptions in a batch and returns the measure results: relevant values, relevance values, and valence values

Creates and returns the appropriate model based on the model name.

Generate queries via LLM for perception retrieval.

Function to get default batch sizes based on GPU memory

An example of input_texts: input_texts = ["Hello!", "How are you?", "Tell me a joke."]

Predicts the output based on the given input text using the loaded model.

Get the sentence embeddings of the input texts using OpenAI API. Args: input_texts (list[str]): A list of input texts. model

Returns the valence of the value vector

Returns the valence of the value vector

Measures the involved entities in the text chunk by chunk Args: text (str): The text to be measured

Measure the given entities in the text based on RAG. Args: - text: str: The text to be measured - values: li

Parse the text into perceptions Args: - text: list[str]: The list of texts to be parsed - batch_size: int: T

Parse the texts into perceptions Args: - text: list[str]: The list of texts to be parsed - entities: list[st