r"""Generates embeddings for the given texts. Args: objs (list[T]): The objects for which to generate the embeddings.

r"""Generates embeddings for the given texts. Args: objs (list[T]): The objects for which to generate the embeddings.

Estimate the number of characters that can fit into a bounding box. :param width_in_inches: The width of the bounding box, in inches. :

r"""Recursively evaluate arguments, including nested calls.

r"""Recursively evaluate arguments, including nested calls.

r"""Execute the input python codes in a security environment. Args: code (str): Generated python code to be executed.

r"""Execute the input python codes in a security environment. Args: code (str): Generated python code to be executed.

Open an image file and use Tesseract OCR to extract text. :param image_path: Path to the image file :return: Extracted text as a string

Open an image file and use Tesseract OCR to extract text. :param image_path: Path to the image file :return: Extracted text as a string

r"""Check if the given instruction passes all filter functions. Args: prompt (str): The prompt of generating the instruction.

r"""Check if the given instruction passes all filter functions. Args: prompt (str): The prompt of generating the instruction.

r"""Creates a File object from a BytesIO object. Args: file (BytesIO): A BytesIO object representing the contents of the

r"""Creates a File object from a BytesIO object. Args: file (BytesIO): A BytesIO object representing the contents of the

r"""Generates a list of system messages from a list of dictionaries. Args: meta_dicts (List[Dict[str, str]]): A list of dictionar

r"""Generates a list of system messages from a list of dictionaries. Args: meta_dicts (List[Dict[str, str]]): A list of dictionar

Cluster points based on similarity. Args: similarity (np.ndarray): The similarity matrix. sim_bound (float): The similarity

Get the content type of the slide.

Get the content type of the slide.

r"""Gets the prompt for generating tasks for a given task type. Args: task_type (TaskType): The type of the task. Return

r"""Gets the prompt for generating tasks for a given task type. Args: task_type (TaskType): The type of the task. Return

r"""Returns a dictionary containing information about the chat session. Args: session_id (str, optional): The ID of the chat sess

r"""Returns a dictionary containing information about the chat session. Args: session_id (str, optional): The ID of the chat sess

Get the last text item at the current document level. Returns: The text item or None, if the current level parent has no children

Get the last text item at the current document level. Returns: The text item or None, if the current level parent has no children

Calculate logo width to preserve aspect ratio. Args: logo_path: Path to logo image target_height: Desired height in inches

Calculate logo width to preserve aspect ratio. Args: logo_path: Path to logo image target_height: Desired height in inches

Get the path to a logo file using fuzzy matching. Args: name: Name of the conference/institution category: T

r"""Get model encoding from tiktoken. Args: value_for_tiktoken: Model value for tiktoken. Returns: tiktoken.Encoding: Model

r"""Get model encoding from tiktoken. Args: value_for_tiktoken: Model value for tiktoken. Returns: tiktoken.Encoding: Model

r"""Retrieve node properties from the graph. Returns: Tuple[List[str], List[Dict[str, Any]]]: A tuple where the first

r"""Retrieve node properties from the graph. Returns: Tuple[List[str], List[Dict[str, Any]]]: A tuple where the first

r"""Extracts plain text from a list of rich text elements. Args: rich_text: A list of dictionaries representing rich text elements.

r"""Extracts plain text from a list of rich text elements. Args: rich_text: A list of dictionaries representing rich text elements.

Compute perplexity for arbitrarily long *text* using a sliding‑window approach. Parameters ---------- text : str The input string

Compute perplexity for arbitrarily long *text* using a sliding‑window approach. Parameters ---------- text : str The input string

r"""Get the JSON schema of a Pydantic model. Args: pydantic_params (Type[BaseModel]): The Pydantic model class to retrieve th

r"""Get the JSON schema of a Pydantic model. Args: pydantic_params (Type[BaseModel]): The Pydantic model class to retrieve th

r"""Retrieve relationship (edge) properties from the graph. Returns: Tuple[List[str], List[Dict[str, Any]]]: A tuple where the fi

r"""Retrieve relationship (edge) properties from the graph. Returns: Tuple[List[str], List[Dict[str, Any]]]: A tuple where the fi

r"""Retrieve relationship types from the graph. Returns: List[str]: A list of relationship (edge) type names.

r"""Retrieve relationship types from the graph. Returns: List[str]: A list of relationship (edge) type names.

r"""Parse the response of the Agent and return task list. Args: task_response (str): The string response of the Agent. Returns:

r"""Parse the response of the Agent and return task list. Args: task_response (str): The string response of the Agent. Returns:

Initialize and return a text model. Args: device (str): The device to run the model on. Returns: BGEM3FlagModel: The in

List all available logos in the system.

List all available logos in the system.

r"""

r"""