eegunity.modules.llm_booster#

Submodules#

eegunity.modules.llm_booster.eeg_llm_booster module#

class eegunity.modules.llm_booster.eeg_llm_booster.EEGLLMBooster(main_instance)[source]#

Bases: _UDatasetSharedAttributes

eegunity.modules.llm_booster.eeg_llm_des_parser module#

eegunity.modules.llm_booster.eeg_llm_des_parser.llm_description_file_parser(directory: str, client_type: str, client_paras: dict, completion_para: dict)[source]#

Parse files in a specified directory to extract sampling rate and channel information using a Large Language Model (LLM) API.

This function traverses a directory to read various file formats. It extracts sampling rates and channel names from the files using an LLM API (e.g., GPT-4), and processes the extracted information based on user inputs to resolve conflicts.

Parameters:

  • directory (str) – The directory path where the files are stored for processing. Generally speaking, it will be the root directory of the dataset

  • client_type (str) – The type of LLM client to use (e.g., “AzureOpenAI”, “OpenAI”).

  • client_paras (dict) – A dictionary containing the parameters needed to initialize the LLM API client. Please refer to OpenAI documentation.

Returns:

dict – Returns an error message if no files are selected or if all data is discarded due to conflicts.

Return type:

A dictionary containing the parsed sampling rate and channel information.

Raises:

ValueError – If no files are selected for further analysis or if there are conflicts in the extracted data.:

Examples

>>> directory = 'path/to/description/directory'
>>> client_paras = {"api_key": "your_api_key", "api_version": "2023-03-15-preview"}
>>> client_type = "AzureOpenAI"
>>> result = llm_description_file_parser(directory, client_paras, client_type)
>>> print("The end result:", json.dumps(result, indent=4, ensure_ascii=False))

Contributor#

Jingyi Ding (Jingyi.Ding21@student.xjtlu.edu.cn), on 2024-07-26. EEGUnity Team modified it on 2025-02-23

eegunity.modules.llm_booster.eeg_llm_file_parser module#

eegunity.modules.llm_booster.eeg_llm_file_parser.llm_boost_parser(file_path: str, client_type: str, client_paras: dict, completion_para: dict, max_iterations: int = 5)[source]#

Parses and processes an EEG data file using Azure OpenAI to generate a function that reads the data, calculates the sampling frequency, and extracts channel names.

This function interacts with Azure OpenAI to automatically generate and refine a Python function that reads EEG data from a CSV or TXT file, determines the sampling frequency from timestamp columns, and extracts the relevant channel names. The function iterates through the process up to max_iterations times to refine the generated code in case of errors or unsatisfactory outputs.

Parameters:

  • file_path (str) – Path to the CSV or TXT file.

  • client_type (str) – Type of LLM client to use (e.g., ‘AzureOpenAI’, ‘OpenAI’).

  • client_paras (dict) – Parameters for initializing the LLM client.

  • completion_para (dict) – Parameters for initializing the LLM completion process. Note: Parameter ‘messages’ is generated by this function, do not specify this parameter in ‘completion_para’.

  • max_iterations ((int, optional)) – Maximum number of iterations to refine the generated function code. Default is 5.

Returns:

mne.io.Raw

Return type:

An MNE RawArray object containing the processed EEG data.

Raises:

  • ValueError – If the file extension is not supported.:

  • FileNotFoundError – If the specified file is not found.:

  • RuntimeError – If the function code cannot be generated within the maximum iteration limit.:

Example

>>> api_key = "your_api_key"
>>> azure_endpoint = "https://your_endpoint"
>>> locator_path = "data_file.csv"
>>> raw_data = llm_boost_parser(locator_path, api_key, azure_endpoint)
>>> print("Extracted Data:", raw_data)

Contributor#

Ziyi Jia (Ziyi.Jia21@student.xjtlu.edu.cn), on 2024-07-26. EEGUnity Team modified this file, on 2025-03-22.

Module contents#