Welcome to Auto Tabular Completion. It automatically fill in missing values in tabular data using in-context learning techniques. This tool leverages the capabilities of LLMs to predict output values based on given input data.

Check demo.ipynb for more details.

Check and for direct usage.

Your Excel file should contain columns prefixed with [Input] for input variables and [Output] for known or expected output values. Any columns not following this naming convention will be ignored during the prediction process.

• Use asyncio and ClientSession: Refactor the code to use asyncio and ClientSession for asynchronous processing instead of using ThreadPoolExecutor. This will improve efficiency and scalability for handling API requests and data processing.

• Save Data to SQLite Table: Update the code to save results into a SQLite database instead of an Excel file. This will involve creating a SQLite table and implementing the necessary functions to insert data into it.