Databonsai : A Python library for data cleaning and organization using Large Language Models (LLMs).

Databonsai

DataBonsai

Databonsai

AI Data Mining AI Development Assistant #Data Cleaning #Machine Learning #Automation #Large Language Models Standard Picks Open Source

Overview :

databonsai is a Python library that leverages Large Language Models (LLMs) to execute data cleaning tasks. It offers a range of tools including data categorization, transformation, and extraction, as well as validation of LLM outputs. It supports batch processing to save tokens and features retry logic to handle rate limits and transient errors.

Target Users :

["Data Scientist: Rapidly classify and clean large datasets to facilitate further analysis.","Developer: Integrable into applications to automate the data preprocessing workflow.","Corporate User: Improve processing efficiency and reduce costs through automated data cleaning."]

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Use Cases

Classifying and sentiment analysis of social media comments.

Automated archiving and thematic classification of news articles.

Organizing and extracting customer feedback data for product improvements.

Features

Data Classification: Uses LLMs to categorize data into predefined categories.

Data Transformation: Converts data using prompts.

Data Extraction: Extracts data into structured format according to patterns.

Batch Processing: Saves tokens by sending one model and example to classify a batch of data.

Retry Logic: Built-in retry logic to handle API-related errors.

Progress Bar: Provides progress feedback when processing large datasets.

Automatic Batch Processing: Automatically adjusts batch size to optimize token usage and error handling.

How to Use

1. Install the databonsai library.

2. Create a .env file with the API key in the root directory of your project.

3. Set up the LLM provider and categories.

4. Use the categorize function to classify individual data records.

5. Use the categorize_batch function to classify data in batches.

6. Use the apply_to_column_autobatch function to automate batch processing for DataFrames or lists.

7. Monitor the progress bar to understand current processing progress.

8. Adjust the batch size or use a better LLM model if errors occur.

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