first commit

.github/workflows/workflow.yml

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+name: CI
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Run tests
+      run: python -m unittest discover tests

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyCharm
+.idea/
+
+# VS Code
+.vscode/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Environment
+.env
+.venv
+env/
+venv/
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+
+testing.ipynb
+testing.py

LICENSE

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+MIT License
+
+Copyright (c) 2024 Vadim Borisov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# augini
+# Augini
+
+Augini is a Python framework for generating synthetic tabular data using AI. It leverages the power of language models to create realistic, fictional data based on existing datasets.
+
+## Installation
+
+You can install Augini using pip:
+```sh
+pip install augini
+```
+
+## Quick Start
+
+Here's a simple example of how to use Augini:
+
+```python
+from augini import Augini
+import pandas as pd
+
+# Initialize Augini
+augini = Augini(api_key="your_api_key", use_openrouter=True)
+
+# Create a sample DataFrame
+data = {
+    'Place of Birth': ['New York', 'London', 'Tokyo'],
+    'Age': [30, 25, 40],
+    'Gender': ['Male', 'Female', 'Male']
+}
+df = pd.DataFrame(data)
+
+# Add synthetic features
+result_df = augini.augment_columns(df, 'NAME', 'OCCUPATION', 'FAVORITE_DRINK')
+
+print(result_df)
+```
+
+## Features
+
+- Generate synthetic data based on existing datasets
+- Customizable prompts for data generation
+- Support for both OpenAI API and OpenRouter
+- Asynchronous processing for improved performance
+
+## Extending and Enriching Data
+
+Augini can be used to extend, augment, and enrich your datasets by adding synthetic features and bringing knowledge from language models to your data.
+
+### Adding Multiple Features
+
+You can add multiple features to your DataFrame:
+
+```python
+result_df = augini.augment_columns(df, 'Hobby', 'FavoriteColor', 'FavoriteMovie')
+print(result_df)
+```
+
+### Custom Prompts for Feature Generation
+
+Custom prompts allow you to generate specific features based on your needs:
+
+```python
+custom_prompt = "Based on the person's name and age, suggest a quirky pet for them. Respond with a JSON object with the key 'QuirkyPet'."
+result_df = augini.augment_single(df, 'QuirkyPet', custom_prompt=custom_prompt)
+print(result_df)
+```
+
+### Anonymizing Data
+
+You can anonymize sensitive information in your dataset by generating synthetic data:
+
+```python
+anonymize_prompt = "Create an anonymous profile for the person based on their age and city. Respond with a JSON object with keys 'AnonymousName' and 'AnonymousEmail'."
+result_df = augini.augment_single(df, 'AnonymousProfile', custom_prompt=anonymize_prompt)
+print(result_df)
+```
+
+## Bringing Knowledge from LLMs
+
+Leverage the knowledge embedded in language models to enhance your datasets:
+
+### Generating Detailed Descriptions
+
+```python
+description_prompt = "Generate a detailed description for a person based on their age and city. Respond with a JSON object with the key 'Description'."
+result_df = augini.augment_single(df, 'Description', custom_prompt=description_prompt)
+print(result_df)
+```
+
+### Suggesting Recommendations
+
+```python
+recommendation_prompt = "Suggest a book and a movie for a person based on their age and city. Respond with a JSON object with keys 'RecommendedBook' and 'RecommendedMovie'."
+result_df = augini.augment_single(df, 'Recommendations', custom_prompt=recommendation_prompt)
+print(result_df)
+```
+
+## Full Example
+
+Here's a full example demonstrating multiple features and custom prompts:
+
+```python
+from augini import Augini
+import pandas as pd
+
+# Initialize Augini
+augini = Augini(api_key="your_api_key", use_openrouter=True)
+
+# Create a sample DataFrame
+data = {
+    'Name': ['Alice Johnson', 'Bob Smith', 'Charlie Davis'],
+    'Age': [28, 34, 45],
+    'City': ['New York', 'Los Angeles', 'Chicago']
+}
+df = pd.DataFrame(data)
+
+# Add multiple synthetic features
+result_df = augini.augment_columns(df, 'Occupation', 'Hobby', 'FavoriteColor')
+
+# Add a custom feature
+custom_prompt = "Based on the person's name and age, suggest a quirky pet for them. Respond with a JSON object with the key 'QuirkyPet'."
+result_df = augini.augment_single(result_df, 'QuirkyPet', custom_prompt=custom_prompt)
+
+# Anonymize data
+anonymize_prompt = "Create an anonymous profile for the person based on their age and city. Respond with a JSON object with keys 'AnonymousName' and 'AnonymousEmail'."
+result_df = augini.augment_single(result_df, 'AnonymousProfile', custom_prompt=anonymize_prompt)
+
+print(result_df)
+```
+
+## Contributing
+
+We welcome contributions to enhance Augini! Feel free to open issues and submit pull requests on our GitHub repository.

augini/__init__.py

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+from .core import Augini
+
+__version__ = "0.1.0"
+__all__ = ["Augini"]