| Name | augini JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | A framework for generating synthetic tabular data using AI |
| upload_time | 2024-12-25 15:31:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | 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. |
| keywords |
augini
synthetic data
tabular data
ai
data generation
openai
openrouter
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
openai
pandas
tqdm
numpy
nest_asyncio
pydantic
sentence-transformers
spacy
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/augini) [](https://pepy.tech/project/augini)
# augini: AI-Powered Data Assistant
<p align="center">
<img src="img/logo_augini.png" alt="augini logo" width="200"/>
</p>
`augini` is an AI-powered data assistant that helps you understand, augment, and transform your tabular data. Built with state-of-the-art language models, it provides an intuitive chat interface and powerful data manipulation capabilities.
## Key Features
### 🤖 Interactive Data Chat
Have natural conversations with your data using `augini`'s chat interface:
```python
from augini import Augini
import pandas as pd
# Initialize with your preferred model
augini = Augini(api_key="your-api-key", model="gpt-4o-mini")
# Load your data
df = pd.read_csv("your_data.csv")
# Start chatting with your data - properly display markdown responses
from IPython.display import display, Markdown
response = augini.chat("What are the main patterns in this dataset?", df)
display(Markdown(response))
# Ask follow-up questions
response = augini.chat("Can you analyze the correlation between age and income?", df)
display(Markdown(response))
```
### 🔄 Intelligent Data Augmentation
Enhance your datasets with AI-generated features:
```python
# Add synthetic features based on existing data
result_df = augini.augment_columns(df, ['occupation', 'interests', 'personality_type'])
# Generate custom features with specific prompts
custom_prompt = """
Based on the person's age and location, suggest:
1. A likely income bracket
2. Preferred shopping categories
3. Travel preferences
Respond with a JSON object with keys 'income_bracket', 'shopping_preferences', 'travel_style'.
"""
enriched_df = augini.augment_columns(df,
['income_bracket', 'shopping_preferences', 'travel_style'],
custom_prompt=custom_prompt
)
```
### 🔒 Data Anonymization
Generate privacy-safe synthetic data while preserving statistical properties:
```python
# Define anonymization strategy
anonymize_prompt = """
Create an anonymized version that:
1. Replaces personal identifiers with synthetic data
2. Maintains statistical distributions
3. Preserves relationships between variables
Respond with a JSON object containing anonymized values.
"""
# Apply anonymization
anonymous_df = augini.augment_columns(df,
['name_anon', 'email_anon', 'address_anon'],
custom_prompt=anonymize_prompt
)
```
## Installation
```bash
pip install augini
```
## Quick Start
1. Get your API key from OpenAI or OpenRouter
2. Initialize Augini:
```python
# Using OpenAI
augini = Augini(api_key="your-api-key", model="gpt-4o-mini", use_openrouter=False)
# Using OpenRouter
augini = Augini(api_key="your-api-key", model="meta-llama/llama-3-8b-instruct", use_openrouter=True)
```
## Enterprise Solutions
For enterprise deployments, local installations, or custom solutions, contact us:
- Email: info@tabularis.ai
Raw data
{
"_id": null,
"home_page": null,
"name": "augini",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "augini, synthetic data, tabular data, AI, data generation, OpenAI, OpenRouter",
"author": null,
"author_email": "Vadim Borisov <vadim@tabularis.ai>",
"download_url": "https://files.pythonhosted.org/packages/69/2e/775fee3cc342162d732c2307979ba6f9145798c03b5f4fe746ea9bb535fb/augini-0.2.1.tar.gz",
"platform": null,
"description": "[](https://badge.fury.io/py/augini) [](https://pepy.tech/project/augini)\n\n# augini: AI-Powered Data Assistant\n\n<p align=\"center\">\n <img src=\"img/logo_augini.png\" alt=\"augini logo\" width=\"200\"/>\n</p>\n\n`augini` is an AI-powered data assistant that helps you understand, augment, and transform your tabular data. Built with state-of-the-art language models, it provides an intuitive chat interface and powerful data manipulation capabilities.\n\n## Key Features\n\n### \ud83e\udd16 Interactive Data Chat\n\nHave natural conversations with your data using `augini`'s chat interface:\n\n```python\nfrom augini import Augini\nimport pandas as pd\n\n# Initialize with your preferred model\naugini = Augini(api_key=\"your-api-key\", model=\"gpt-4o-mini\")\n\n# Load your data\ndf = pd.read_csv(\"your_data.csv\")\n\n# Start chatting with your data - properly display markdown responses\nfrom IPython.display import display, Markdown\n\nresponse = augini.chat(\"What are the main patterns in this dataset?\", df)\ndisplay(Markdown(response))\n\n# Ask follow-up questions\nresponse = augini.chat(\"Can you analyze the correlation between age and income?\", df)\ndisplay(Markdown(response))\n```\n\n### \ud83d\udd04 Intelligent Data Augmentation\n\nEnhance your datasets with AI-generated features:\n\n```python\n# Add synthetic features based on existing data\nresult_df = augini.augment_columns(df, ['occupation', 'interests', 'personality_type'])\n\n# Generate custom features with specific prompts\ncustom_prompt = \"\"\"\nBased on the person's age and location, suggest:\n1. A likely income bracket\n2. Preferred shopping categories\n3. Travel preferences\n\nRespond with a JSON object with keys 'income_bracket', 'shopping_preferences', 'travel_style'.\n\"\"\"\n\nenriched_df = augini.augment_columns(df, \n ['income_bracket', 'shopping_preferences', 'travel_style'],\n custom_prompt=custom_prompt\n)\n```\n\n### \ud83d\udd12 Data Anonymization\n\nGenerate privacy-safe synthetic data while preserving statistical properties:\n\n```python\n# Define anonymization strategy\nanonymize_prompt = \"\"\"\nCreate an anonymized version that:\n1. Replaces personal identifiers with synthetic data\n2. Maintains statistical distributions\n3. Preserves relationships between variables\n\nRespond with a JSON object containing anonymized values.\n\"\"\"\n\n# Apply anonymization\nanonymous_df = augini.augment_columns(df, \n ['name_anon', 'email_anon', 'address_anon'],\n custom_prompt=anonymize_prompt\n)\n```\n\n## Installation\n\n```bash\npip install augini\n```\n\n## Quick Start\n\n1. Get your API key from OpenAI or OpenRouter\n2. Initialize Augini:\n```python\n# Using OpenAI\naugini = Augini(api_key=\"your-api-key\", model=\"gpt-4o-mini\", use_openrouter=False)\n\n# Using OpenRouter\naugini = Augini(api_key=\"your-api-key\", model=\"meta-llama/llama-3-8b-instruct\", use_openrouter=True)\n```\n\n## Enterprise Solutions\n\nFor enterprise deployments, local installations, or custom solutions, contact us:\n- Email: info@tabularis.ai\n",
"bugtrack_url": null,
"license": "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.",
"summary": "A framework for generating synthetic tabular data using AI",
"version": "0.2.1",
"project_urls": {
"Bug Tracker": "https://github.com/tabularis-ai/augini/issues",
"Homepage": "https://github.com/tabularis-ai/augini"
},
"split_keywords": [
"augini",
" synthetic data",
" tabular data",
" ai",
" data generation",
" openai",
" openrouter"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "165b6bfeaa89b81a46558a30f5fafe5f0b3bd9247fbe3f017853231573b64390",
"md5": "51c64fecdcdde288e58285c414715e33",
"sha256": "abbf63dae38e0f1d4b725e410f034a4a80e549b8b867de47c2f3a950539a1fcd"
},
"downloads": -1,
"filename": "augini-0.2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "51c64fecdcdde288e58285c414715e33",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 11884,
"upload_time": "2024-12-25T15:31:29",
"upload_time_iso_8601": "2024-12-25T15:31:29.425156Z",
"url": "https://files.pythonhosted.org/packages/16/5b/6bfeaa89b81a46558a30f5fafe5f0b3bd9247fbe3f017853231573b64390/augini-0.2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "692e775fee3cc342162d732c2307979ba6f9145798c03b5f4fe746ea9bb535fb",
"md5": "2d35a5fb02a39d51e49d0d75affdff9b",
"sha256": "a8bbdb94f2e4d5a69d4e8767c691d708122b514fdbc59fc34eaa27e59d8c687d"
},
"downloads": -1,
"filename": "augini-0.2.1.tar.gz",
"has_sig": false,
"md5_digest": "2d35a5fb02a39d51e49d0d75affdff9b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 12979,
"upload_time": "2024-12-25T15:31:30",
"upload_time_iso_8601": "2024-12-25T15:31:30.977416Z",
"url": "https://files.pythonhosted.org/packages/69/2e/775fee3cc342162d732c2307979ba6f9145798c03b5f4fe746ea9bb535fb/augini-0.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-25 15:31:30",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tabularis-ai",
"github_project": "augini",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "openai",
"specs": [
[
">=",
"1.35.13"
]
]
},
{
"name": "pandas",
"specs": []
},
{
"name": "tqdm",
"specs": []
},
{
"name": "numpy",
"specs": [
[
"<",
"2.0.0"
]
]
},
{
"name": "nest_asyncio",
"specs": []
},
{
"name": "pydantic",
"specs": []
},
{
"name": "sentence-transformers",
"specs": [
[
">=",
"2.2.0"
]
]
},
{
"name": "spacy",
"specs": [
[
">=",
"3.5.0"
]
]
}
],
"lcname": "augini"
}
