| Name | augini JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | AI-powered Python framework for tabular data enrichment and analysis using LLMs. Features include intelligent feature engineering, natural language data analysis, and AI agents for automated workflows. |
| upload_time | 2025-02-08 17:08:01 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License
Copyright (c) 2024 Vadim Borisov @ Tabularis AI
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 |
ai agents
augini
llm
data analysis
feature engineering
tabular data
data enrichment
natural language
data science
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
openai
pandas
tqdm
numpy
nest_asyncio
pydantic
PyYAML
python-dotenv
pytest
black
isort
mypy
pylint
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Augini 🤖
<p align="center">
<img src="docs/assets/images/logo_augini.png" alt="augini logo" width="200"/>
</p>
<div align="center">
[](https://badge.fury.io/py/augini)
[](https://pepy.tech/project/augini)
[](https://tabularis-ai.github.io/augini/)
[](https://discord.com/channels/1310217643520819251/)
[](https://x.com/tabularis_ai)
[](https://huggingface.co/tabularisai)
</div>
## 🎯 What is Augini?
Augini is an AI-powered Python framework for tabular data enrichment and analysis. It leverages Large Language Models (LLMs) to:
- Generate meaningful features from your data
- Provide natural language data analysis
- Create AI agents for automated data workflows
## 🚀 Quick Start
```bash
pip install augini
```
```python
from augini import DataEngineer, DataAnalyzer
import pandas as pd
# Sample customer data
df = pd.DataFrame({
'CustomerID': ['C001', 'C002'],
'Age': [25, 45],
'MonthlyCharges': [50.0, 75.0]
})
# Initialize with your API key (supports OpenAI, OpenRouter, Azure)
engineer = DataEngineer(
api_key="your-api-key",
model="gpt-4o-mini", # Use OpenRouter's GPT-4
base_url="https://openrouter.ai/api/v1" # Optional: use OpenRouter
)
# Generate customer insights
df = engineer.generate_features(
df=df,
new_feature_specs=[
{
'new_feature_name': 'CustomerSegment',
'new_feature_description': 'Classify customer segment based on age and spending',
'output_type': 'category',
'constraints': {'categories': ['Premium', 'Regular', 'Budget']}
},
{
'new_feature_name': 'ChurnRisk',
'new_feature_description': 'Calculate churn risk score (0-100)',
'output_type': 'float',
'constraints': {'min': 0, 'max': 100}
}
]
)
# Initialize analyzer for natural language insights
analyzer = DataAnalyzer(
api_key="your-api-key",
model="gpt-4o-mini",
enable_memory=True # Enable conversation context
)
# Fit data and ask questions
analyzer.fit(df)
insights = analyzer.chat("What patterns do you see in customer segments?")
print(insights)
```
## 🎁 Key Features
### 🔄 DataEngineer
- **Feature Generation**: Create meaningful features using AI
- **Data Augmentation**: Enrich datasets with synthetic data
- **Custom Constraints**: Control output formats and ranges
- **Batch Processing**: Handle large datasets efficiently
### 📊 DataAnalyzer
- **Natural Language Analysis**: Ask questions about your data
- **Pattern Detection**: Uncover hidden trends and correlations
- **Memory Context**: Build on previous analysis
- **Visualization Integration**: Generate plots and charts
### 🤖 AI Agents
- **Automated Workflows**: Create agents for repetitive tasks
- **Custom Behaviors**: Define agent goals and constraints
- **Chain Actions**: Connect multiple agents for complex workflows
## 🌐 Provider Agnostic
Augini works with multiple LLM providers:
- OpenAI
- OpenRouter
- Azure OpenAI
- Anthropic (coming soon)
## 🤝 Contributing
We welcome contributions!
## 📜 License
Augini is released under the [MIT License](LICENSE).
Raw data
{
"_id": null,
"home_page": null,
"name": "augini",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "AI agents, augini, LLM, data analysis, feature engineering, tabular data, data enrichment, natural language, data science, machine learning",
"author": null,
"author_email": "Vadim Borisov <vadim@tabularis.ai>",
"download_url": "https://files.pythonhosted.org/packages/ac/c6/f78e1561d307e23fd69f1ab82e01f7d7516223bb9b9b6f5508eae40cd6be/augini-0.3.3.tar.gz",
"platform": null,
"description": "# Augini \ud83e\udd16\n\n<p align=\"center\">\n <img src=\"docs/assets/images/logo_augini.png\" alt=\"augini logo\" width=\"200\"/>\n</p>\n\n<div align=\"center\">\n \n[](https://badge.fury.io/py/augini) \n[](https://pepy.tech/project/augini)\n[](https://tabularis-ai.github.io/augini/)\n[](https://discord.com/channels/1310217643520819251/)\n[](https://x.com/tabularis_ai)\n\n[](https://huggingface.co/tabularisai)\n\n</div>\n\n## \ud83c\udfaf What is Augini?\n\nAugini is an AI-powered Python framework for tabular data enrichment and analysis. It leverages Large Language Models (LLMs) to:\n- Generate meaningful features from your data\n- Provide natural language data analysis\n- Create AI agents for automated data workflows\n\n## \ud83d\ude80 Quick Start\n\n```bash\npip install augini\n```\n\n```python\nfrom augini import DataEngineer, DataAnalyzer\nimport pandas as pd\n\n# Sample customer data\ndf = pd.DataFrame({\n 'CustomerID': ['C001', 'C002'],\n 'Age': [25, 45],\n 'MonthlyCharges': [50.0, 75.0]\n})\n\n# Initialize with your API key (supports OpenAI, OpenRouter, Azure)\nengineer = DataEngineer(\n api_key=\"your-api-key\",\n model=\"gpt-4o-mini\", # Use OpenRouter's GPT-4\n base_url=\"https://openrouter.ai/api/v1\" # Optional: use OpenRouter\n)\n\n# Generate customer insights\ndf = engineer.generate_features(\n df=df,\n new_feature_specs=[\n {\n 'new_feature_name': 'CustomerSegment',\n 'new_feature_description': 'Classify customer segment based on age and spending',\n 'output_type': 'category',\n 'constraints': {'categories': ['Premium', 'Regular', 'Budget']}\n },\n {\n 'new_feature_name': 'ChurnRisk',\n 'new_feature_description': 'Calculate churn risk score (0-100)',\n 'output_type': 'float',\n 'constraints': {'min': 0, 'max': 100}\n }\n ]\n)\n\n# Initialize analyzer for natural language insights\nanalyzer = DataAnalyzer(\n api_key=\"your-api-key\",\n model=\"gpt-4o-mini\",\n enable_memory=True # Enable conversation context\n)\n\n# Fit data and ask questions\nanalyzer.fit(df)\ninsights = analyzer.chat(\"What patterns do you see in customer segments?\")\nprint(insights)\n```\n\n## \ud83c\udf81 Key Features\n\n### \ud83d\udd04 DataEngineer\n- **Feature Generation**: Create meaningful features using AI\n- **Data Augmentation**: Enrich datasets with synthetic data\n- **Custom Constraints**: Control output formats and ranges\n- **Batch Processing**: Handle large datasets efficiently\n\n### \ud83d\udcca DataAnalyzer\n- **Natural Language Analysis**: Ask questions about your data\n- **Pattern Detection**: Uncover hidden trends and correlations\n- **Memory Context**: Build on previous analysis\n- **Visualization Integration**: Generate plots and charts\n\n### \ud83e\udd16 AI Agents\n- **Automated Workflows**: Create agents for repetitive tasks\n- **Custom Behaviors**: Define agent goals and constraints\n- **Chain Actions**: Connect multiple agents for complex workflows\n\n## \ud83c\udf10 Provider Agnostic\n\nAugini works with multiple LLM providers:\n- OpenAI\n- OpenRouter\n- Azure OpenAI\n- Anthropic (coming soon)\n\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! \n\n## \ud83d\udcdc License\n\nAugini is released under the [MIT License](LICENSE).\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2024 Vadim Borisov @ Tabularis AI\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
"summary": "AI-powered Python framework for tabular data enrichment and analysis using LLMs. Features include intelligent feature engineering, natural language data analysis, and AI agents for automated workflows.",
"version": "0.3.3",
"project_urls": {
"Bug Tracker": "https://github.com/tabularis-ai/augini/issues",
"Discord": "https://discord.com/channels/1310217643520819251/",
"Documentation": "https://tabularis-ai.github.io/augini",
"Homepage": "https://github.com/tabularis-ai/augini"
},
"split_keywords": [
"ai agents",
" augini",
" llm",
" data analysis",
" feature engineering",
" tabular data",
" data enrichment",
" natural language",
" data science",
" machine learning"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "b759bac18f7a034525b259fa890719241e5b688c2123f1b9fe3aab1e3b39e9dc",
"md5": "f75f440162ccf0460ef3724b874873c3",
"sha256": "8944085bdfd2926be4f1890b5377055f1d2e972b8adcf0081c00c0545e75757b"
},
"downloads": -1,
"filename": "augini-0.3.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f75f440162ccf0460ef3724b874873c3",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 17293,
"upload_time": "2025-02-08T17:08:00",
"upload_time_iso_8601": "2025-02-08T17:08:00.209556Z",
"url": "https://files.pythonhosted.org/packages/b7/59/bac18f7a034525b259fa890719241e5b688c2123f1b9fe3aab1e3b39e9dc/augini-0.3.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "acc6f78e1561d307e23fd69f1ab82e01f7d7516223bb9b9b6f5508eae40cd6be",
"md5": "07f37908ff74b7e2f06b9b4599dfebcd",
"sha256": "3d188fce9d165b7ddedbc5a62a4767731a265ecd6147522ee607ae74e9e086ab"
},
"downloads": -1,
"filename": "augini-0.3.3.tar.gz",
"has_sig": false,
"md5_digest": "07f37908ff74b7e2f06b9b4599dfebcd",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 18926,
"upload_time": "2025-02-08T17:08:01",
"upload_time_iso_8601": "2025-02-08T17:08:01.922130Z",
"url": "https://files.pythonhosted.org/packages/ac/c6/f78e1561d307e23fd69f1ab82e01f7d7516223bb9b9b6f5508eae40cd6be/augini-0.3.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-08 17:08:01",
"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": [
[
">=",
"2.0.0"
]
]
},
{
"name": "tqdm",
"specs": [
[
">=",
"4.65.0"
]
]
},
{
"name": "numpy",
"specs": [
[
"<",
"2.0.0"
]
]
},
{
"name": "nest_asyncio",
"specs": [
[
">=",
"1.5.6"
]
]
},
{
"name": "pydantic",
"specs": [
[
">=",
"2.0.0"
]
]
},
{
"name": "PyYAML",
"specs": [
[
">=",
"6.0.0"
]
]
},
{
"name": "python-dotenv",
"specs": [
[
">=",
"1.0.0"
]
]
},
{
"name": "pytest",
"specs": [
[
">=",
"7.0.0"
]
]
},
{
"name": "black",
"specs": [
[
">=",
"22.0.0"
]
]
},
{
"name": "isort",
"specs": [
[
">=",
"5.0.0"
]
]
},
{
"name": "mypy",
"specs": [
[
">=",
"1.0.0"
]
]
},
{
"name": "pylint",
"specs": [
[
">=",
"2.17.0"
]
]
}
],
"lcname": "augini"
}
