semvec/pypi-packages huggingface dataset:
llm-friendly 0.1.1
llm-friendly converts structured data to LLM-friendly text.
Requires Python: >=3.10,<4.0

# llm-friendly

![MIT Licence](https://img.shields.io/badge/licence-MIT-red)
[![pytest](https://github.com/GovTechSG/llm-friendly/actions/workflows/pytest.yml/badge.svg)](https://github.com/GovTechSG/llm-friendly/actions/workflows/pytest.yml)
![PyPI](https://img.shields.io/pypi/v/llm-friendly)
![PyPI - Downloads](https://img.shields.io/pypi/dm/llm-friendly)


`llm-friendly` converts API responses to LLM-friendly text.

```mermaid
flowchart LR
    APIResponse["API Response \n or \n JSON body"]
    LLMFriendly["llm-friendly"]
    LLM["Large-Language Model \n (e.g. OpenAI or Llama 2"]
    
    APIResponse -- JSON --> LLMFriendly -- string --> LLM
```

## Currently Supported Source APIs

 - Amazon Web Services (AWS)
   - Textract

[//]: # ( - Azure AI)

[//]: # (   - Vision)

[//]: # ( - Google Cloud Platform &#40;GCP&#41;)

[//]: # (   - Vision)

## Installation

### Latest Stable

```shell
pip install llm-friendly
```

### Latest Development Version

```shell
pip install git+https://github.com/GovTechSG/llm-friendly.git
```

## Usage

Convert your tables into CSV, JSON or Markdown formats.

```python
from llm_friendly.aws import textract

textract_response = {...}
text_content = textract.to_llm_output(textract_response, mode=textract.MODE_CSV) # MODE_JSON or MODE_MARKDOWN
print(text_content)
```

## Tests

```shell
pytest
```

## Similar Projects

- [Unstructured](https://github.com/Unstructured-IO/unstructured/): Supports primarily unstructured formats (Word, PDF, Excel, Email etc)
csklearing 0.0.2
自己平时会使用的一些统计学和数学模型,目前有两个改进的朴素贝叶斯算法和一个TOPSIS
Keywords: Machine Learning,Python


# 使用说明

自己平时会使用的一些统计学和数学模型,目前有两个改进的朴素贝叶斯算法和一个TOPSIS

GitHub: [https://github.com/CheckeyZerone/Checkey-Sklearn](https://github.com/CheckeyZerone/Checkey-Sklearn)

PyPI: [https://pypi.org/project/csklearing/](https://pypi.org/project/csklearing/)

## 版权声明
    Checkey-Sklearn
    Copyright (C) 2023  CheckeyZerone

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Affero General Public License as published
    by the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Affero General Public License for more details.

    You should have received a copy of the GNU Affero General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.

## 安装方法
```commandline
pip install csklearn
```

## 依赖的第三方模块包

```
numpy
pandas
scikit-learn
```

## 实现算法

- 朴素贝叶斯改进
- 熵权-TOPSIS模型

## 使用方法

```python
model = Model(*params)
model.fit(x_train[, y_train, params])
model.predict(x_test)  # or model.transform(x_test)
```