Metadata-Version: 2.4
Name: hamtaa-texttools
Version: 1.0.3
Summary: TextTools is a high-level NLP toolkit built on top of modern LLMs.
Author-email: Tohidi <the.mohammad.tohidi@gmail.com>, Montazer <montazerh82@gmail.com>, Givechi <mohamad.m.givechi@gmail.com>, MoosaviNejad <erfanmoosavi84@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Hamtaa
        
        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: nlp,llm,text-processing,openai
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: openai==1.97.1
Requires-Dist: PyYAML>=6.0
Dynamic: license-file

# TextTools

## 📌 Overview

**TextTools** is a high-level **NLP toolkit** built on top of modern **LLMs**.  

It provides ready-to-use utilities for **translation, question detection, keyword extraction, categorization, NER extractor, and more** — designed to help you integrate AI-powered text processing into your applications with minimal effort.

**Thread Safety:** All methods in TheTool are thread-safe, allowing concurrent usage across multiple threads without conflicts.

---

## ✨ Features

TextTools provides a rich collection of high-level NLP utilities built on top of LLMs.  
Each tool is designed to work out-of-the-box with structured outputs (JSON / Pydantic).

- **Categorizer** → Zero-finetuning text categorization for fast, scalable classification.  
- **Keyword Extractor** → Identify the most important keywords in a text.  
- **Question Merger** → Merge the provided questions, preserving all the main points 
- **NER (Named Entity Recognition) Extractor** → Extract people, places, organizations, and other entities.  
- **Question Detector** → Determine whether a text is a question or not.  
- **Question Generator From Text** → Generate high-quality, context-relevant questions from provided text.
- **Question Generator From Subject** → Generate high-quality, context-relevant questions from a subject.
- **Rewriter** → Rewrite text while preserving meaning or without it.
- **Summarizer** → Condense long passages into clear, structured summaries. 
- **Translator** → Translate text across multiple languages, with support for custom rules.

---

## ⚙️ with_analysis, logprobs, output_lang, and user_prompt parameters

TextTools provides several optional flags to customize LLM behavior:

- **`with_analysis=True`** → Adds a reasoning step before generating the final output. Useful for debugging, improving prompts, or understanding model behavior.  
  ⚠️ Note: This doubles token usage per call because it triggers an additional LLM request.

- **`logprobs=True`** → Returns token-level probabilities for the generated output. You can also specify `top_logprobs=<N>` to get the top N alternative tokens and their probabilities.  

- **`output_lang="en"`** → Forces the model to respond in a specific language. The model will ignore other instructions about language and respond strictly in the requested language.

- **`user_prompt="..."`** → Allows you to inject a custom instruction or prompt into the model alongside the main template. This gives you fine-grained control over how the model interprets or modifies the input text.

All these flags can be used individually or together to tailor the behavior of any tool in **TextTools**.

---

## 🚀 Installation

Install the latest release via PyPI:

```bash
pip install -U hamta-texttools
```

---

## ⚡ Quick Start

```python
from openai import OpenAI

from texttools import TheTool

# Create your OpenAI client
client = OpenAI(base_url = "your_url", API_KEY = "your_api_key")

# Specify the model
model = "gpt-4o-mini"

# Create an instance of TheTool
# ⚠️ Note: Enabling `with_analysis=True` provides deeper insights but incurs additional LLM calls and token usage.
the_tool = TheTool(client = client, model = model, with_analysis = True)

# Example: Question Detection
print(the_tool.detect_question("Is this project open source?")["result"])
# Output: True

# Example: Translation
print(the_tool.translate("سلام، حالت چطوره؟", target_language="English")["result"])
# Output: "Hi! How are you?"
```

---

## 📚 Use Cases

Use **TextTools** when you need to:

- 🔍 **Classify** large datasets quickly without model training  
- 🌍 **Translate** and process multilingual corpora with ease  
- 🧩 **Integrate** LLMs into production pipelines (structured outputs)  
- 📊 **Analyze** large text collections using embeddings and categorization  
- 👍 **Automate** common text-processing tasks without reinventing the wheel  

---

## 🤝 Contributing

Contributions are welcome!  
Feel free to **open issues, suggest new features, or submit pull requests**.  

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
