Metadata-Version: 2.4
Name: hamtaa-texttools
Version: 1.1.17
Summary: 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>, Zareshahi <a.zareshahi1377@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: pydantic>=2.0.0
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 both **sync (`TheTool`)** and **async (`AsyncTheTool`)** APIs for maximum flexibility.

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

---

## ✨ Features

TextTools provides a rich collection of high-level NLP utilities,
Each tool is designed to work with structured outputs (JSON / Pydantic).

- **`categorize()`** - Classifies text into given categories (You have to create a category tree)
- **`extract_keywords()`** - Extracts keywords from text
- **`extract_entities()`** - Named Entity Recognition (NER) system
- **`is_question()`** - Binary detection of whether input is a question
- **`text_to_question()`** - Generates questions from text
- **`merge_questions()`** - Merges multiple questions with different modes
- **`rewrite()`** - Rewrites text with different wording/meaning
- **`subject_to_question()`** - Generates questions about a specific subject
- **`summarize()`** - Text summarization
- **`translate()`** - Text translation between languages
- **`propositionize()`** - Convert text to atomic independence meaningful sentences 
- **`run_custom()`** - Allows users to define a custom tool with an arbitrary BaseModel

---

## ⚙️ `with_analysis`, `logprobs`, `output_lang`, `user_prompt`, `temperature`, `validator` and `priority` parameters

TextTools provides several optional flags to customize LLM behavior:

- **`with_analysis (bool)`** → Adds a reasoning step before generating the final output.
**Note:** This doubles token usage per call because it triggers an additional LLM request.

- **`logprobs (bool)`** → 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.  
**Note:** This feature works if it's supported by the model.

- **`output_lang (str)`** → 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 (str)`** → 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.

- **`temperature (float)`** → Determines how creative the model should respond. Takes a float number from `0.0` to `2.0`.

- **`validator (Callable)`** → Forces TheTool to validate the output result based on your custom validator. Validator should return a bool (True if there were no problem, False if the validation fails.) If the validator fails, TheTool will retry to get another output by modifying `temperature`. You can specify `max_validation_retries=<N>` to change the number of retries.

- **`priority (int)`** → Task execution priority level. Higher values = higher priority. Affects processing order in queues.
**Note:** This feature works if it's supported by the model and vLLM.

**Note:** There might be some tools that don't support some of the parameters above.

---

## 🧩 ToolOutput

Every tool of `TextTools` returns a `ToolOutput` object which is a BaseModel with attributes:
- **`result: Any`** → The output of LLM
- **`analysis: str`** → The reasoning step before generating the final output
- **`logprobs: list`** → Token-level probabilities for the generated output 
- **`process: str`** → The tool name which processed the input
- **`processed_at: datetime`** → The process time
- **`execution_time: float`** → The execution time (seconds)
- **`errors: list[str]`** → Any error that have occured during calling LLM

**Note:** You can use `repr(ToolOutput)` to see details of your ToolOutput.

---

## 🚀 Installation

Install the latest release via PyPI:

```bash
pip install -U hamtaa-texttools
```

---

## 🧨 Sync vs Async
| Tool         | Style   | Use case                                    |
|--------------|---------|---------------------------------------------|
| `TheTool`    | Sync    | Simple scripts, sequential workflows        |
| `AsyncTheTool` | Async | High-throughput apps, APIs, concurrent tasks |

---

## ⚡ Quick Start (Sync)

```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
the_tool = TheTool(client=client, model=model)

# Example: Question Detection
detection = the_tool.is_question("Is this project open source?", logprobs=True, top_logprobs=2)
print(detection.result)
print(detection.logprobs)
# Output: True + logprobs

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

---

## ⚡ Quick Start (Async)

```python
import asyncio
from openai import AsyncOpenAI
from texttools import AsyncTheTool

async def main():
    # Create your AsyncOpenAI client
    async_client = AsyncOpenAI(base_url="your_url", api_key="your_api_key")

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

    # Create an instance of AsyncTheTool
    async_the_tool = AsyncTheTool(client=async_client, model=model)
    
    # Example: Async Translation and Keyword Extraction
    translation_task = async_the_tool.translate("سلام، حالت چطوره؟", target_language="English")
    keywords_task = async_the_tool.extract_keywords("Tomorrow, we will be dead by the car crash")

    (translation, keywords) = await asyncio.gather(translation_task, keywords_task)
    print(translation.result)
    print(keywords.result)

asyncio.run(main())
```

---

## 👍 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  

---

## 🔍 Logging

TextTools uses Python's standard `logging` module. The library's default logger level is `WARNING`, so if you want to modify it, follow instructions:


```python
import logging

# Default: warnings and errors only
logging.basicConfig(level=logging.WARNING)

# Debug everything (verbose)
logging.basicConfig(level=logging.DEBUG)

# Complete silence
logging.basicConfig(level=logging.CRITICAL)
```

---

## 📚 Batch Processing

Process large datasets efficiently using OpenAI's batch API.

## ⚡ Quick Start (Batch)

```python
from pydantic import BaseModel
from texttools import BatchJobRunner, BatchConfig

# Configure your batch job
config = BatchConfig(
    system_prompt="Extract entities from the text",
    job_name="entity_extraction",
    input_data_path="data.json",
    output_data_filename="results.json",
    model="gpt-4o-mini"
)

# Define your output schema
class Output(BaseModel):
    entities: list[str]

# Run the batch job
runner = BatchJobRunner(config, output_model=Output)
runner.run()
```

---

## 🤝 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.
