Metadata-Version: 2.4
Name: hamtaa-texttools
Version: 1.1.22
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
        
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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 **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.

**Note:** Most features of `texttools` are reliable when you use `google/gemma-3n-e4b-it` model.

---

## ✨ Features

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

- **`categorize()`** - Classifies text into given categories
- **`extract_keywords()`** - Extracts keywords from the text
- **`extract_entities()`** - Named Entity Recognition (NER) system
- **`is_question()`** - Binary question detection
- **`text_to_question()`** - Generates questions from text
- **`merge_questions()`** - Merges multiple questions into one
- **`rewrite()`** - Rewrites text in a diffrent way
- **`subject_to_question()`** - Generates questions about a specific subject
- **`summarize()`** - Text summarization
- **`translate()`** - Text translation
- **`propositionize()`** - Convert text to atomic independence meaningful sentences 
- **`check_fact()`** - Check whether a statement is relevant to the source text
- **`run_custom()`** - Allows users to define a custom tool with an arbitrary BaseModel

---

## 🚀 Installation

Install the latest release via PyPI:

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

---

## 📊 Tool Quality Tiers

| Status | Meaning | Tools | Use in Production? |
|--------|---------|----------|-------------------|
| **✅ Production** | Evaluated, tested, stable. | `categorize()` (list mode), `extract_keywords()`, `extract_entities()`, `is_question()`, `text_to_question()`, `merge_questions()`, `rewrite()`, `subject_to_question()`, `summarize()`, `run_custom()` | **Yes** - ready for reliable use. |
| **🧪 Experimental** | Added to the package but **not fully evaluated**. Functional, but quality may vary. | `categorize()` (tree mode), `translate()`, `propositionize()`, `check_fact()` | **Use with caution** - outputs not yet validated. |

---

## ⚙️ `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.

- **`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.

- **`user_prompt: str`** → Allows you to inject a custom instruction or 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 (Experimental)`** → Forces TheTool to validate the output result based on your custom validator. Validator should return a boolean. If the validator fails, TheTool will retry to get another output by modifying `temperature`. You can also specify `max_validation_retries=<N>`.

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

---

## 🧩 ToolOutput

Every tool of `TextTools` returns a `ToolOutput` object which is a BaseModel with attributes:
- **`result: Any`**
- **`analysis: str`**
- **`logprobs: list`**
- **`errors: list[str]`**
- **`ToolOutputMetadata`** →
    - **`tool_name: str`**
    - **`processed_at: datetime`**
    - **`execution_time: float`**

**Note:** You can use `repr(ToolOutput)` to print your output with all the details.

---

## 🧨 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

client = OpenAI(base_url = "your_url", API_KEY = "your_api_key")
model = "model_name"

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

detection = the_tool.is_question("Is this project open source?")
print(repr(detection))
```

---

## ⚡ Quick Start (Async)

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

async def main():
    async_client = AsyncOpenAI(base_url="your_url", api_key="your_api_key")
    model = "model_name"

    async_the_tool = AsyncTheTool(client=async_client, model=model)
    
    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(repr(translation))
    print(repr(keywords))

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  

---

## 📚 Batch Processing

Process large datasets efficiently using OpenAI's batch API.

## ⚡ Quick Start (Batch Runner)

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

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"
)

class Output(BaseModel):
    entities: list[str]

runner = BatchRunner(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.
