hamtaa-texttools 1.0.7

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

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 extractor, and more — designed to help you integrate AI-powered text processing into your applications with minimal effort.

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

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✨ 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).

• categorize() - Classifies text into Islamic studies categories
• is_question() - Binary detection of whether input is a question
• extract_keywords() - Extracts keywords from text
• extract_entities() - Named Entity Recognition (NER) system
• summarize() - Text summarization
• 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
• translate() - Text translation between languages
• run_custom() - Allows users to define a custom tool with arbitrary BaseModel

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⚙️ 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.

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🚀 Installation

Install the latest release via PyPI:

pip install -U hamtaa-texttools

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Sync vs Async

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

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⚡ Quick Start (Sync)

from openai import OpenAI
from pydantic import BaseModel
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: You can give parameters to TheTool so that you don't need to give them to each tool
the_tool = TheTool(client=client, model=model, with_analysis=True, output_lang="English")

# 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

# Example: Translation
# Note: You can overwrite with_analysis if defined at TheTool
print(the_tool.translate("سلام، حالت چطوره؟", target_language="English", with_analysis=False)["result"])
# Output: "Hi! How are you?"

# Example: Custom Tool
# Note: Output model should only contain result key
# Everything else will be ignored
class Custom(BaseModel):
  result: list[list[dict[str, int]]]

custom_prompt = "Something"
custom_result = the_tool.run_custom(custom_prompt, Custom)
print(custom_result)

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⚡ Quick Start (Async)

import asyncio
from openai import AsyncOpenAI
from texttools import AsyncTheTool

async def main():
    # Create your async OpenAI 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
    the_tool = AsyncTheTool(client=async_client, model=model)

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

asyncio.run(main())

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📚 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

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🤝 Contributing

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

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License

This project is licensed under the MIT License - see the LICENSE file for details.