hamtaa-texttools 1.0.3

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 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.

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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).

• 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.

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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 hamta-texttools

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⚡ Quick Start

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?"

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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.