hamtaa-texttools 2.1.0

A high-level NLP toolkit built on top of modern LLMs.

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

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✨ Features

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

• categorize() - Classify text into given categories
• extract_keywords() - Extract keywords from the text
• extract_entities() - Perform Named Entity Recognition (NER)
• is_question() - Detect if the input is phrased as a question
• to_question() - Generate questions from the given text / subject
• merge_questions() - Merge multiple questions into one
• augment() - Rewrite text in different augmentations
• summarize() - Summarize the given text
• translate() - Translate text between languages
• propositionize() - Convert a text into atomic, independent, meaningful sentences
• is_fact() - Check whether a statement is a fact based on the source text
• run_custom() - Custom tool that can do almost anything

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🚀 Installation

Install the latest release via PyPI:

pip install -U hamtaa-texttools

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📊 Tool Quality Tiers

Status	Meaning	Tools	Safe for Production?
✅ Production	Evaluated and tested.	categorize(), extract_keywords(), extract_entities(), is_question(), to_question(), merge_questions(), augment(), summarize(), run_custom()	Yes - ready for reliable use.
🧪 Experimental	Added to the package but not fully evaluated.	translate(), propositionize(), is_fact()	Use with caution

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⚙️ Additional Parameters

• raise_on_error: bool → (TheTool/AsyncTheTool parameter) Raise errors (True) or return them in output (False). Default is True.

• 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 into the model alongside the main template.

• temperature: float → Determines how creative the model should respond. Takes a float number between 0.0 and 2.0.

• validator: Callable (Experimental) → Forces the tool to validate the output result based on your validator function. 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) → Affects processing order in queues.
  Note: This feature works if it's supported by the model and vLLM.

• timeout: float → Maximum time in seconds to wait for the response before raising a timeout error.
  Note: This feature is only available in AsyncTheTool.

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🧩 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

• Serialize output to JSON using the to_json() method.

• Verify operation success with the is_successful() method.

• Convert output to a dictionary with the to_dict() method.

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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 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(detection.to_json())

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⚡ Quick Start (Async)

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_lang="English")
    keywords_task = async_the_tool.extract_keywords("This open source project is great for processing large datasets!")

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

asyncio.run(main())

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✅ Use Cases

Use TextTools when you need to:

• 🔍 Classify large datasets quickly without model training
• 🧩 Integrate LLMs into production pipelines (structured outputs)
• 📊 Analyze large text collections using embeddings and categorization