swarmauri_tool_textlength 0.8.2

Text Length Tool for Swarmauri

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Swarmauri Tool · Text Length

A Swarmauri-ready helper that measures text length in characters, words, and sentences using NLTK tokenization. Drop it into content pipelines, moderation bots, or editorial agents to monitor message size and cadence.

• Counts characters (excluding spaces) for quick size checks.
• Uses NLTK tokenizers to calculate word and sentence totals accurately.
• Returns a structured dictionary suitable for logging, analytics, or conversational outputs.

Requirements

• Python 3.10 – 3.13.
• nltk with the punkt_tab resource available (downloaded automatically on import).
• Core Swarmauri dependencies (swarmauri_base, swarmauri_standard, pydantic).

Installation

Pick the packaging tool that fits your workflow; each command installs dependencies.

pip

pip install swarmauri_tool_textlength

Poetry

poetry add swarmauri_tool_textlength

uv

# Add to the current project and update uv.lock
uv add swarmauri_tool_textlength

# or install into the active environment without editing pyproject.toml
uv pip install swarmauri_tool_textlength

Tip: When building containers or offline environments, pre-fetch NLTK data with python -m nltk.downloader punkt_tab to avoid runtime downloads.

Quick Start

from swarmauri_tool_textlength import TextLengthTool

text = "This is a simple test sentence."

length_tool = TextLengthTool()
result = length_tool(text)

print(result)
# {
#   'num_characters': 26,
#   'num_words': 7,
#   'num_sentences': 1
# }

Usage Scenarios

Enforce Message Length in Moderation Bots

from swarmauri_tool_textlength import TextLengthTool

length_checker = TextLengthTool()
message = """Please keep replies under 50 words so the queue stays manageable."""

metrics = length_checker(message)
if metrics["num_words"] > 50:
    raise ValueError("Message too long for moderation queue")

Provide Real-Time Feedback in a Swarmauri Agent

from swarmauri_core.agent.Agent import Agent
from swarmauri_core.messages.HumanMessage import HumanMessage
from swarmauri_standard.tools.registry import ToolRegistry
from swarmauri_tool_textlength import TextLengthTool

registry = ToolRegistry()
registry.register(TextLengthTool())
agent = Agent(tool_registry=registry)

message = HumanMessage(content="Analyze how long this paragraph is compared to our guideline.")
response = agent.run(message)
print(response)

Batch Audit Documentation for Sentence Length

from pathlib import Path
from swarmauri_tool_textlength import TextLengthTool

length_tool = TextLengthTool()

for doc in Path("docs").rglob("*.md"):
    metrics = length_tool(doc.read_text(encoding="utf-8"))
    print(f"{doc}: {metrics['num_sentences']} sentences, {metrics['num_words']} words")

Scan an entire documentation set to identify sprawling sections or under-documented topics.

Troubleshooting

• LookupError: Resource punkt_tab not found – Run python -m nltk.downloader punkt_tab ahead of time, especially in air-gapped deployments.
• Unexpected character counts – The tool excludes spaces; adjust the implementation if you need raw length including whitespace.
• Non-English text – NLTK’s default tokenizers target English. Swap in language-specific tokenizers if needed.

License

swarmauri_tool_textlength is released under the Apache 2.0 License. See LICENSE for full details.