sdkrouter-tools 0.1.2

SDK Router Tools — collection of utility tools for automation pipelines (telegram, logging, html cleaner, etc.)

sdkrouter-tools

SDK Router Tools — collection of utility tools for automation pipelines.

Installation

pip install sdkrouter-tools

Tools Included

• logging — Rich-powered logger with file persistence
• telegram — Rate-limited Telegram sender with priority queue
• html — HTML cleaner optimized for LLM pipelines

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1. Logging (Rich-powered)

Universal Python logger with Rich console output and file persistence.

from sdkrouter_tools import get_logger

log = get_logger(__name__)
log.info("Hello world")
log.error("Something failed", exc_info=True)

# With custom level
log = get_logger(__name__, level="DEBUG")
log.debug("Debug details: %s", data)

Features

• Rich console output with colors and formatting
• Automatic file logging (daily rotation)
• Auto-detects project root for log directory
• Rich tracebacks with local variables

Convenience Functions

from sdkrouter_tools.logging import debug, info, warning, error, critical

info("Processing started")
warning("Low memory")
error("Failed to connect")

Configuration

from sdkrouter_tools import setup_logging

setup_logging(
    level="DEBUG",           # Log level
    log_to_file=True,        # Write to file
    log_to_console=True,     # Output to console
    app_name="myapp",        # App name for log file
    rich_tracebacks=True,    # Rich exception formatting
)

---

2. Telegram Sender

Rate-limited Telegram message sender with priority queue support.

from sdkrouter_tools import TelegramSender, ParseMode

sender = TelegramSender(
    bot_token="YOUR_BOT_TOKEN",
    chat_id="YOUR_CHAT_ID",
)

sender.send_message("Hello from sdkrouter-tools!")
sender.send_message("<b>Bold</b> message", parse_mode=ParseMode.HTML)

Convenience Functions

from sdkrouter_tools.telegram import (
    send_error, send_success, send_warning,
    send_info, send_stats, send_alert,
)

send_error("Something went wrong!", {"details": "error info"})
send_success("Task completed!", {"items_processed": 100})
send_warning("Disk space low", {"available": "10GB"})
send_alert("Critical: Server down!", {"server": "prod-1"})

Environment Variables

export TELEGRAM_BOT_TOKEN="your_bot_token"
export TELEGRAM_CHAT_ID="your_chat_id"

Priority Queue

Messages are processed with rate limiting (20 msg/sec):

from sdkrouter_tools import MessagePriority

# CRITICAL (1), HIGH (2), NORMAL (3), LOW (4)
sender.send_message("Important!", priority=MessagePriority.HIGH)

Sending Files

sender.send_photo("/path/to/image.jpg", caption="Check this out!")
sender.send_document("/path/to/file.pdf", caption="Report attached")

Queue Management

from sdkrouter_tools import telegram_queue

stats = telegram_queue.get_stats()
telegram_queue.flush(timeout=10.0)  # Wait before script exit

---

3. HTML Cleaner

HTML cleaner optimized for LLM pipelines. Aggressive DOM cleaning, SSR hydration extraction, CSS class filtering, semantic chunking, and multiple output formats.

from sdkrouter_tools import HTMLCleaner, CleanerConfig, OutputFormat

cleaner = HTMLCleaner()
result = cleaner.clean(html)

print(result.output)
print(f"Reduction: {result.stats.reduction_percent}%")
print(f"Tokens: {result.stats.original_tokens} -> {result.stats.cleaned_tokens}")

Quick Functions

from sdkrouter_tools import clean, clean_to_json

# Quick clean
result = clean(html, max_tokens=5000, output_format="markdown")

# Get JSON if SSR data available, otherwise cleaned HTML
data = clean_to_json(html)

Configuration

from sdkrouter_tools import CleanerConfig, OutputFormat

config = CleanerConfig(
    max_tokens=5000,
    output_format=OutputFormat.MARKDOWN,  # HTML, MARKDOWN, AOM, XTREE
    filter_classes=True,
    class_threshold=0.5,
    try_hydration=True,
)

cleaner = HTMLCleaner(config)
result = cleaner.clean(html)

SSR Hydration Extraction

Extract structured data from server-side rendered pages:

from sdkrouter_tools.html import extract_hydration, detect_framework

framework = detect_framework(html)  # NEXTJS_APP, NUXT3, etc.

data = extract_hydration(html)
if data.has_data:
    products = data.page_props.get("products", [])

Supported: Next.js, Nuxt 2/3, SvelteKit, Remix, Gatsby, Qwik, Astro

CSS Class Filtering

from sdkrouter_tools.html import score_class, filter_classes, detect_css_framework

# Score classes by semantic relevance
result = score_class("product-card")  # High score
result = score_class("css-abc123")    # Low score (hash)

# Filter list of classes
classes = ["product-card", "css-abc123", "flex", "MuiButton-root"]
kept = filter_classes(classes, threshold=0.5)  # ["product-card"]

# Detect CSS framework
framework = detect_css_framework(html)  # "tailwind", "bootstrap", etc.

Output Formats

from sdkrouter_tools.html import to_markdown, to_aom_yaml, to_xtree
from bs4 import BeautifulSoup

soup = BeautifulSoup(html, "lxml")

# Markdown
md = to_markdown(soup)

# AOM YAML (Playwright-style aria snapshot)
yaml = to_aom_yaml(soup)
# - navigation:
#   - link "Home"
#   - link "Products"

# XTree (hierarchical tree)
tree = to_xtree(soup)
# ROOT
# ├─ nav#main-nav
# │  └─ a.nav-link → "Home"
# └─ main

Pipeline API

from sdkrouter_tools import clean_html, clean_for_llm

result = clean_html(html, max_tokens=5000, output_format="markdown")
output = clean_for_llm(html)  # Returns dict (SSR) or str (cleaned HTML)

Advanced Features

from sdkrouter_tools.html import (
    # Shadow DOM
    flatten_shadow_dom,
    # Downsampling
    downsample_html, estimate_tokens,
    # Semantic Chunking
    SemanticChunker, ChunkConfig,
    # Context Extraction
    extract_context, generate_selector,
    # Helpers
    json_to_toon, html_to_text, extract_links, extract_images,
)

---

Requirements

• Python >= 3.10
• rich >= 13.0
• pyTelegramBotAPI >= 4.14
• beautifulsoup4 >= 4.12
• lxml >= 5.3
• pydantic >= 2.10
• markdownify >= 0.14
• tiktoken >= 0.8

License

MIT