2installationer

v0.1.14

llm-html

CMDOP Färdighet — installera och använd via CMDOP-agent:

Bash

cmdop-skill install llm-html

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LLM-optimierad HTML-rengöring: hydreringsextraktion, tokenbudgetar, flera utdataformat.

Installera

Bash

pip install llm-html

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Snabbstart

Python

from llm_html import HTMLCleaner, CleanerConfig, OutputFormat

# Grundläggande rengöring
  cleaner = HTMLCleaner()
  result = cleaner.clean(html)
  print(f"Reduktion: {result.stats.reduction_percent}%")

# Hydrering-först (extraherar SSR-data från Next.js, Nuxt, etc.)
  if result.hydration_data:
    data = result.hydration_data
  else:
    cleaned = result.html

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Komfortfunktioner

Python

from llm_html import clean, clean_to_json, clean_html, clean_for_llm

# Snabb rengöring
  result = clean(html)

# Få JSON om SSR-data finns tillgängligt, annars rensad HTML
  data = clean_to_json(html)

# Pipelinen med full kontroll
  result = clean_html(html, max_tokens=5000)
  result = clean_for_llm(html, output_format="markdown")

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Utdataformat

Python

from llm_html import to_markdown, to_aom_yaml, to_xtree

md = to_markdown(html)
  aom = to_aom_yaml(html)
  xtree = to_xtree(html)

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Downsampling

Token-budget-mål med D2Snap-algoritmen:

Python

from llm_html import downsample_html, estimate_tokens

  tokens = estimate_tokens(html)
  if tokens > 10000:
    html = downsample_html(html, target_tokens=8000)

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Semantisk chunkning

Delar stora sidor i LLM-storlekade chunkar:

Python

from llm_html import SemanticChunker, ChunkConfig

  config = ChunkConfig(max_tokens=8000, max_items=20)
  chunker = SemanticChunker(config)
  result = chunker.chunk(soup)
  for chunk in result.chunks:
    process(chunk.html)

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Shadow DOM

Flattar Web Components för LLM-synlighet:

Python

from llm_html import flatten_shadow_dom

  flat = flatten_shadow_dom(html)

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Hjälpmedel

Python

from llm_html import html_to_text, extract_links, extract_images, json_to_toon

  text = html_to_text(html)
  links = extract_links(html, base_url="https://example.com")
  images = extract_images(html)
  toon = json_to_toon({"key": "value"})

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Licens

MIT

Bash

cmdop-skill install llm-html

Bash

pip install llm-html

Python

from llm_html import HTMLCleaner, CleanerConfig, OutputFormat

# Grundläggande rengöring
  cleaner = HTMLCleaner()
  result = cleaner.clean(html)
  print(f"Reduktion: {result.stats.reduction_percent}%")

# Hydrering-först (extraherar SSR-data från Next.js, Nuxt, etc.)
  if result.hydration_data:
    data = result.hydration_data
  else:
    cleaned = result.html

Python

from llm_html import clean, clean_to_json, clean_html, clean_for_llm

# Snabb rengöring
  result = clean(html)

# Få JSON om SSR-data finns tillgängligt, annars rensad HTML
  data = clean_to_json(html)

# Pipelinen med full kontroll
  result = clean_html(html, max_tokens=5000)
  result = clean_for_llm(html, output_format="markdown")

Python

from llm_html import to_markdown, to_aom_yaml, to_xtree

md = to_markdown(html)
  aom = to_aom_yaml(html)
  xtree = to_xtree(html)

Python

from llm_html import downsample_html, estimate_tokens

  tokens = estimate_tokens(html)
  if tokens > 10000:
    html = downsample_html(html, target_tokens=8000)

Python

from llm_html import SemanticChunker, ChunkConfig

  config = ChunkConfig(max_tokens=8000, max_items=20)
  chunker = SemanticChunker(config)
  result = chunker.chunk(soup)
  for chunk in result.chunks:
    process(chunk.html)

Python

from llm_html import flatten_shadow_dom

  flat = flatten_shadow_dom(html)

Python

from llm_html import html_to_text, extract_links, extract_images, json_to_toon

  text = html_to_text(html)
  links = extract_links(html, base_url="https://example.com")
  images = extract_images(html)
  toon = json_to_toon({"key": "value"})

llm-html

llm-html

Installera

Snabbstart

Komfortfunktioner

Utdataformat

Downsampling

Semantisk chunkning

Shadow DOM

Hjälpmedel

Licens

Recensioner