2installazioni

v0.1.14

llm-html

Habilidad de CMDOP — instale y utilice mediante agente de CMDOP:

Bash

cmdop-skill install llm-html

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Optimización de HTML para LLM: extracción de hidratación, presupuestos de tokens, múltiples formatos de salida.

Instalación

Bash

cpip install llm-html

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Inicio rápido

Python

from llm_html import HTMLCleaner, CleanerConfig, OutputFormat

# Limpieza básica

cleaner = HTMLCleaner()
result = cleaner.clean(html)
print(f'Reducción: {result.stats.reduction_percent}%')

# Hidratación primero (extrae datos de SSR de Next.js, Nuxt, etc.)
if result.hydration_data:
    data = result.hydration_data
else:
    cleaned = result.html

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Funciones de conveniencia

Python

from llm_html import clean, clean_to_json, clean_html, clean_for_llm

# Limpieza rápida
result = clean(html)

# Obten JSON si hay datos de SSR disponibles, de lo contrario, HTML limpio
data = clean_to_json(html)

# Pipeline con control completo
result = clean_html(html, max_tokens=5000)
result = clean_for_llm(html, output_format="markdown")

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Formatos de salida

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

Algoritmo D2Snap para objetivos de presupuesto de tokens:

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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Chunking semántico

Divide páginas grandes en trozos LLM:

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

Flata Web Components para visibilidad de LLM:

Python

from llm_html import flatten_shadow_dom

flat = flatten_shadow_dom(html)

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Ayudas

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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Licencia

MIT

Bash

cmdop-skill install llm-html

Bash

cpip install llm-html

Python

from llm_html import HTMLCleaner, CleanerConfig, OutputFormat

# Limpieza básica

cleaner = HTMLCleaner()
result = cleaner.clean(html)
print(f'Reducción: {result.stats.reduction_percent}%')

# Hidratación primero (extrae datos de SSR de 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

# Limpieza rápida
result = clean(html)

# Obten JSON si hay datos de SSR disponibles, de lo contrario, HTML limpio
data = clean_to_json(html)

# Pipeline con control completo
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

Instalación

Inicio rápido

Funciones de conveniencia

Formatos de salida

Downsampling

Chunking semántico

Shadow DOM

Ayudas

Licencia

Recensioni