extracthero 0.1.5.1

LLM-driven extraction from raw HTML and website screenshots, preserving spatial context with optional validation.

extracthero

Extract accurate, structured facts from messy real-world content — raw HTML, screenshots, PDFs, JSON blobs or plain text — with almost zero compromise.

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Why extracthero?

Pain-point	extracthero's answer
DOM spaghetti (ads, nav bars, JS widgets) pollutes extraction. Markdown converters drop dynamic/JS-rendered elements.	We use a rule-based DomReducer to remove non-content related HTML tags. This process is custom tailored to not destroy any structural data including tables etc. In general this gives us 20% reduction in size. Markdown converting operations are too vague to trust for prod and they usually dismiss useful data.
Needle in haystack is common problem. If you overwork a LLM, it can hallucinate or start outputting unstructured garbage which breaks production.	We define extraction in 2 phases. First phase is context aware filtering, and second phase is parsing this filtered data. Since LLM processes less data, the attention mechanism works better as well and more accurate results.
LLM prompts that just say "extract price" are brittle because in real life scenarios extraction logic is more complex and dependent on other variables.	Extracthero asks you to fill WhatToRetain specifications that include the field's name, desc, and optional text_rules, so the LLM knows the full context and returns sniper-accurate results.
In real life, source data comes in different formats (JSON, strings, dicts, HTML) and each requires different optimization strategies.	ExtractHero handles each data format intelligently. You can input JSON and if it can extract keys directly, it will use a fast-path. If it doesn't find what you need, you can use fallback mechanisms to route it to LLM processing for extraction.
Post-hoc validation is messy.	Regex/type guards live inside each WhatToRetain; a failed field flips success=False, so you can retry or send to manual review.

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Key ideas

1 Schema-first extraction

from extracthero import WhatToRetain

price_spec = WhatToRetain(
    name="price",
    desc="currency-prefixed current product price",
    regex_validator=r"€\d+\.\d{2}",
    text_rules=[
        "Ignore crossed-out promotional prices",
        "Return the live price only"
    ],
    example="€49.99"
)