LLM-driven extraction from raw HTML and website screenshots, preserving spatial context with optional validation.
Project description
# extracthero
Now you can extract information from any data with almost zero compromise.
## Features
- **Multi-modal input**: ingest raw HTML strings, screenshot image files, or paired HTML+image inputs
- **Spatial context preservation**: maintain element positions to extract data that depends on layout or visual proximity
- **LLM-powered extraction**: plug in your favorite LLM (e.g. OpenAI, Anthropic Claude) for flexible schema-driven parsing
- **Optional validation**: enable built-in rules or custom validators to enforce types, ranges and required fields
- **Extensible pipeline**: hook into pre- and post-processing steps
## Installation
```bash
pip install extracthero
Quickstart
from extracthero import Extractor, ExtractConfig
# 1. Initialize with your LLM backend and schema
config = ExtractConfig(
llm_backend="openai",
prompt_template="Extract product titles and prices from the page",
validation_enabled=True
)
extractor = Extractor(config)
# 2a. Extract from raw HTML
html = "<html>…</html>"
result_html = extractor.extract_from_html(html)
print(result_html)
# 2b. Extract from a screenshot
result_img = extractor.extract_from_screenshot("page.png")
print(result_img)
# 2c. Extract using both HTML + screenshot (preserve layout)
result_combo = extractor.extract_from_both(html, "page.png")
print(result_combo)
Configuration
| Option | Type | Default | Description |
|---|---|---|---|
llm_backend |
str |
"openai" |
Which LLM to use (“openai”, “anthropic”, etc.) |
prompt_template |
str |
None |
Template guiding the LLM’s extraction instructions |
validation_enabled |
bool |
False |
Turn on built-in schema and rule validation |
ocr_engine |
str |
"tesseract" |
OCR engine for screenshot text extraction |
spatial_threshold |
float |
0.5 |
Minimum layout-overlap ratio to consider two elements related |
from extracthero import ExtractConfig
config = ExtractConfig(
llm_backend="anthropic",
validation_enabled=False,
spatial_threshold=0.7
)
Validation Logic
When validation_enabled=True, extracted fields are checked against:
- Type rules (e.g. string, number, date)
- Range checks (e.g. price ≥ 0, date within the last year)
- Presence (required vs. optional fields)
Customize or extend:
from extracthero.validation import Validator, FieldRule
# custom rule: price must be < 1 000 000
class PriceRule(FieldRule):
def validate(self, value):
return isinstance(value, (int, float)) and value < 1_000_000
config.custom_validators = {
"price": PriceRule()
}
CLI Interface
# Extract from HTML file
extracthero html input.html --schema product_schema.json --output out.json
# Extract from screenshot
extracthero image page.png --prompt "Get titles" --output out.json
# Combined mode
extracthero combo input.html page.png --output out.json
Use extracthero --help for full options and flags.
Examples
- E-commerce product scraper
- News article metadata extraction
- Invoice data capture with layout
See the examples/ folder for ready-to-run demos.
Contributing
- Fork the repo
- Create a feature branch (
git checkout -b feat/my-feature) - Implement & test
- Submit a pull request
Please follow our code style guide and write tests for new features.
Release history Release notifications | RSS feed
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