web2json 0.0.11

Convert web content into JSON using local Ollama LLM

web2json

web2json converts web content into structured JSON using a local Ollama server. It exposes a simple command line interface.

This repository began from code by abdo-Mansour and was adapted for use at the NOAA Global Systems Laboratory.

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt
   

3. Optionally set OLLAMA_HOST and OLLAMA_MODEL to point to your Ollama instance and model.

Command line usage

Run the CLI module with the content to process and your schema definition. The tool can also crawl multiple pages from a starting URL:

python -m web2json.cli --schema SCHEMA [--url] [--crawl] [--max_pages N] [--output FILE] CONTENT

• CONTENT can be a URL or raw text.
• --url tells the tool to treat CONTENT as a URL.
• --schema accepts a JSON Schema definition directly or the path to a JSON file containing it.
• When loading a schema from a file, an optional prompt string may be included to append additional instructions for the language model.
• The file may also contain a postprocess section with link_patterns that map field names to regular expressions and optional css_selectors for extracting values using CSS paths. These settings help fill in missing data based on the cleaned HTML.
• --crawl treats the content as a starting URL and processes each discovered page.
• --max_pages limits how many pages are crawled when using --crawl (default: 10).
• --debug prints the preprocessed content and other intermediate information to stderr.
• --output writes the resulting JSON to FILE instead of only printing to stdout.
• When a URL is provided, relative links in the page are converted to absolute URLs so they can be extracted correctly. The page URL itself is assigned to the url field if that key exists in the schema. Missing URLs can be filled using regex patterns in the post-processor when provided.
• Character encoding is determined automatically when downloading pages so accented characters are preserved correctly.
• If your schema defines a content field, the CLI removes common header, footer and navigation sections (including the official U.S. government banner) so that field only contains the main page body.

Example:

python -m web2json.cli https://example.com --url --schema '{"properties": {"title": {"type": "string", "description": "Page title"}}}'

You can place the schema in a file instead. For example schema.json:

{
  "properties": {"title": {"type": "string"}},
  "postprocess": {
    "link_patterns": {"ftp_download": "(ftp://\\S+)"},
    "css_selectors": {"preview": {"selector": "a.preview", "attr": "href"}}
  }
}

Run the CLI using that file:

python -m web2json.cli https://example.com --url --schema schema.json

To crawl and process multiple pages under https://example.com/docs/:

python -m web2json.cli https://example.com/docs/ --crawl --schema '{"properties": {"title": {"type": "string"}}}'

The extracted JSON is printed to standard output. Unicode characters are preserved so accent marks appear correctly. Any schema validation errors are reported to standard error. When --debug is used, intermediate output such as the cleaned HTML is also sent to standard error.

Library usage

The pipeline components are exposed as Python classes so you can build custom workflows.

from web2json.cli import parse_schema_input
from web2json.preprocessor import BasicPreprocessor
from web2json.postprocessor import PostProcessor
from web2json.pipeline import Pipeline
from web2json.ai_extractor import OllamaLLMClient

schema_json = '{"properties": {"title": {"type": "string"}, "content": {"type": "string"}}}'
schema = parse_schema_input(schema_json)
# Exclude header, footer and navigation markup when cleaning HTML
pre = BasicPreprocessor(config={"remove_boilerplate": True})
llm = OllamaLLMClient()
post = PostProcessor()
pipe = Pipeline(pre, llm, post)
result = pipe.run("<h1>Title</h1>", False, schema)

Regex link_patterns and css_selectors can be supplied for special cases where the language model misses links or other values.

The post-processor also applies simple heuristics to recover categories, keywords, and notable features directly from the cleaned HTML. These values replace the model output when they differ from the page content.

Code overview

1. Preprocessor - cleans and normalizes HTML or text input. When remove_boilerplate is enabled, common header, footer and navigation elements (like the U.S. government banner) are stripped before text extraction. The CLI turns this setting on automatically if your schema includes a content field.
2. AIExtractor - sends a prompt to the LLM and returns the raw JSON text.
3. PostProcessor - repairs malformed JSON and adds missing URLs.

These pieces are wired together by the Pipeline class and driven by the CLI script.

Running tests

Install pytest and run the suite:

pip install -r requirements.txt
pip install pytest
pytest

Tests also run automatically through GitHub Actions on every push and pull request.

Additional tests

The test suite now covers the CLI utilities as well as core components. Additional tests live under tests/ and exercise:

• The AIExtractor prompt formatting logic.
• Error handling in PostProcessor.process when invalid JSON is returned.
• The _fetch_content method in BasicPreprocessor.
• run_pipeline success and error scenarios.
• Pipeline operation with a mocked LLM.