📦 Parsera
Lightweight Python library for scraping websites with LLMs. You can test it on Parsera website.
Why Parsera?
Because it's simple and lightweight. With interface as simple as:
scraper = Parsera()
result = scraper.run(url=url, elements=elements)
Table of Contents
- Installation
- Documentation
- Basic usage
- Running with Jupyter Notebook
- Running with CLI
- Running in Docker
Installation
pip install parsera
playwright install
Documentation
Check out documentation to learn more about other features, like running custom models and playwright scripts.
Basic usage
First, set up PARSERA_API_KEY env variable (If you want to run custom LLM see Custom Models).
You can do this from python with:
import os
os.environ["PARSERA_API_KEY"] = "YOUR_PARSERA_API_KEY_HERE"
Next, you can run a basic version:
from parsera import Parsera
url = "https://news.ycombinator.com/"
elements = {
"Title": "News title",
"Points": "Number of points",
"Comments": "Number of comments",
}
scraper = Parsera()
result = scraper.run(url=url, elements=elements)
result variable will contain a json with a list of records:
[
{
"Title":"Hacking the largest airline and hotel rewards platform (2023)",
"Points":"104",
"Comments":"24"
},
...
]
There is also arun async method available:
result = await scrapper.arun(url=url, elements=elements)
Running with Jupyter Notebook:
Either place this code at the beginning of your notebook:
import nest_asyncio
nest_asyncio.apply()
Or instead of calling run method use async arun.
Running with CLI
Before you run Parsera as command line tool don't forget to put your OPENAI_API_KEY to env variables or .env file
Usage
You can configure elements to parse using JSON string or FILE.
Optionally, you can provide FILE to write output and amount of SCROLLS, that you want to do on the page
python -m parsera.main URL {--scheme '{"title":"h1"}' | --file FILENAME} [--scrolls SCROLLS] [--output FILENAME]
Running in Docker
In case of issues with your local environment you can run Parsera with Docker, see documentation.