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https://github.com/raznem/parsera
Lightweight library for scraping web-sites with LLMs
https://github.com/raznem/parsera
data-extraction llm opensource python scraping webscraping
Last synced: 15 days ago
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Lightweight library for scraping web-sites with LLMs
- Host: GitHub
- URL: https://github.com/raznem/parsera
- Owner: raznem
- License: gpl-2.0
- Created: 2024-08-12T13:04:33.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T14:12:16.000Z (about 1 month ago)
- Last Synced: 2024-12-08T15:19:12.773Z (about 1 month ago)
- Topics: data-extraction, llm, opensource, python, scraping, webscraping
- Language: Python
- Homepage: https://docs.parsera.org
- Size: 1.23 MB
- Stars: 911
- Watchers: 13
- Forks: 59
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
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README
# 📦 Parsera
[](https://parsera.org)
[](https://pepy.tech/project/parsera)
[](https://discord.gg/gYXwgQaT7p)
Lightweight Python library for scraping websites with LLMs.
You can test it on [Parsera website](https://parsera.org).
## Why Parsera?
Because it's simple and lightweight, with minimal token use which boosts speed and reduces expenses.
## Table of Contents
- [Installation](#Installation)
- [Documentation](#Documentation)
- [Basic usage](#Basic-usage)
- [Running with Jupyter Notebook](#Running-with-Jupyter-Notebook)
- [Running with CLI](#Running-with-CLI)
- [Running in Docker](#Running-in-Docker)
## Installation
```shell
pip install parsera
playwright install
```
## Documentation
Check out [documentation](https://docs.parsera.org) to learn more about other features, like running custom models and playwright scripts.
## Basic usage
If you want to use OpenAI, remember to set up `OPENAI_API_KEY` env variable.
You can do this from python with:
```python
import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY_HERE"
```
Next, you can run a basic version that uses `gpt-4o-mini`
```python
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:
```json
[
{
"Title":"Hacking the largest airline and hotel rewards platform (2023)",
"Points":"104",
"Comments":"24"
},
...
]
```
There is also `arun` async method available:
```python
result = await scrapper.arun(url=url, elements=elements)
```
## Running with Jupyter Notebook:
Either place this code at the beginning of your notebook:
```python
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
```sh
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](https://docs.parsera.org/features/docker/).