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  • Host: GitHub
  • Owner: Harish-dealwallet
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 12.2 MB
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Created almost 2 years ago · Last pushed almost 2 years ago
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Readme Changelog Contributing License Code of conduct Citation Security

README.md

ScrapeGraphAI: You Only Scrape Once

English | | | |

Downloads linting: pylint Pylint CodeQL License: MIT

ScrapeGraphAI is a web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.).

Just say which information you want to extract and the library will do it for you!

ScrapeGraphAI Hero

Quick install

The reference page for Scrapegraph-ai is available on the official page of PyPI: pypi.

```bash pip install scrapegraphai

playwright install ```

Note: it is recommended to install the library in a virtual environment to avoid conflicts with other libraries

Optional Dependencies Additional dependecies can be added while installing the library: - More Language Models: additional language models are installed, such as Fireworks, Groq, Anthropic, Hugging Face, and Nvidia AI Endpoints. This group allows you to use additional language models like Fireworks, Groq, Anthropic, Together AI, Hugging Face, and Nvidia AI Endpoints. ```bash pip install scrapegraphai[other-language-models] ``` - Semantic Options: this group includes tools for advanced semantic processing, such as Graphviz. ```bash pip install scrapegraphai[more-semantic-options] ``` - Browsers Options: this group includes additional browser management tools/services, such as Browserbase. ```bash pip install scrapegraphai[more-browser-options] ``` - faiss Options: this group includes faiss integration ```bash pip install scrapegraphai[faiss-cpu] ```

Installing "More Browser Options"

This group includes an ocr scraper for websites bash pip install scrapegraphai[screenshot_scraper]

Usage

There are multiple standard scraping pipelines that can be used to extract information from a website (or local file).

The most common one is the SmartScraperGraph, which extracts information from a single page given a user prompt and a source URL.

```python import json from scrapegraphai.graphs import SmartScraperGraph

Define the configuration for the scraping pipeline

graphconfig = { "llm": { "apikey": "YOUROPENAIAPIKEY", "model": "openai/gpt-4o-mini", }, "verbose": True, "headless": False, }

Create the SmartScraperGraph instance

smartscrapergraph = SmartScraperGraph( prompt="Find some information about what does the company do, the name and a contact email.", source="https://scrapegraphai.com/", config=graph_config )

Run the pipeline

result = smartscrapergraph.run() print(json.dumps(result, indent=4)) ```

The output will be a dictionary like the following:

python { "company": "ScrapeGraphAI", "name": "ScrapeGraphAI Extracting content from websites and local documents using LLM", "contact_email": "contact@scrapegraphai.com" }

There are other pipelines that can be used to extract information from multiple pages, generate Python scripts, or even generate audio files.

| Pipeline Name | Description | |-------------------------|------------------------------------------------------------------------------------------------------------------| | SmartScraperGraph | Single-page scraper that only needs a user prompt and an input source. | | SearchGraph | Multi-page scraper that extracts information from the top n search results of a search engine. | | SpeechGraph | Single-page scraper that extracts information from a website and generates an audio file. | | ScriptCreatorGraph | Single-page scraper that extracts information from a website and generates a Python script. | | SmartScraperMultiGraph | Multi-page scraper that extracts information from multiple pages given a single prompt and a list of sources. | | ScriptCreatorMultiGraph | Multi-page scraper that generates a Python script for extracting information from multiple pages and sources. |

It is possible to use different LLM through APIs, such as OpenAI, Groq, Azure and Gemini, or local models using Ollama.

Remember to have Ollama installed and download the models using the ollama pull command, if you want to use local models.

Demo

Official streamlit demo:

My Skills

Try it directly on the web using Google Colab:

Open In Colab

Documentation

The documentation for ScrapeGraphAI can be found here.

Check out also the Docusaurus here.

Sponsors

Browserbase SerpAPI Stats

Contributing

Feel free to contribute and join our Discord server to discuss with us improvements and give us suggestions!

Please see the contributing guidelines.

My Skills My Skills My Skills

Roadmap

We are working on the following features! If you are interested in collaborating right-click on the feature and open in a new tab to file a PR. If you have doubts and wanna discuss them with us, just contact us on discord or open a Discussion here on Github!

```mermaid %%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#5C4B9B', 'edgeLabelBackground':'#ffffff', 'tertiaryColor': '#ffffff', 'primaryBorderColor': '#5C4B9B', 'fontFamily': 'Arial', 'fontSize': '16px', 'textColor': '#5C4B9B' }}}%% graph LR A[DeepSearch Graph] --> F[Use Existing Chromium Instances] F --> B[Page Caching] B --> C[Screenshot Scraping] C --> D[Handle Dynamic Content] D --> E[New Webdrivers]

style A fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10
style F fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10
style B fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10
style C fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10
style D fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10
style E fill:#ffffff,stroke:#5C4B9B,stroke-width:2px,rx:10,ry:10

click A href "https://github.com/VinciGit00/Scrapegraph-ai/issues/260" "Open DeepSearch Graph Issue"
click F href "https://github.com/VinciGit00/Scrapegraph-ai/issues/329" "Open Chromium Instances Issue"
click B href "https://github.com/VinciGit00/Scrapegraph-ai/issues/197" "Open Page Caching Issue"
click C href "https://github.com/VinciGit00/Scrapegraph-ai/issues/197" "Open Screenshot Scraping Issue"
click D href "https://github.com/VinciGit00/Scrapegraph-ai/issues/279" "Open Handle Dynamic Content Issue"
click E href "https://github.com/VinciGit00/Scrapegraph-ai/issues/171" "Open New Webdrivers Issue"

```

Telemetry

We collect anonymous usage metrics to enhance our package's quality and user experience. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable SCRAPEGRAPHAITELEMETRYENABLED=false. For more information, please refer to the documentation here.

Contributors

Contributors

Citations

If you have used our library for research purposes please quote us with the following reference: text @misc{scrapegraph-ai, author = {Marco Perini, Lorenzo Padoan, Marco Vinciguerra}, title = {Scrapegraph-ai}, year = {2024}, url = {https://github.com/VinciGit00/Scrapegraph-ai}, note = {A Python library for scraping leveraging large language models} }

Authors

Authors_logos

| | Contact Info | |--------------------|----------------------| | Marco Vinciguerra | Linkedin Badge | | Marco Perini | Linkedin Badge | | Lorenzo Padoan | Linkedin Badge |

License

ScrapeGraphAI is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgements

  • We would like to thank all the contributors to the project and the open-source community for their support.
  • ScrapeGraphAI is meant to be used for data exploration and research purposes only. We are not responsible for any misuse of the library.

Owner

  • Login: Harish-dealwallet
  • Kind: user

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