dr-web-engine 0.5.1

A data retrieval engine based on Playwright.

DR Web Engine

DR Web Engine is an open-source data retrieval engine designed to extract structured data from web pages using Playwright. It allows users to define queries in JSON5 or YAML format and execute them to retrieve data from websites. The project is highly extensible and can be used for web scraping, data extraction, and automation tasks.

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Features

• Queryable Web: Define extraction queries in JSON5 or YAML format.
• Playwright Integration: Leverages Playwright for robust and reliable web automation.
• Extensible: Easily add new parsers, extractors, or custom logic.
• CLI Support: Run queries directly from the command line.
• Docker Support: Run the engine in a containerized environment.

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Installation

Option 1: Install from Source

1. Clone the repository:

   git clone https://github.com/starlitlog/dr-web-engine.git
   cd dr-web-engine
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install the project in editable mode:

   pip install -e .
   

Option 2: Install via pip

The project is available on PyPI. Install it using:

pip install dr-web-engine

Option 3: Run with Docker

1. Build the Docker image:

   docker build -t dr-web-engine .
   

2. Run the container:

   docker run -v $(pwd)/data:/app/data dr-web-engine -q /app/data/query.json5 -o /app/data/output.json
   

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Usage

Command Line Interface (CLI)

Run the CLI with the following command:

python cli/cli.py -q engine/data/query.json5 -o engine/data/output.json

Parameters

• -q or --query: Path to the query file (JSON5 or YAML).
• -o or --output: Path to the output file where results will be saved.
• -f or --format: Query format (json5 or yaml). Default: json5.
• -l or --log-level: Logging level (error, warning, info, debug). Default: error.
• --log-file: Path to the log file. If not provided, logs are printed to stdout.

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Extending the Project

Adding New Parsers

1. Create a new parser in the web_engine/parsers/ directory.
2. Implement a parse() function that takes a file path and returns a parsed query.
3. Register the parser in web_engine/parsers/__init__.py using the get_parser() function.

Example:

# web_engine/parsers/custom_parser.py
def parse(file_path):
    # Custom parsing logic
    return parsed_query

Adding New Extractors

1. Create a new extractor in the web_engine/extractors/ directory.
2. Implement the extraction logic.
3. Update the execute_query() function in web_engine/engine.py to use the new extractor.

Adding New Query Types

1. Define a new query model in web_engine/models.py.
2. Update the ExtractionQuery class to support the new query type.
3. Add corresponding parsing and extraction logic.

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Example Query

JSON5 Query

{
  "@url": "https://example.com",
  "@steps": [
    {
      "@xpath": "//div[@class='item']",
      "@name": "items",
      "@fields": {
        "title": ".//h2",
        "description": ".//p"
      }
    }
  ],
  "@pagination": {
    "@xpath": "//a[@class='next']",
    "@limit": 5
  }
}

YAML Query

url: https://example.com
steps:
  - xpath: //div[@class='item']
    name: items
    fields:
      title: .//h2
      description: .//p
pagination:
  xpath: //a[@class='next']
  limit: 5

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Learn More

For more details about the project and its use cases, check out the blog posts at:

• Queryable Web Blog

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request with a detailed description of your changes.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Support

If you encounter any issues or have questions, please open an issue on GitHub.

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