Search Engine

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Search Engine is a simple, efficient engine that builds a reverse index for keyword searching and ranks results using the PageRank algorithm.

⚙️ Installation

Please create a virtual environment using venv, as the project is still in alpha testing and in its initial implementations.

python3 -m venv .env
source .env/bin/activate
pip install search-engine-cpp

🚀 Usage

from search_engine.crawler import Crawler

crawler = Crawler("https://en.wikipedia.org", "/wiki/", "Cat", test_mode=True)
graph = crawler.run(limit=10)
my_dict = graph.compute_page_rank()
top = sorted(my_dict.items(), key=lambda item: item[1], reverse=True)[:3]

print(top)

📋 Requirements for Contributions

Before compiling the project, ensure your environment meets the following requirements:

• CMake 3.10 or higher
• Google Test for unit testing
• A C++11 compatible compiler or higher

📂 Project Structure

The project is organized as follows:

• src/: Main implementation of the search engine, including reverse indexing and the PageRank algorithm.
• tests/: Unit tests to verify the functionality of the system.
• CMakeLists.txt: Configuration file for building the project with CMake.

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🔧 Building the Project

To compile the project, follow these steps:

1. Create a build directory and navigate into it:

   mkdir build && cd build
   

2. Run CMake to generate the build files:

   cmake ..
   

3. Compile the project using make:

   make
   

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🧪 Running Tests for Contributions

Run unit tests to ensure the correctness of the system.

1. After building the project, navigate to the build directory and execute:

   ./tests/unit-tests/LibUnitTests
   

This will run the tests covering search engine functionality, reverse indexing, and the PageRank algorithm.

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🏃 Running Examples for Contributions

The first step is building the project, for this to run:

poetry install
poetry build

After building it, run this command to see the library working:

poetry run python Examples/graph_example.py

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⚙️ How It Works

• Reverse Indexing: Maps keywords to the documents where they appear.
• PageRank: An algorithm that assigns a relevance score to each document based on its links and structure.
• Querying: Searches for documents related to a keyword and ranks them according to their PageRank score.

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📄 License

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

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👥 Contributors

We welcome all contributions to this project! Please make sure to follow the guidelines outlined in the CONTRIBUTING.md file.
Thanks to all contributors

Made with contrib.rocks.

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Keep learning,
Pedro;)