Inverto 💬 Inverted-Index-Search-Engine 🔎

Overview

Inverto is an advanced inverted-index search engine designed to optimize query processing speed through the application of effective compression technologies. This project encompasses the entire lifecycle of a search engine, from data ingestion to query execution, ensuring high performance and efficient data management.

Features

• Advanced Compression Techniques: Utilizes state-of-the-art compression methods to enhance query processing speed.
• Natural Language Processing (NLP): Leverages Python's rich library ecosystem to perform complex NLP tasks with ease.
• User-Friendly Interface: Built with Qt, providing a seamless and intuitive user experience.
• Scalable Data Storage: Employs MongoDB for robust and scalable storage of the inverted index.

Implementation

1. Data Ingestion

The initial step involves ingesting raw data and preprocessing it to extract relevant information. This data is then tokenized, filtered, and normalized to ensure consistency.

2. Index Construction

An inverted index is constructed from the preprocessed data. This involves mapping each term to its corresponding document occurrences, creating a highly efficient data structure for quick lookups.

3. Compression

Effective compression techniques are applied to the inverted index to reduce storage requirements and improve query processing speed. Various algorithms are evaluated to determine the most suitable one for the dataset and query patterns.

4. Query Processing

The search engine processes user queries by efficiently retrieving relevant documents from the compressed inverted index. Advanced algorithms are employed to rank and return the most relevant results.

5. User Interface

The front-end interface, designed using Qt, allows users to interact with the search engine seamlessly. It supports various query types and provides an intuitive way to view and navigate the search results.

Technologies Used

• Python: The core programming language used for implementing the search engine. Python's extensive libraries and frameworks facilitate complex algorithms and NLP tasks.
• MongoDB: A NoSQL database used for storing the inverted index, ensuring scalability and fast data retrieval.
• Qt: A powerful framework for creating the graphical user interface (GUI) of the search engine, offering a rich user experience.

Getting Started

Prerequisites

• Python 3.x
• MongoDB
• Qt

Installation

1. Clone the Repository:

   git clone https://github.com/themihirmathur/Inverto.git
   cd Inverto
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Setup MongoDB:

   • Ensure MongoDB is installed and running on your local machine or server.
   • Configure the connection settings in the configuration file.

4. Run the Application:

   python main.py
   

Usage

1. Indexing Data:

   • Use the provided scripts to ingest and index your dataset.
   • Ensure the inverted index is stored in MongoDB.

2. Executing Queries:

   • Launch the search engine UI using Qt.
   • Enter your search queries and view the results in the GUI.

Contributing

We welcome contributions to improve Inverto. Please follow these steps to contribute:

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

Contact

For any inquiries or feedback, please contact Mihir Mathur at [email protected].

Thank you for using Inverto! We hope it provides a robust and efficient solution for your search engine needs.