🌟 String Matching Tool

Project Overview 📑

The String Matching Tool is a web application designed to allow users to search across multiple .txt documents simultaneously. The tool utilizes two powerful algorithms:

• ⚡ BM25 Algorithm: Used for document relevancy ranking based on the search query.

• 🔍 Boyer-Moore Algorithm: Efficient for fast string searching to detect lines containing the queried term.

The application is built using a React frontend and a Django backend, effectively leveraging modern data structures such as Stacks to detect and store relevant lines in documents.

Features ✨

• 📄 Simultaneous search across multiple .txt documents.

• 📈 BM25 Algorithm for document relevancy ranking.

• 🧠 Boyer-Moore Algorithm for efficient string searching.

• 💡 Data structures like Stacks used for results formatting and optimization.

• 🖥️ Easy-to-use web interface for uploading documents and querying search terms.

• 🗣️ Search terms can include phrases as well.

Note:

• 📁 Currently, only up to 5 files can be uploaded at a time, and all files must be in .txt format.

• 🗂️ All uploaded files are stored in the media/ folder of the Django server.

Tech Stack 🛠️

• Frontend: React.js ⚛️

• Backend: Django (Python) 🐍

• Search Algorithms: BM25 (document relevancy) 📊, Boyer-Moore (string searching) 🔍

• Data Structures: Stacks 🏗️

• File Handling: Support for .txt files only 📄 (up to 5 files at once)

System Architecture 🏗️

1. Frontend (React): Users can upload .txt files and input search queries.

2. Backend (Django): Manages the search operations using the BM25 and Boyer-Moore algorithms, and returns the lines of text where the search term appears.

Key Algorithms 🔑

BM25 (Best Matching 25) 🧠

The BM25 algorithm is used to evaluate the relevance of a document to a given query by:

• Calculating term frequency and inverse document frequency.

• Ranking documents based on their relevance scores.

Here is the Algorithm Library referred to while developing this project: BM25 ALGORITHM

Boyer-Moore Algorithm ⚡

The Boyer-Moore algorithm is a string searching technique that skips sections of the text where the search term cannot possibly match. Its efficiency makes it suitable for large documents.

Learn more about the algorithm here: Boyer Moore Algorithm for Pattern Searching - GeeksforGeeks

Setup Instructions 🛠️

Follow these steps to set up and run the project on your local machine.

Prerequisites 📋

Make sure you have the following installed on your machine:

• ⚛️ Node.js and npm

• 🐍 Python 3.x and pip

• 🖥️ Django

• 🔄 Virtualenv (recommended for Django)

1. Clone the Repository 💻

git clone https://github.com/ByteBard18/StringMatchingTool.git

cd string-matching-tool

2. Frontend Setup (REACT-VITE) 🚀

cd frontend

npm install

3. Backend Setup (DJANGO) 🛠️

cd ../backend

pip install -r requirements.txt  # Install all the dependencies

4. Creating Superuser for Admin Access 🧑‍💻

python manage.py createsuperuser

You will be prompted to enter a username, email, and password for the admin account.

Once the superuser is created, you can access the Django admin panel by navigating to http://localhost:8000/admin/.

5. Running the Application 🏃‍♂️

Run the Django Backend 🚀

Navigate to the backend directory.

Apply migrations and start the Django development server.

python manage.py migrate

python manage.py runserver

By default, this will start the backend on http://localhost:8000/.

Run the React Frontend ⚛️

In another terminal, navigate to the frontend directory and run the React app:

cd frontend

npm run dev

This will start the React development server on http://localhost:5173/.

6. Testing the Application 🧪

Open your browser and go to http://localhost:5173/ to interact with the frontend. You can now upload .txt files (up to 5 at a time) and start querying search terms, including phrases. The React frontend communicates with the Django backend to process the search.

Folder Structure 🗂️

string-matching-tool/

├──  docs/

├──  frontend/  # React frontend

│  ├──  public/

│  ├──  src/

│  └──  package.json

│

├──  StringMatchingTool/  # Django backend

│  ├──  StringMatchingTool/  #project

| ├──  search_handler/  #app

│  ├──  media/  # Directory where uploaded files are stored

│  ├──  manage.py

│  └──  requirements.txt

│

└──  README.md  # Project documentation

Future Enhancements 🚀

Handling Larger Datasets: Scaling with more efficient indexing and storage mechanisms such as Elasticsearch for faster retrieval.

Support for More File Types: Adding support for PDFs and Word documents.

Improved UX: Incorporating advanced filtering options and fuzzy search capabilities for better user experience.

Machine Learning Integration: Using NLP models for better relevancy ranking beyond BM25.

Contributing 🤝

Feel free to submit issues or pull requests if you want to contribute to the project. Contributions are always welcome!