HIREWAY (NLP_DEV_SEARCH) - Natural Language Powered Search Application Using TOGETHER AI

This project is a demonstration of a web application that leverages Natural Language Processing (NLP) to enhance search functionality. It consists of a backend (Python) and a frontend (HTML, CSS, JavaScript) designed to provide users with more intuitive and context-aware search results.

About

The goal of this project is to showcase how NLP techniques can be integrated into a search application to understand user queries beyond simple keyword matching. By using NLP, the application can:

• Understand intent: Interpret the meaning behind user queries, even if they are phrased in natural language.
• Handle synonyms and related terms: Return relevant results even if the exact keywords are not present.
• Provide more contextually relevant results: Rank results based on their semantic similarity to the query.

Project Structure

Key Components:

• Backend (backend/):
  • Implemented using Python and FastAPI.
  • Handles the search logic, NLP processing, and data retrieval.
  • Uses a virtual environment (.venv) for dependency management.
  • The search.py file contains the core NLP-powered search functionality.
  • fake_data.py is used to simulate data for development.
  • Environment variables (API keys, etc.) are stored in .env (which should NOT be committed).
• Frontend (frontend/):
  • Built with HTML, CSS, and JavaScript.
  • Provides the user interface for interacting with the search application.
  • app.js handles the client-side logic and communication with the backend.

Getting Started

STEP 1 - Install Dependencies:

1. Clone the repository:

   git clone [repository URL]
   cd NLP_DEV_SEARCH

2. Create and activate a virtual environment (backend):

   python3 -m venv .venv
   source .venv/bin/activate  # On macOS/Linux
   .venv\Scripts\activate  # On Windows

3. Install backend dependencies:

   pip install -r requirements.txt

4. Create a .env file (if needed) and add your environment variables.

STEP 2 - Run the Backend:

1. Open the backend folder in your terminal or IDE.
2. Start the FastAPI server:

   uvicorn app.main:app --reload

STEP 3 - Open the Frontend:

1. Open a new terminal window.
2. Navigate to the frontend directory.
3. Open index.html in your web browser. You can typically do this by simply double-clicking the file in your file explorer, or by dragging and dropping it into your browser window.

Future Enhancements

• Implement more sophisticated NLP techniques (e.g., semantic search, entity recognition).
• Integrate with a real database for persistent data storage.
• Add user authentication and authorization.
• Improve the frontend user interface and user experience.
• Implement testing for both backend and frontend.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues to suggest improvements or report bugs.