🌟 Overview

This system provides a robust framework for extracting, processing, and querying information from various text sources, including PDFs and web articles. Users can create custom "Spaces" to manage and query content efficiently. The platform ensures flexibility, ease of use, and is entirely free for local setup.

✅ Key Features

• 🗂️ Spaces for Organization: Users can create multiple spaces to categorize and manage text data.
• 📑 PDF and URL Support: Upload multiple PDFs and paste article URLs to extract and process content.
• ⚙️ Text Processing: Extract content, split it into chunks, and generate embeddings for semantic understanding.
• 🔍 Custom Querying: Query the processed content within any selected space using advanced embeddings.
• 💸 Free to Use: Completely free and easy to set up locally.
• 🔑 Customizable API Key: Users can input and update their Google AI Studio API key as needed.
• 🖥️ User-Friendly Integration: A simple and intuitive system for text processing and querying.

🔧 Installation

Backend

1. Clone the repository:

   git clone https://github.com/HarshVz/QuerySpace.git
   cd QuerySpace/Backend
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the backend server:

   uvicorn main:app --reload
   
   

Frontend

1. Navigate to the frontend directory:

   cd QuerySpace/Frontend
   

2. Install dependencies:

   npm install
   

3. Run the frontend development server:

   npm run dev
   
   

🔧 How It Works

1. Creating a Space: Users create a space to organize their text data.
2. Uploading Data: Within the space, users can upload PDFs and paste URLs for processing.
3. Processing Content:
   • 📂 PDFs and URLs are processed to extract content.
   • 🧩 Extracted content is combined, split into smaller chunks, and embeddings are generated.

4. Querying the Space:
   • 🔎 Users can query processed content by selecting the relevant space.
   • 🧠 Queries are powered by semantic embeddings for precise results.
   • 💬 A response is generated using the extracted data and provided context.

5. Manage Spaces: Users can view all created spaces and manage them efficiently.

🛠️ Technology Stack

• Backend: Python, FastAPI, PyPDF, BeautifulSoup, Langchain, Transformers (Hugging Face), Gemini
• Frontend: React, Tailwind CSS, Markdown

📌 Contributing

Contributions are welcome! Please open an issue or pull request for suggestions or bug fixes.