📄 ASK-MY-PDF

ASk MY PDF is a powerful Streamlit-based application designed to simplify document management. Upload your PDF documents, create embeddings for efficient retrieval, and interact with your documents through an intelligent chatbot interface. 🚀

🛠️ Features

• 📂 Upload Documents: Easily upload and preview your PDF documents within the app.
• 🧠 Create Embeddings: Generate embeddings for your documents to enable efficient search and retrieval.
• 🤖 Chatbot Interface: Interact with your documents using a smart chatbot that leverages the created embeddings.
• 📧 Contact: Get in touch with the developer or contribute to the project on GitHub.
• 🌟 User-Friendly Interface: Enjoy a sleek and intuitive UI with emojis and responsive design for enhanced user experience.

🖥️ Tech Stack

The Document Buddy App leverages a combination of cutting-edge technologies to deliver a seamless and efficient user experience. Here's a breakdown of the technologies and tools used:

• LangChain: Utilized as the orchestration framework to manage the flow between different components, including embeddings creation, vector storage, and chatbot interactions.

• Unstructured: Employed for robust PDF processing, enabling the extraction and preprocessing of text from uploaded PDF documents.

• BGE Embeddings from HuggingFace: Used to generate high-quality embeddings for the processed documents, facilitating effective semantic search and retrieval.

• Qdrant: A vector database running locally via Docker, responsible for storing and managing the generated embeddings for fast and scalable retrieval.

• LLaMA 3.2 via Ollama: Integrated as the local language model to power the chatbot, providing intelligent and context-aware responses based on the document embeddings.

• Streamlit: The core framework for building the interactive web application, offering an intuitive interface for users to upload documents, create embeddings, and interact with the chatbot.

📁 Directory Structure

ASK-MY-PDF/

├── app.py
├── Attention Is All U Need.pdf
├── chatbot.py
│── logo.png
├── README.md
├── requirements.txt
├── vectors.py

🚀 Getting Started

Follow these instructions to set up and run the Document Buddy App on your local machine.

1. Clone the Repository

git clone https://github.com/Ganesh2409/ASK-MY-PDF.git
cd ASK-MY-PDF

2. Create a Virtual Environment

You can either use Python’s venv or Anaconda to create a virtual environment for managing dependencies.

Option 1: Using venv

On Windows:

python -m venv venv

Activate the venv

 cd venv\Scripts\activate

3. Install Dependencies

Once the environment is set up (whether venv or Conda), install the required dependencies using requirements.txt:

pip install -r requirements.txt

4. Running Qdrant in Docker

To run Qdrant using Docker, follow these steps:

1. Install Docker: Make sure you have Docker installed on your machine. You can download it from Docker's official website.

2. Pull the Qdrant Docker Image: Open your terminal and run the following command to pull the latest Qdrant image:

   docker pull qdrant/qdrant

   docker run -p 6333:6333 qdrant/qdrant

5. Run the App

Start the Streamlit app using the following command:

streamlit run app.py

This command will launch the app in your default web browser. If it doesn’t open automatically, navigate to the URL provided in the terminal (usually http://localhost:8501).

🤝 Contributing

Contributions are welcome! Whether it’s reporting a bug, suggesting a feature, or submitting a pull request, your input is highly appreciated. Follow these steps to contribute:

1. Fork the Repository: Click on the “Fork” button at the top-right corner of the repository page.
2. Clone Your Fork
3. Create a New Branch:

git checkout -b feature/YourFeatureName

4. Make Your Changes: Implement your feature or fix.
5. Commit Your Changes:

git commit -m "Add Your Feature Description"

6. Push to Your Fork:

git push origin feature/YourFeatureName

7. Create a Pull Request: Navigate to the original repository and create a pull request from your fork.

📫 Contact

• Email:[email protected] ✉️

Feel free to reach out for any queries, suggestions, or contributions. Your feedback is invaluable!

🔗 Useful Links

• Streamlit Documentation: https://docs.streamlit.io/

• LangChain Documentation: https://langchain.readthedocs.io/

• Qdrant Documentation: https://qdrant.tech/documentation/

• ChatOllama Documentation: https://github.com/ollama/ollama

© 2024 ASK-MY-PDF. Made with ❤️