Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ganesh2409/ask-my-pdf

The Document Buddy App is a user-friendly Streamlit application designed to enhance the way individuals interact with their PDF documents. Whether you're a student, researcher, or professional, this app provides powerful tools to simplify document management.
https://github.com/ganesh2409/ask-my-pdf

chat-with-pdf genai langchain python qdrant

Last synced: 25 days ago
JSON representation

The Document Buddy App is a user-friendly Streamlit application designed to enhance the way individuals interact with their PDF documents. Whether you're a student, researcher, or professional, this app provides powerful tools to simplify document management.

Awesome Lists containing this project

README 

        
# 📄 ASK-MY-PDF



![Document Buddy App](logo.png)



**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](https://langchain.readthedocs.io/)**: Utilized as the orchestration framework to manage the flow between different components, including embeddings creation, vector storage, and chatbot interactions.

  

- **[Unstructured](https://github.com/Unstructured-IO/unstructured)**: Employed for robust PDF processing, enabling the extraction and preprocessing of text from uploaded PDF documents.

  

- **[BGE Embeddings from HuggingFace](https://huggingface.co/BAAI/bge-small-en)**: Used to generate high-quality embeddings for the processed documents, facilitating effective semantic search and retrieval.

  

- **[Qdrant](https://qdrant.tech/)**: 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](https://ollama.com/)**: Integrated as the local language model to power the chatbot, providing intelligent and context-aware responses based on the document embeddings.

  

- **[Streamlit](https://streamlit.io/)**: 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



```bash

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:

```bash

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](https://www.docker.com/get-started).



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



   ```bash

   docker pull qdrant/qdrant

   ```



   ```bash

   docker run -p 6333:6333 qdrant/qdrant

   ```



### 5. Run the App



Start the Streamlit app using the following command:

```bash

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:



```bash

git checkout -b feature/YourFeatureName

```



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

5.	Commit Your Changes:



```bash

git commit -m "Add Your Feature Description"

```



6.	Push to Your Fork:



```bash

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 ❤️

```