📚 PDF Chat&View Assistant optimize for Vietnamese !

Welcome to the PDF Chat&View Assistant! This application allows you to upload PDF files for viewing and chat with them using the power of AI. Powered by LangChain, and Groq's LLM, it brings your static documents to life by giving you answers to your questions based on their content!

Test app : https://chatviewwithpdfvietnamese.streamlit.app/

🚀 Getting Started

Prerequisites

To get started with the PDF Chat&View Assistant, you will need to have:

• Python 3.8+
• Streamlit
• Hugging Face Transformers
• Groq API Token

Installation

1. Clone the Repository

   https://github.com/MonsieurNam/ChatViewWithPDFVietNamese.git

2. Install the Required Packages

   Install all dependencies by running:

   pip install -r requirements.txt

3. Set up Environment Variables

   Create a .env file in the root directory and add your Groq API Token:

   GROQ_API_TOKEN="your_groq_api_key_here"
   

🎯 How to Use

1. Run the Application

   Start the application using the following command:

   streamlit run app.py 

2. Upload PDFs

   Use the document uploader on the right side of the page to upload your PDF files. Attention to pre-prepare 3 file:

   • Your pdf file.pdf
   • Your text content of pdf file.pdf
   • The structure of pdf file.txt: <mã phần>#<trang bắt đầu>#<trang kết thúc>#<tên phần>
   • EX:
   • a#001#009#NHỮNG QUỐC GIA ĐẦU TIÊN TRÊN LÃNH THỔ VIỆT NAM
   • b#010#082#XÂY DỰNG VÀ BẢO VỆ ĐẤT NƯỚC VIỆT NAM
   • b1#010#016#Đấu tranh giành độc lập thời kì Bắc thuộc .......

3. Ask Questions

   Once your documents are processed, you can start asking questions based on the content of your PDFs in the chat interface at sidebar.

4. Customize the AI

   Modify the system prompt to change how the AI responds. For example, you can ask it to be more casual, technical, or creative!

🛠️ Project Architecture

Key Components:

• Streamlit: Frontend interface and chat system.
• LangChain: Manages conversational logic and connects your queries with document content.
• Hugging Face Embeddings: Transforms document text into meaningful vector representations.
• Groq API: The powerhouse behind generating intelligent responses based on document data.

Workflow

1. Upload PDFs: The user uploads PDFs that need to be processed.
2. Text Processing: Text is extracted and split into chunks for easier embedding.
3. Viewing and Conversational Retrieval: View the PDF file and using the AI searches through the document to retrieve relevant information and answers questions.

🌟 Customization Options

• System Prompt: Customize the behavior of the AI by changing the system prompt.
• Temperature: Adjust the temperature to control the creativity of responses.

Technologies Used:

• Python: Core programming language.
• Streamlit: For the web interface.
• PyPDF2: For extracting text from PDFs.
• LangChain: For conversational AI and document vectorization.
• Groq API: Powers the conversational responses.