📄 GenAI Project: PDF Question Answering System

1. Introduction

This project presents a Generative AI-based system that allows users to upload a PDF document and ask questions related to its content. The system processes the document and provides accurate answers using modern AI techniques.

---

2. Objective

The main objective is to build an intelligent system that can understand and extract information from documents and respond to user queries effectively.

---

3. Methodology

The workflow includes:

• Uploading a PDF
• Extracting text
• Splitting text into chunks
• Converting text into embeddings
• Storing embeddings in a vector database
• Retrieving relevant information
• Generating answers using a language model

---

4. Tools and Technologies Used

• Python
• Streamlit (Frontend)
• LangChain (Framework)
• FAISS (Vector Database)
• Llama 3 via Groq (LLM)

---

5. System Architecture

User → Streamlit UI → PDF Loader → Text Splitter → Embeddings → FAISS → Retriever → LLM → Answer

---

6. Working of the System

1. The user uploads a PDF through the interface.
2. The system processes the document and converts it into vector embeddings.
3. When a user asks a question, the system retrieves relevant information.
4. The LLM generates an answer based on the retrieved data.

---

7. Advantages

• Works offline (using Llama)
• Fast document search
• Accurate answers
• Easy to use interface

---

8. Conclusion

This project demonstrates how Generative AI can be used to build intelligent document-based question answering systems. It highlights the power of RAG and LLMs in real-world applications.