AI Document Chatbot (RAG)

This project demonstrates a Retrieval Augmented Generation (RAG) system that answers questions about company reports using vector search.

Architecture

Documents (PDF)

  ↓

Chunking

  ↓

Embeddings (Sentence Transformers)

  ↓

FAISS Vector Search

  ↓

Relevant Text Chunks

  ↓

Displayed in Streamlit Web App

Project Structure

rag-chatbot

│

├── data

├── vectorstore

├── ingest.py

├── vectorstore.py

├── app.py

└── requirements.txt

How it works

1. ingest.py

   • Loads PDFs
   • Splits text into chunks

2. vectorstore.py

   • Converts chunks into embeddings
   • Creates FAISS vector index

3. app.py

   • Streamlit interface
   • User asks question
   • System retrieves relevant text chunks

Run the project

Install dependencies

pip install -r requirements.txt

Create vector database

python ingest.py python vectorstore.py

Run the app

streamlit run app.py

Example Questions

• What risks are mentioned in the report?
• What sustainability initiatives are described?
• What are the main financial highlights?