🧠 SmartDoc QA Bot with Vertex AI

RAG Chatbot using Vertex AI + LangChain

This project is a Retrieval-Augmented Generation (RAG) chatbot that allows users to ask questions about a long PDF document (100–200 pages). The chatbot uses Vertex AI (Google) for embedding and generation, LangChain for chunking and retrieval, and Streamlit for the user interface.

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🚀 Features

• 🔍 PDF ingestion using pdfplumber
• 🧩 Text splitting into chunks using LangChain
• 🔎 FAISS-based retrieval of relevant chunks
• 🧠 Vertex AI:
  • textembedding-gecko for embedding
  • gemini-pro for content generation
• 💬 Streamlit UI

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📁 Folder Structure

rag_chatbot_project/
│
├── app.py                # Streamlit UI
├── rag_pipeline.py       # Core RAG logic
├── requirements.txt      # Dependencies
├── .env.example          # API key template
└── README.md             # Project overview

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⚙️ Setup Instructions

1. Clone the repo

git clone https://github.com/Diksha20Kam/RAG-Chatbot-using-Vertex-AI.git
cd RAG-Chatbot-using-Vertex-AI

2. Create & activate conda env

conda create -n rag_chatbot_env python=3.10 -y
conda activate rag_chatbot_env

3. Install dependencies

pip install -r requirements.txt

4. Add your API key

Create a .env file in the root directory:

VERTEX_API_KEY=your_actual_vertex_api_key

5. Run Streamlit app

streamlit run app.py

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🛠️ Requirements

streamlit
langchain
langchain-community
python-dotenv
pdfplumber
faiss-cpu
requests

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