📄 Research QA Bot – Chat with Your Papers

A Streamlit-powered AI Assistant that lets you upload Multiple research papers (PDFs) and ask natural language questions about them.
No more manual skimming through dozens of pages — this bot extracts, chunks, embeds, and retrieves the most relevant context from your PDFs and generates concise, accurate answers.

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🚀 Features

• 📂 Upload multiple research papers in PDF format
• 🔍 Extracts and preprocesses text from PDFs
• ✂️ Splits content into semantic chunks for better retrieval
• 📊 Uses FAISS Vector Store for efficient similarity search
• 🧠 Powered by Google Generative AI (Gemini & Embeddings)
• 💬 Interactive Q&A chat interface with Streamlit
• ⚡ Accurate, context-aware answers with guardrails against hallucinations

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🛠️ Tech Stack

• Python 3.10+
• Streamlit – UI for user interaction
• PyPDF2 – PDF text extraction
• LangChain – Text chunking & QA chain
• FAISS – Vector store for semantic search
• Google Generative AI – Embeddings & Gemini LLM
• dotenv – Manage API keys securely

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📂 Project Pipeline

Here’s the step-by-step data flow pipeline that powers the bot:

1. PDF Upload – User uploads one or more research papers via Streamlit.
2. Text Extraction – Extract raw text from each page using PyPDF2.
3. Text Chunking – Break text into overlapping chunks using RecursiveCharacterTextSplitter (helps preserve context).
4. Embeddings Generation – Convert text chunks into embeddings using Google Generative AI Embeddings.
5. Vector Store Indexing – Store embeddings in a FAISS index for efficient similarity search.
6. User Query – User asks a question in natural language.
7. Retrieval – Relevant chunks are fetched from FAISS.
8. Answer Generation – Context + query passed to Gemini LLM via LangChain for structured, accurate answers.
9. Streamlit UI – Display answers in real-time.

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⚡ Quickstart

1️⃣ Clone the Repo

git clone https://github.com/your-username/research-qa-bot.git
cd research-qa-bot

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Set up Environment Variables

Create a .env file in the root directory and add your Google API key:

GOOGLE_API_KEY=your_api_key_here

4️⃣ Run the App

streamlit run app.py

5️⃣ Upload PDFs & Ask Questions!

• Upload one or multiple research papers.
• Type your question in the input box.
• Get concise, context-aware answers instantly.