📚 Exam Preparation context QnA Bot with Gemini & FAISS :

Chat with your PDFs like they’re alive — upload lecture notes, textbooks, resumes, or any PDF, and ask questions in natural language. The app retrieves context from your documents and combines it with Gemini’s reasoning to answer clearly.

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🚀 Features

• 📄 Upload and process multiple PDFs.
• ✂️ Smart text chunking for long documents.
• 🧠 Vector embeddings with FAISS for semantic search.
• 🤖 Google Gemini integration for natural QnA and summarization.
• 📝 Document summaries generated automatically.
• 💬 Conversation memory to keep context from past questions.
• 📥 Export your entire chat history + document summaries as JSON.

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🛠️ Tech Stack

• Frontend/UI: Streamlit
• Document parsing: PyPDF2
• Chunking: RecursiveCharacterTextSplitter (LangChain)
• Embeddings: SpaCy (en_core_web_sm)
• Vector DB: FAISS
• LLM: Google Gemini (gemini-1.5-flash)
• Config & Env: python-dotenv
• Persistence: JSON export

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🏗️ Architecture

        ┌───────────────┐
        │     PDF(s)    │
        └───────┬───────┘
                │ Extract (PyPDF2)
                ▼
        ┌────────────────────┐
        │ Text Splitter      │
        │ (chunk_size=1000)  │
        └─────────┬──────────┘
                  │
                  ▼
        ┌────────────────────┐
        │ Embeddings (SpaCy) │
        └─────────┬──────────┘
                  │
                  ▼
        ┌────────────────────┐
        │ Vector DB (FAISS)  │
        └─────────┬──────────┘
                  │ Retrieval (top-k)
                  ▼
        ┌─────────────────────────┐
        │ Gemini LLM              │
        │  - Uses context chunks  │
        │  - Falls back to GK if  │
        │    no match found       │
        └─────────┬──────────────┘
                  │
                  ▼
        ┌────────────────────┐
        │ Chat UI (Streamlit)│
        │ + conversation mem │
        └────────────────────┘

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Mechanism :

1. Upload PDF → Extract text → Chunk it → Vectorize → Store in FAISS
2. Ask question → Vectorize → Search in FAISS → Get context → Gemini generates answer
3. If not found → fallback to general AI knowledge
4. Chat + Export feature

⚙️ Installation & Setup

1. Clone repo

   git clone https://github.com/your-repo/pdf-qna-bot.git
   cd pdf-qna-bot

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate   # Mac/Linux
   venv\Scripts\activate      # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Download SpaCy model

   python -m spacy download en_core_web_sm

5. Set environment variable Create a .env file in project root:

   GEMINI_API_KEY=your_api_key_here
   

6. Run app

   streamlit run app.py

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🧪 Usage

1. Upload one or more PDFs from the sidebar.

2. Click "Process Documents" — text is split, embedded, and stored in FAISS.

3. Ask any question in the chat box.

   • If answer is in PDF → retrieved and answered using Gemini.
   • If not found → Gemini provides a fallback general knowledge answer.

4. See summaries per document in the sidebar.

5. Export chat + summaries as JSON.

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Made with ❤️‍🔥 in AI domain.