🚀 QueryMyPDF — Chat with Your PDFs Locally (RAG + FAISS + Llama3)

QueryMyPDF is a fully offline AI-powered document assistant.

Upload PDFs → Ask questions → Get intelligent answers —
without cloud APIs, billing, or quotas.

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🔥 Powered By

• Ollama (Llama3 Local LLM) - Machine Inference
• LangChain
• FAISS Vector Store
• HuggingFace Embeddings (all-MiniLM-L6-v2)
• PDFPlumber for text extraction
• Streamlit UI

👉 Internet connection not required
👉 No API bills or tokens
👉 Runs entirely on your machine

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✨ Features

✔ Upload PDFs
✔ Extract text using PDFPlumber
✔ Local vector embeddings (MiniLM)
✔ RAG search over documents
✔ Query using Llama3 via Ollama
✔ Shows document-referenced answers

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🧱 Architecture Overview

📄 PDF → 🔍 Extract Text ↓ 🧩 Chunking → 📌 Embedding → 🎯 FAISS Vector DB ↓ 🔎 Retrieval ↓ 🧠 Local Llama3 → generates response using context

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🛠 Tech Stack

Component	Technology
UI	Streamlit
Embeddings	HuggingFace Sentence Transformers
Vector DB	FAISS
Model Runtime	Ollama
Local LLM	Llama3
PDF Parsing	pdfplumber
RAG Logic	LangChain community components

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📂 Project Structure

QueryMyPDF/ │── app.py # Streamlit interface │── RAG_backend.py # Core RAG logic using Ollama │── requirements.txt # Dependencies │── README.md # Documentation └── venv (ignore)

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📸 Screenshots

📄 PDF Uploading Section

2.

🤖 AI Response Output alongwith Source Doc

Source Response 3. ,

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🔧 Installation

1️⃣ Clone repository

git clone https://github.com/chaudhary-pawan/QueryMyPDF.git
cd QueryMyPDF

2️⃣ Install Ollama (REQUIRED)

👉 Download from:

https://ollama.com/download

Then pull the model:

ollama pull llama3

3️⃣ Create a virtual environment python -m venv venv

4️⃣ Install dependencies pip install -r requirements.txt

▶️ Run the app streamlit run app.py

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✔ Upload PDFs ✔ Ask questions ✔ Get answers offline