🌐 Universal Offline AI Chatbot

Build your own domain-specific chatbot — offline, modular, and blazing fast.

The Universal Offline AI Chatbot is a privacy-respecting, offline-ready assistant that can chat over any set of PDFs. It’s ideal for legal, cybersecurity, academic, enterprise, or technical domains.

It uses a locally hosted LLM (mistral:instruct via Ollama) and semantic search powered by HuggingFace embeddings and FAISS. You get fast, accurate responses, without sending anything to the cloud.

GitHub Link: https://github.com/AdityaBhatt3010/Universal-Offline-AI-Chatbot
DockerHub Link: https://hub.docker.com/r/adityabhatt3010/ai-chatbot

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✨ Highlights

• 🔐 Fully offline-capable with local LLM (via Ollama)
• 📄 Works out-of-the-box with your PDFs
• 🧠 Semantic vector search using all-MiniLM-L6-v2
• ⚡️ Fast and responsive using FAISS backend
• 🧩 Modular, extendable architecture (Streamlit frontend + CLI)
• 🐳 Docker-ready for deployment
• 📸 UI Preview with screenshots
• ✅ Built-in CI/CD check via GitHub Actions
• 🎯 Fully reproducible setup via PowerShell script or Docker

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🧱 Tech Stack

Layer	Stack
LLM	mistral:instruct via Ollama
Embeddings	all-MiniLM-L6-v2 via SentenceTransformers
Vector Store	FAISS (in-memory + disk)
Framework	LangChain (v0.2+)
Language	Python 3.11+
UI	Streamlit
Container	Docker
CI/CD	GitHub Actions (.github/workflows/python.yml)

⚠️ HuggingFace Token is required to fetch the embedding model once. It's cached locally afterward.

Example .env:

HF_TOKEN=your_huggingface_token_here

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💡 Use Cases

Chatbot Type	Add These PDFs
👨‍⚖️ LawyerBot	Legal, Constitution, HR documents
🧬 ResearchBot	Whitepapers, scientific papers
🛡️ CyberSecBot	SOC2, GDPR, ISO27001, NIST docs
📚 EdTechBot	Notes, textbooks, question banks
🧑‍💼 HR/CompanyBot	SOPs, onboarding docs, HR policies

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📁 Project Structure

Universal-Offline-AI-Chatbot/
│
├── data/                   # Place your PDF documents here
│   └── Try.pdf
│
├── Screenshots/           # UI snapshots
│   ├── Loading_Screen.png
│   └── Running_the_Model.png
│
├── src/                   # Modular source code
│   ├── chunker.py
│   ├── config.py
│   ├── embedding.py
│   ├── loader.py
│   ├── model_loader.py
│   ├── prompts.py
│   ├── qa_chain.py
│   ├── utils.py
│   └── vectorstore.py
│
├── vectorstore/           # Local FAISS vector index
│   └── db_faiss/
│
├── Bot.py                 # CLI script
├── Bot.ipynb              # Jupyter notebook version
├── main.py                # Entry-point (optional)
├── streamlit_app.py       # Frontend UI (Streamlit)
├── requirements.txt       # Python dependencies
├── setup.ps1              # PowerShell setup script
├── Dockerfile             # Docker image definition
├── .dockerignore
├── setup.py             # PyPI config
├── pyproject.toml       # Modern packaging metadata
├── MANIFEST.in          # Source packaging rules
├── .env                   # Contains HF_TOKEN
├── README.md
└── LICENSE

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🧰 Setup Instructions

🖥️ One-Click Setup (Windows Only)

.\setup.ps1

This will:

• Create virtual env
• Install dependencies
• Pull Mistral via Ollama
• Ask for Hugging Face token
• Build Docker image

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🛠 Manual Setup

1. Install Python Requirements

pip install -r requirements.txt

2. Install & Pull Ollama Model

ollama pull mistral:instruct

3. Set HuggingFace Token (First Time Only)

export HUGGINGFACEHUB_API_TOKEN=your_token      # macOS/Linux
set HUGGINGFACEHUB_API_TOKEN=your_token         # Windows CMD

4. Run the CLI Bot

python Bot.py

📦 Install from PyPI

pip install universal-offline-ai-chatbot

After installation, you can launch the CLI like this:

universal-offline-ai-chatbot

This runs the main.py CLI bundled in the PyPI package.

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🌐 Run with Streamlit Frontend

streamlit run streamlit_app.py

📸 Streamlit Preview

⏳ Loading Screen

🤖 Chat in Action

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🐳 Docker Support

Prerequisites

• Docker installed & running
• .env file containing HF_TOKEN (Hugging Face token)

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🛠️ Docker Build & Run

To build and run the chatbot using Docker, follow these steps:

1. Build the Docker image:

   docker build -t ai-chatbot .

2. Run the container (with volume mount and token):

   docker run -p 8501:8501 --env-file .env -v ${PWD}/data:/app/data ai-chatbot

   This will:

   • Map the container's port 8501 to local 8501
   • Use your local .env for HF_TOKEN
   • Mount the data/ folder into the container for access to PDFs

Access the chatbot at http://localhost:8501

📸 Screenshot:

🐳 Building the Image

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🔄 Using Your Own PDFs

# Replace default file(s)
mv your_files/*.pdf ./data/

# Re-run the bot or restart Streamlit
python Bot.py

Automatically re-indexes your new documents using FAISS.

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🧪 Sample Interaction

🧠 You: What does Article 21 state?

🤖 Bot: Article 21 of the Indian Constitution guarantees the protection of life and personal liberty...

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🧑‍💻 Author

Aditya Bhatt
Cybersecurity Specialist | VAPT Expert | OSS Contributor
GitHub | Medium

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