🧠 AI Knowledge Bot

This is my own custom-built offline AI bot that lets you chat with PDFs and web pages using local embeddings and local LLMs like LLaMA 3.

I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much).

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🚀 Features

• 📄 Chat with uploaded PDF files
• 🌍 Ask questions about a webpage URL
• 🧠 Uses local HuggingFace embeddings (all-MiniLM-L6-v2)
• 🦙 Powered by Ollama + LLaMA 3 (fully offline LLM)
• 🗃️ Built-in FAISS vectorstore
• 🧾 PDF inline preview
• 🧮 Built-in calculator + summarizer tools (via LangChain agents)
• 🧠 Page citation support (know where each answer came from)
• 📜 Chat history viewer with download button (JSON)
• 🎛️ Simple Streamlit UI with dark/light mode toggle
• 👨‍💻 Footer credit: Developed by EzioDEVio

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📦 Tech Stack

• langchain, langchain-community
• sentence-transformers for local embeddings
• ollama for local LLMs (llama3)
• PyPDF2 for PDF parsing
• FAISS for vector indexing
• Streamlit for frontend

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🛠 Setup Guide

1. Clone this repo

git clone https://github.com/EzioDEVio/ai-knowledge-bot.git
cd ai-knowledge-bot

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2. Create and activate virtualenv (optional but recommended)

python -m venv venv
.\venv\Scripts\activate  # Windows for Mac is different

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3. Install dependencies

pip install -r requirements.txt

Make sure sentence-transformers is installed — needed for local embeddings.

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4. Install Ollama (for local LLM)

Download and install from:

👉 https://ollama.com/download

After installation, verify:

ollama --version

Then pull and run the model:

ollama run llama3

This will download the LLaMA 3 model (approx. 4–8GB). You can also try mistral, codellama, etc.

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5. Run the app

streamlit run app.py

The app will open at:

http://localhost:8501

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📁 Folder Structure

ai-knowledge-bot/
├── app.py                     # Main Streamlit UI
├── backend/
│   ├── pdf_loader.py          # PDF text extraction
│   ├── web_loader.py          # Webpage scraper
│   ├── vector_store.py        # Embedding + FAISS
│   └── qa_chain.py            # LLM QA logic (Ollama + tools)
├── .env                       # Not used anymore (was for API keys)
├── requirements.txt
└── README.md

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✅ Working Setup Summary

Component	Mode
Embeddings	Local (HuggingFace)
Vectorstore	Local (FAISS)
LLM Response	Local (Ollama + llama3)
Internet Needed?	❌ Only for first-time model download

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⚠️ Why I Avoided OpenAI / DeepSeek

• OpenAI failed with RateLimitError and quota issues unless I added billing.
• DeepSeek embedding endpoints didn’t work — only chat models supported.

So I switched to:

• 🔁 Local HuggingFaceEmbeddings for vectorization
• 🦙 ChatOllama for full offline AI answers

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✅ Now Completed Features

• ✅ PDF upload + preview
• ✅ URL content QA
• ✅ Chat history with page citations
• ✅ Calculator + summarizer tools
• ✅ Footer attribution
• ✅ JSON export
• ✅ 100% offline functionality

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🐳 Run with Docker (Secure Production Mode)

Build and run the app securely using a multi-stage Dockerfile:

1. Build the container

docker build -t ai-knowledge-bot .

2. Run the container Make sure Ollama is running on the host, open up a powershell or in different terminal then:

docker run -p 8501:8501 \
  --add-host=host.docker.internal:host-gateway \
  ai-knowledge-bot

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🔐 Dockerfile Security Highlights

✅ Multi-stage build (separates dependencies from runtime)

✅ Minimal base (python:3.10-slim)

✅ Non-root appuser by default

✅ .env, venv, logs excluded via .dockerignore

✅ Exposes only necessary port (8501)

✅ Automatically starts Streamlit app

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💬 License

MIT — feel free to fork, use, or improve it.

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🔥 Built by EzioDEVio | 🇮🇶 | 🧠

From concept to offline AI — all step by step.

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