🤖 AI Knowledge Assistant

An AI-powered Knowledge Assistant built using LangChain, Streamlit, Ollama, ChromaDB, and HuggingFace Embeddings.

The application allows users to upload documents, process websites, build a local knowledge base, and chat with AI using Retrieval-Augmented Generation (RAG) — all without requiring any API key.

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🚀 Features

• 📄 Upload PDF Documents
• 🌐 Process Website URLs
• 🧠 Retrieval-Augmented Generation (RAG)
• 🔍 Semantic Search using Vector Embeddings
• 💾 ChromaDB Vector Database
• 🤖 Multiple Ollama Model Support
• 💬 Conversational Chat Interface
• 📝 Chat Memory
• 🔒 Runs Completely Locally
• 🔑 No API Key Required

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🛠️ Technologies Used

• Python
• Streamlit
• LangChain
• Ollama
• ChromaDB
• HuggingFace Embeddings
• BeautifulSoup
• Requests

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📂 Project Structure

AI-Knowledge-Assistant/
│
├── project3.py
├── requirements.txt
├── README.md
├── .gitignore
└── project3.ipynb

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⚙️ System Requirements

Software Required

• Python 3.10 or higher
• Git
• Ollama

Download Links

Python: https://www.python.org/downloads/

Git: https://git-scm.com/downloads

Ollama: https://ollama.com/download

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📥 Clone the Repository

git clone https://github.com/maherdhami/AI-Knowledge-Assistant.git

cd AI-Knowledge-Assistant

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🐍 Create Virtual Environment

Windows

python -m venv venv

venv\Scripts\activate

Linux / Mac

python3 -m venv venv

source venv/bin/activate

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📦 Install Dependencies

pip install -r requirements.txt

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🤖 Install Ollama Model

Pull at least one model before running the application.

Recommended:

ollama pull gemma3:latest

Optional Models:

ollama pull llama3:8b

ollama pull gemma:2b

ollama pull glm-4.7-flash:latest

Verify installed models:

ollama list

Example Output:

NAME
gemma3:latest
llama3:8b
gemma:2b
glm-4.7-flash:latest

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▶️ Start Ollama

Open a terminal and run:

ollama serve

Keep this terminal running.

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▶️ Run the Application

Open another terminal.

Activate virtual environment:

Windows

venv\Scripts\activate

Linux / Mac

source venv/bin/activate

Run Streamlit:

streamlit run project3.py

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🌐 Open the Application

Open your browser and visit:

http://localhost:8501

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📚 How to Use

Step 1: Select AI Model

Choose any installed Ollama model from the sidebar.

Recommended:

gemma3:latest

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Step 2: Upload Documents

Upload one or more PDF documents.

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Step 3: Process Documents

Click:

Process Documents

The application will:

• Extract document content
• Split text into chunks
• Generate embeddings
• Store vectors in ChromaDB
• Create a searchable knowledge base

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Step 4: Ask Questions

Example Queries:

Summarize this document.

What are the key findings?

Explain chapter 3.

List important points.

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Step 5: Website Knowledge Base

Enter a website URL:

https://en.wikipedia.org/wiki/Artificial_intelligence

Click:

Process Documents

The website content becomes part of the knowledge base.

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🔒 Privacy

• Runs entirely on local machine
• No OpenAI API required
• No Groq API required
• No Gemini API required
• User data remains local
• Uploaded files are not sent to external services

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🎯 Quick Start

git clone https://github.com/maherdhami/AI-Knowledge-Assistant.git

cd AI-Knowledge-Assistant

python -m venv venv

venv\Scripts\activate

pip install -r requirements.txt

ollama pull gemma3:latest

ollama serve

Open a second terminal:

cd AI-Knowledge-Assistant

venv\Scripts\activate

streamlit run project3.py

Open:

http://localhost:8501

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📖 Academic Purpose

This project demonstrates:

• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Local Large Language Models (LLMs)
• Semantic Search
• LangChain Framework
• Streamlit Application Development
• Document Question Answering Systems

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👨‍💻 Author

Maher Dhami

GitHub: https://github.com/maherdhami

LinkedIn: https://www.linkedin.com/in/maher-dhami-a15197225/

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📄 License

This project is developed for educational and academic learning purposes.

Feel free to use, modify, and learn from the code.