Step 1: Open WSL Terminal

Open Windows Terminal, PowerShell, or Command Prompt and launch WSL:

wsl

(Optionally navigate to your project directory in WSL, e.g. cd /mnt/c/Projects/LLM) ──────

Step 2: Install & Start Ollama in WSL

Ollama serves the local LLM (llama3:8b) that aichatbot.py uses for generation.

1. Install Ollama (inside your WSL shell): curl -fsSL https://ollama.com/install.sh | sh

2. Start the Ollama Server (run in the background or a separate WSL terminal window): ollama serve

3. Pull the Llama model: ollama pull llama3.2:3b

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Step 3: Set Up Python Virtual Environment & Install Dependencies

Inside your WSL terminal, navigate to your project folder and set up Python:

1. Navigate to project folder: cd /mnt/c/Projects/LLM

2. Install required Linux system packages (if not already installed): sudo apt update sudo apt install -y python3-venv python3-pip rsync

3. Create and activate a Linux virtual environment: python3 -m venv venv_wsl source venv_wsl/bin/activate

4. Install Python packages: pip install --upgrade pip pip install docling chromadb sentence-transformers transformers watchdog rank_bm25 python-dotenv langchain langchain-core langchain-ollama

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Step 4: Run the Ingestion Pipeline (ingest.py)

This parses your PDFs in ./docs/, creates text chunks, computes embeddings, and builds the ChromaDB index in ./chromadb_index/.

1. Run ingestion: python3 ingest.py --reset

2. Run a test query to verify indexing: python3 ingest.py --test-query "What are the requirements for graduation?" You should see top matching text chunks with relevance scores printed to your screen. ──────

Step 5: Run the Kiosk Chatbot (aichatbot.py)

Now start the interactive RAG chatbot in your active WSL virtual environment:

python3 aichatbot.py

• Ask questions: Type any question related to the documents in ./docs/. • View citations: Type sources after getting an answer to inspect exact page numbers and section headings. • Reset context: Type clear to reset chat history. • Exit: Type exit or quit