This repository contains the materials for Session 5 of Applied NLP.

• Slides: see slides/ folder
• Notebooks: see notebooks/ folder

This session introduces Retrieval-Augmented Generation (RAG), a technique that combines information retrieval with large language models to answer questions based on external documents.

1. Text Chunking — Split long documents into overlapping chunks for efficient retrieval
2. Embeddings — Convert text chunks into semantic vector representations
3. Vector Databases — Store and search embeddings using FAISS
4. Retrieval Chains — Build a complete RAG pipeline connecting retrieval + generation
5. Local LLM Integration — Use Ollama for API-free local inference

We use Lewis Carroll's two Alice books as our corpus like all previous sessions:

• Alice's Adventures in Wonderland
• Through the Looking-Glass

• LangChain — Framework for building LLM applications
• FAISS — Facebook AI Similarity Search for vector storage
• Sentence Transformers — Embedding models (all-mpnet-base-v2)
• Ollama — Local LLM runtime (using llama3.2 model)
• LangSmith — Prompt hub for retrieval templates

Before starting, please fork this repository and create a fresh Python virtual environment.
All required libraries are listed in requirements.txt.

⚠️ If you encounter errors during pip install, try removing the version pinning for the failing package(s) in requirements.txt.
On Apple M1/M2 systems you may also need to install additional system packages (the “M1 shizzle”).

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate

# Upgrade pip and install dependencies
pip install --upgrade pip
pip install -r requirements.txt

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1

# Upgrade pip and install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt

# Select Python version (if using pyenv)
pyenv local 3.11.3

# Create and activate virtual environment
python -m venv .venv
source .venv/Scripts/activate

# Upgrade pip and install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt

You’re now ready to run the session notebooks!

Deactivate the environment when you’re done:

deactivate