In this pilot course, you'll build a Naive RAG-based question-answer agent that supports the following HTML documents:

• Retrieval-Augmented Generation for Large Language Models: A Survey
• LLM Powered Autonomous Agents

Understanding Naive RAG architecture is the first step towards understanding more advanced architectures and techniques appropriate for production settings.

.
├── .devcontainer  # Docker environment config files
├── 1 query_qa_rag_agent.ipynb  # Start here. Use to check your environment works.
├── 2 indexer.ipynb  # Tutorial 2
├── 3 retriever.ipynb  # Tutorial 3
├── 4 generator.ipynb  # Tutorial 4
├── 5 summary.ipynb  # Tutorial 5
├── LICENSE
├── README.md
├── cheat_code  # Completed, working code you can cheat off of if you get stuck
├── data  # Nothing useful in here for now
├── requirements.txt  # Python packages
└── workshop_code  # The code you will edit in the tutorials

Your development environment will be in a VSCode Dev Container to eliminate "works on my machine" issues. Due to variability in how individual developers configure their base OS, around 50% of course attendees have environment-related issues completing the tutorial if using their base OS instead of a container.

On a clean OS configuration, Python venv will also work. If you are familiar with using venv and are confident your OS doesn't have a conflicting environment manager like conda persistent, you may use venv instead.

Recent OS distributions no longer support system-level installation of Python packages.

If you want to push your changes to your own fork, note that your SSH settings won't be copied to your Dev Container. So, you'll need to push your changes from outside the container on your local machine. Technically, SSH can be configured, but I didn't have time to figure out how to make SSH on Dev Container automatically work on both Windows and Mac.

If you want to inspect the environment configuration, check out the folder ".devcontainer/". Feel free to ask any questions.

We will use VSCode for this tutorial because it supports both Python Notebooks and standard Python editing. Although other editors have their merits, VSCode is also the most standard editor in 2024. The containerized development environment is only tested to work with VSCode. You must use VSCode to get instructor or TA support during the workshop.

You will use this extension to develop within the Docker container dev environment.

Open VSCode > Extensions. Search for "Remote Containers". Click install.

If you're not sure, check if you have git installed already. Copy and paste this commmand into your terminal:

if command -v git &>/dev/null; then echo "Git is installed and on the PATH"; else echo "Git is not installed or not on the PATH"; fi

If it's not installed, install git using Homebrew, Apt, etc as appropriate.

Easy option: install Git Bash

Microsoft Official Option:

1. Install Windows Subsystem for Linux
2. Open Ubuntu shell
3. apt install git

1. Go to the Github repo for this course
2. Click Fork
3. Clone your forked repo with a terminal
4. cd into the rag-mini-bootcamp repo folder

We will use OpenAI embeddings, GPT3.5, and Pinecone vector database for this tutorial, so you will need to securely store your API keys in a .env file. This file is in .gitignore so it doesn't accidentally get committed to the repo where your keys would be exposed.

Create a .env file in the root of the repo from this template:

OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
PINECONE_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxx

Go to OpenAI API settings. Create a new API key and copy it to the .env file. If you're out of free trial credits, purchase $5 of credits.

Create a Pinecone account. Create a new index.

• Index Name: something like "tobys-sandbox"
• Configuration: Setup by model > text-embedding-3-small
• Capacity Mode: serverless
• Cloud Provider: doesn't matter
• Region: default
• Click "Create Index"

Open the folder "rag-mini-bootcamp" in VSCode. Open the folder in Dev Container mode by using Cmd/Ctrl + Shift + P > Dev Containers: Open Folder in Container. The first time, the container will take some time to build while it downloads the required packages.

Once the container is finished building, open ./query_qa_rag_agent.ipynb in VSCode. Click "Run All". The notebook should display a GPT3.5 completion to the question about the paper. If you get an error, try to diagnose it or flag one of us for help.