Workshop on Building Advanced RAG Applications and Systems

📑 Table of Contents

• 📋 Prerequisites
• 🎯 Getting Started
• 🏗️ Set Up Your Local Infrastructure
• ⚡️ Running the Code

📋 Prerequisites

Local Tools

For all the modules, you'll need the following tools installed locally:

Tool	Version	Purpose	Installation Link
Python	3.12	Programming language runtime	Download
uv	≥ 0.4.30	Python package installer and virtual environment manager	Download
GNU Make	≥ 3.81	Build automation tool	Download
Git	≥2.44.0	Version control	Download
Docker	≥27.4.0	Containerization platform	Download

Cloud Services

Also, the course requires access to these cloud services. The authentication to these services is done by adding the corresponding environment variables to the .env file:

Service	Purpose	Cost	Environment Variable	Setup Guide
OpenAI API	LLM API	Pay-per-use	OPENAI_API_KEY	Quick Start Guide

When working locally, the infrastructure is set up using Docker. Thus, you can use the default values found in the config.py file for all the infrastructure-related environment variables.

But, in case you want to deploy the code, you'll need to setup the following services with their corresponding environment variables:

Service	Purpose	Cost	Required Credentials	Setup Guide
MongoDB	document database (with vector search)	Free tier	MONGODB_URI	1. Create a free MongoDB Atlas account 2. Create a Cluster </br> 3. Add a Database User </br> 4. Configure a Network Connection

🎯 Getting Started

1. Clone the Repository

Start by cloning the repository and navigating to the workshop directory:

git clone https://github.com/decodingml/second-brain-ai-assistant-course.git
cd second-brain-ai-assistant-course/workshops/rag
cd solution # or template

2. Installation

To install the dependencies and activate the virtual environment, run the following commands:

uv venv .venv-rag
. ./.venv-rag/bin/activate # or source ./.venv-rag/bin/activate
uv pip install -e .

3. Environment Configuration

Before running any command, you have to set up your environment:

1. Create your environment file:

   cp .env.example .env

2. Open .env and configure the required credentials following the inline comments and the recommendations from the Cloud Services section.

🏗️ Set Up Your Local Infrastructure

We use Docker to set up the local infrastructure (ZenML, MongoDB).

WARNING

Before running the command below, ensure you do not have any processes running on port 27017 (MongoDB).

To start the Docker infrastructure, run:

make local-infrastructure-up

To stop the Docker infrastructure, run:

make local-infrastructure-down

NOTE

To visualize the raw and RAG data from MongoDB, we recommend using MongoDB Compass or Mongo's official IDE plugin (e.g., MongoDB for VS Code). To connect to the working MongoDB instance, use the MONGODB_URI value from the .env file or found inside the config.py file.

⚡️ Running the Code

The ML pipeline consists of three main steps that can be run using Make commands:

1. Data Ingestion Pipeline

   make run-ingestion-pipeline

   This step processes and loads your raw data into the vector database.

2. Generation Pipeline

   make run-generation-pipeline

   This step runs the RAG pipeline to generate responses.

3. Interactive Agent Application

   make run-agent-app

   This launches an interactive application where you can interact with the agentic RAG system.

IMPORTANT

Make sure you have the local infrastructure running (make local-infrastructure-up) before executing any of these pipeline steps.