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------ # Workshop on Building Advanced RAG Applications and Systems # 📑 Table of Contents - [📋 Prerequisites](#-prerequisites) - [🎯 Getting Started](#-getting-started) - [🏗️ Set Up Your Local Infrastructure](#-set-up-your-local-infrastructure) - [⚡️ Running the Code](#️-running-the-code-for-each-module) # 📋 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](https://www.python.org/downloads/) | | uv | ≥ 0.4.30 | Python package installer and virtual environment manager | [Download](https://github.com/astral-sh/uv) | | GNU Make | ≥ 3.81 | Build automation tool | [Download](https://www.gnu.org/software/make/) | | Git | ≥2.44.0 | Version control | [Download](https://git-scm.com/downloads) | | Docker | ≥27.4.0 | Containerization platform | [Download](https://www.docker.com/get-started/) | ## 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](https://openai.com/index/openai-api/) | LLM API | Pay-per-use | `OPENAI_API_KEY` | [Quick Start Guide](https://platform.openai.com/docs/quickstart) | When working locally, the infrastructure is set up using Docker. Thus, you can use the default values found in the [config.py](src/second_brain_offline/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](https://rebrand.ly/second-brain-course-mongodb) | document database (with vector search) | Free tier | `MONGODB_URI` | 1. [Create a free MongoDB Atlas account](https://rebrand.ly/second-brain-course-mongodb-setup-1)
2. [Create a Cluster](https://rebrand.ly/second-brain-course-mongodb-setup-2)
3. [Add a Database User](https://rebrand.ly/second-brain-course-mongodb-setup-3)
4. [Configure a Network Connection](https://rebrand.ly/second-brain-course-mongodb-setup-4) | # 🎯 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: ```bash 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: ```bash cp .env.example .env ``` 2. Open `.env` and configure the required credentials following the inline comments and the recommendations from the [Cloud Services](#-prerequisites) 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: ```bash make local-infrastructure-up ``` To stop the Docker infrastructure, run: ```bash make local-infrastructure-down ``` > [!NOTE] > To visualize the raw and RAG data from MongoDB, we recommend using [MongoDB Compass](https://rebrand.ly/second-brain-course-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](src/second_brain_offline/config.py) file. ![mongodb_atlas_example.png](../../../static/mongodb_atlas_example.png) # ⚡️ Running the Code The ML pipeline consists of three main steps that can be run using Make commands: 1. **Data Ingestion Pipeline** ```bash make run-ingestion-pipeline ``` This step processes and loads your raw data into the vector database. 2. **Generation Pipeline** ```bash make run-generation-pipeline ``` This step runs the RAG pipeline to generate responses. 3. **Interactive Agent Application** ```bash 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.