quest 3

Author: juliamuiruri4

03-Run-Serverless-RAG-Support-System/README.md

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+![Serverless RAG](./assets/favicon.png)
+
+Livestream starting soon! **Click below to register.**
+
+[![Reactor Livestream](./assets/poster-quest3.png)](https://developer.microsoft.com/en-us/reactor/events/26775/)
+
+## Overview
+
+In this quest, you will set up and run a **Serverless Retrieval-Augmented Generation (RAG)** support system using the provided codebase. Once you have completed the setup, you will start a CodeTour that will guide you through each step of the RAG implementation with detailed explanations.
+
+![Serverless RAG - CodeTour](./assets/codetour-setup.png)
+
+## Steps to Complete the Quest
+
+### Codebase Setup
+
+1. **Fork and Clone the Repository**: Start by [forking](https://github.com/Azure-Samples/serverless-chat-langchainjs/fork) the Serverless RAG with LangChain repository to your GitHub account and then clone it.
+
+    ```bash
+    git clone https://github.com/<your-username>/serverless-chat-langchainjs.git
+    ```
+
+    Navigate to the project directory:
+
+    ```bash
+    cd serverless-chat-langchainjs
+    ```
+
+2. **Download Ollama** (if you haven't already): You won't need to deploy to Azure for this quest, but you will need to connect to local models for text completions and embeddings. 
+
+    > [!NOTE]  
+    > Foundry Local doesn't support embeddings models yet, so you'll need to use Ollama for this quest.
+
+    Download and install Ollama from [ollama.com](https://ollama.com/).
+
+3. **Pull the Required Models**: Open your terminal and run the following commands to pull the necessary models for text completions and embeddings:
+
+    ```bash
+    ollama pull llama3.1:latest
+    ollama pull nomic-embed-text:latest
+    ```
+4. **Install Dependencies**: Install the required project dependencies using:
+
+    ```bash
+    npm install
+    ```
+
+5. **Start the Application**: 
+
+    Launch the application with:
+
+    ```bash
+    npm start
+    ```
+
+    Then, in a separate terminal, run the following command to upload the PDF documents from the `/data` folder to the API:
+
+    ```bash
+    npm run upload:docs
+    ```
+
+    Interact with the application by asking questions related to the uploaded documents and observe how the RAG system retrieves and generates responses based on the content.
+
+    > [!NOTE]  
+    > While local models usually work well enough to answer the questions, sometimes they may not be able to perfectly follow the advanced formatting instructions for the citations and follow-up questions.
+    >
+    > This is expected, and a limitation of using smaller local models.
+
+### Start the CodeTour
+
+This quest is designed to give you a guided tour of the codebase and its implementation of the complete RAG pipeline. To start the CodeTour:
+
+1. **Install the CodeTour Extension**: If you haven't already, install the [CodeTour extension](https://marketplace.visualstudio.com/items?itemName=vsls-contrib.codetour) in Visual Studio Code.
+
+2. **Open the CodeTour**: Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P on Mac) and type "**CodeTour: Start Tour**".
+
+    ![Start CodeTour](./assets/codetour-start.png)
+
+There are 6 tours to walk you through the entire RAG implementation flow. We recommend going through them in order as they build upon each other.
+
+- Tour 1: RAG Architecture (10 steps)
+- Tour 2: Document Ingestion (10 steps)
+- Tour 3: Vector Storage (8 steps)
+- Tour 4: Query & Retrieval (7 steps)
+- Tour 5: Response Generation (7 steps)
+- Tour 6: Streaming & Chat History (8 steps)
+
+### Return to the Build-a-thon
+
+Once you have completed the CodeTour and explored the RAG implementation, return to the main Build-a-thon repository to continue with the next quests.
+
+## Stay connected
+
+Have a question, project or insight to share? Post in the [RAG discussion hub](https://github.com/Azure-Samples/JavaScript-AI-Buildathon/discussions/89)
+
+## AI Note
+
+This quest was partially created with the help of AI. The author reviewed and revised the content to ensure accuracy and quality.