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Serverless RAG

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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

Steps to Complete the Quest

Codebase Setup

  1. Fork and Clone the Repository: Start by forking the Serverless RAG with LangChain repository to your GitHub account and then clone it.

    git clone https://github.com/<your-username>/serverless-chat-langchainjs.git

    Navigate to the project directory:

    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.

  3. Pull the Required Models: Open your terminal and run the following commands to pull the necessary models for text completions and embeddings:

    ollama pull llama3.1:latest
ollama pull nomic-embed-text:latest

  4. Install Dependencies: Install the required project dependencies using:

    npm install

  5. Start the Application:

    Launch the application with:

    npm start

    Then, in a separate terminal, run the following command to upload the PDF documents from the /data folder to the API:

    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 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

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

AI Note

This quest was partially created with the help of AI. The author reviewed and revised the content to ensure accuracy and quality.