azure-openai-in-a-day-workshop

In this technical workshop, you will get a comprehensive introduction to Azure OpenAI Service and Azure OpenAI Studio. You will learn how to create and refine prompts for various scenarios using hands-on exercises. You will also discover how to leverage Azure OpenAI Service to access and analyze your company data. Moreover, you will explore existing solution accelerators and best practices for prototyping and deploying use cases end-to-end. The workshop will end with a Q&A session and a wrap-up.

Workshop agenda

🌅 Morning (9:00 – 12:00)

Fokus: Introduction and first steps

• 📣 Intro (90min)
  • Into Workshop (15mins)
  • Intro Azure OpenAI Service (30mins)
  • Azure Azure OpenAI Studio (45mins)
• 🧑🏼‍💻 Prompt Engineering Exercises using Studio (90mins)

🌆 Afternoon (1:00 – 4:30)

Focus: Solutions

• Recap (15min)
• 📣 Using Azure OpenAI Service to access company data (60min)
  • How to bring your own data
  • Fine-tuning and embedding
  • Solutions Accelerators
• QnA session (30min)
• 💻 Hands-on lab on two exemplay use-cases (90min)

^{📣 Presentation, 🧑🏼‍💻 Hands-on lab}

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Preparation

This is only required for the hands-on lab. If you are only attending the presentation, you can skip this section.

Azure OpenAI Service subscription and deployments

Grant the participant access to the Azure OpenAI Service subscription and create the required deployments.

Ideally, grant the participants access to Azure OpenAI Service service be assigning the Cognitive Service OpenAI user. If the participant is a Cognitive Service OpenAI contributor, they can create the following deployments themselves.

Otherwise, create 'text-davinci-003' and 'text-embedding-ada-002' deployments (and assign the participant to the deployments).

There are two ways to authenticate (see Jupyter notebooks):

1. (Recommended) Use the Azure CLI to authenticate to Azure and Azure OpenAI Service
2. Using a token (not needed if using the Azure CLI)

Get the Azure OpenAI Service endpoint (and key) from the Azure portal.

Workspace environment

Choose one of the following options to set up your environment: Codespaces, Devcontainer or bring your own environment (Anaconda). Building the environment can take a few minutes, so please start early.

1️⃣ Codespaces

🌟 Highly recommended: Best option if you already have a Github account. You can develop on local VSCode or in a browser window.

• Go to Github repository and click on Code button
• Create and edit the .env file in the base folder including Azure OpenAI Service endpoint and key before starting any notebooks

2️⃣ Devcontainer

Usually a good option if VSCode and Docker Desktop are already installed.

• Install Docker
• Install Visual Studio Code
• Install Remote - Containers extension
• Clone this repository
• Open the repository in Visual Studio Code
• Click on the green button in the bottom left corner of the window
• Select Reopen in Container
• Wait for the container to be built and started
• Create and edit .env file in the base folder including Azure OpenAI Service endpoint and key before starting any notebooks

3️⃣ Bring your own environment

If you already have a Python environment with Jupyter Notebook and the Azure CLI installed.

Make sure you have the requirements installed in your Python environment using pip install -r requirements.txt.

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Content of the repository

• 💡 Guideline for writing better prompts

Exercises

• 💪 Simple prompt writing exercises
• 💪 Quickstart - just to make sure everything works!
• 💪 Preprocessing - principles of preprocessing and token handling!
• 💪 Q&A with embeddings
• 💪 Unsupervised movie classification and recommendations
• 💪 Email Summarization and Answering App

Solutions

Do not cheat! 😅

• 💡 Solution - Q&A with embeddings
• 💡 Solution - Unsupervised movie classification and recommendations
• 💡 Solution - Email Summarization and Answering App

Q&A Quick Start

If you want to quickly create a Q&A webapp using your own data, please follow the quickstart guide notebook.

If you want to use LangChain to build an interactive chat experience on your own data, follow the quickstart chat on private data using LangChain.

If you want to use LlamaIndex 🦙 (GPT Index), follow the quickstart guide notebook with llama-index.