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README.md

프롬프트 엔지니어링 Code를 이용한 실습

실습을 위한 개발환경 선택하기

해당 실습을 원활하게 제공하기 위해서 .devcontainer 환경을 제공하고 있습니다. 나의 PC에 Docker나 IDE 설치를 원하지 않는다면, CodeSpace를 권장합니다.

  • Local PC에 Docker가 설치되어 있다면, VS Code에서 Reopen DevContainer를 실행하여 Docker에 컨테이너 이미지를 생성하면 자동으로 실습 가능한 런타임과 패키지들이 설치되도록 구성되어 있습니다.
  • GitHub에서 제공하는 CodeSpace를 활용하면, CodeSpace가 제공하는 리모트 VM에 컨테이너가 올라가고, CodeSpace가 제공하는 웹브라우저 용 VS Code를 통해 즉시 개발을 진행할 수 있습니다.

참고: CodeSpace는 GitHub 개인 계정에게 월 15GB의 저장공간과 120 시간/core의 VM을 무료로 제공합니다. 자세한 가격 정보 참고는 클릭

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.

Prepare Conda Environment

Use requirements.txt to install necessary packages

Setup Environmental Variable

After creating Azure OPENAI service, setup 2 environmental variables for

  • OPENAI_API_BASE
  • OPENAI_API_KEY
  • DEPLOYMENT_NAME = 'gpt-35-turbo'

Optional - Used for "OpenAI Large Language Model Chain of Thoughts Demo"

  • AZURE_COGNITIVE_SEARCH_ENDPOINT
  • AZURE_COGNITIVE_SEARCH_KEY

Run Demo Code