sashgeorge/semantickernel_promptflow

Using planner

This console application demonstrates the final solution to the planner doc article.

Prerequisites

• Python 3.8 and above
  • Poetry is used for packaging and dependency management
  • Semantic Kernel Tools

Configuring the sample

The sample can be configured with a .env file in the project which holds api keys and other secrets and configurations.

Make sure you have an Open AI API Key or Azure Open AI service key

Copy the .env.example file to a new file named .env. Then, copy those keys into the .env file:

GLOBAL__LLM_SERVICE="OpenAI"

AZURE_OPEN_AI__DEPLOYMENT_TYPE="chat-completion"
AZURE_OPEN_AI__CHAT_COMPLETION_DEPLOYMENT_NAME="gpt-35-turbo"
AZURE_OPEN_AI__TEXT_COMPLETION_DEPLOYMENT_NAME="text-davinci-003"
AZURE_OPEN_AI__ENDPOINT=""
AZURE_OPEN_AI__API_KEY=""

OPEN_AI__MODEL_TYPE="chat-completion"
OPEN_AI__CHAT_COMPLETION_MODEL_ID="gpt-3.5-turbo"
OPEN_AI__TEXT_COMPLETION_MODEL_ID="text-davinci-003"
OPEN_AI__API_KEY=""
OPEN_AI__ORG_ID=""

Running the sample

To run the console application within Visual Studio Code, just hit F5. As configured in launch.json and tasks.json, Visual Studio Code will run poetry install followed by python hello_world/main.py

To build and run the console application from the terminal use the following commands:

poetry install
poetry run python main.py