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Basic standard flow

A basic standard flow define using function entry that calls Azure OpenAI with connection info stored in environment variables.

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Run flow

  • Prepare your Azure OpenAI resource follow this instruction and get your api_key if you don't have one.

  • Setup environment variables

Ensure you have put your azure OpenAI endpoint key in .env file. You can create one refer to this example file.

cat ../.env
  • Run/Debug as normal Python file
python programmer.py
  • Test with flow entry
pf flow test --flow programmer:write_simple_program --inputs text="Java Hello World!"
  • Test with flow yaml
# test with sample input value in flow.flex.yaml
pf flow test --flow .
# test with UI
pf flow test --flow . --ui
  • Create run with multiple lines data
# using environment from .env file (loaded in user code: hello.py)
pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --stream

You can also skip providing column-mapping if provided data has same column name as the flow. Reference here for default behavior when column-mapping not provided in CLI.

  • List and show run meta
# list created run
pf run list

# get a sample run name

name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_")) | .name'| head -n 1 | tr -d '"')
# show specific run detail
pf run show --name $name

# show output
pf run show-details --name $name

# visualize run in browser
pf run visualize --name $name

Run flow in cloud with connection

  • Assume we already have a connection named open_ai_connection in workspace.
# set default workspace
az account set -s <your_subscription_id>
az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
  • Create run
# run with environment variable reference connection in azureml workspace
pfazure run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_ENDPOINT='${open_ai_connection.api_base}' --stream
# run using yaml file
pfazure run create --file run.yml --stream
  • List and show run meta
# list created run
pfazure run list -r 3

# get a sample run name
name=$(pfazure run list -r 100 | jq '.[] | select(.name | contains("basic_")) | .name'| head -n 1 | tr -d '"')

# show specific run detail
pfazure run show --name $name

# show output
pfazure run show-details --name $name

# visualize run in browser
pfazure run visualize --name $name