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Basic standard flow

A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables.

Tools used in this flow：

  • prompt tool
  • custom python Tool

Connections used in this flow:

  • None

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Run flow

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

  • Setup environment variables

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

cat .env
  • Test flow/node
# test with default input value in flow.dag.yaml
pf flow test --flow .

# test with flow inputs
pf flow test --flow . --inputs text="Java Hello World!"

# test node with inputs
pf flow test --flow . --node llm --inputs prompt="Write a simple Hello World program that displays the greeting message when executed."
  • Create run with multiple lines data
# using environment from .env file (loaded in user code: hello.py)
pf run create --flow . --data ./data.jsonl --stream
  • 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_default")) | .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 with connection

Storing connection info in .env with plaintext is not safe. We recommend to use pf connection to guard secrets like api_key from leak.

  • Show or create open_ai_connection
# create connection from `azure_openai.yml` file
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>

# check if connection exists
pf connection show -n open_ai_connection
  • Test using connection secret specified in environment variables Note: we used ' to wrap value since it supports raw value without escape in powershell & bash. For windows command prompt, you may remove the ' to avoid it become part of the value.
# test with default input value in flow.dag.yaml 
pf flow test --flow . --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}'
  • Create run using connection secret binding specified in environment variables, see run.yml
# create run
pf run create --flow . --data ./data.jsonl --stream --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}'
# create run using yaml file
pf run create --file run.yml --stream

# show outputs
name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_default")) | .name'| head -n 1 | tr -d '"')
pf run show-details --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 --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --stream --runtime demo-mir
# run using yaml file
pfazure run create --file run.yml --stream --runtime demo-mir
  • 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_default")) | .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