Unify AI helps you use a LLM from a wide variety of models and providers using a single Unify API key. You can make an optimal choice by comparing trade-offs between quality, cost and latency.

Refer Unify AI documentation.

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Run flow

Prepare your Unify AI account follow this instruction and get your api_key if you don't have one.
Setup environment variables

Ensure you have put your Unify 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

# 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 UNIFY_AI_API_KEY='<unify_api_key>' UNIFY_AI_BASE_URL='https://api.unify.ai/v0/' --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