microsoft--promptflow/examples/flows/standard/basic-with-builtin-llm/README.md

Basic flow with builtin llm tool

A basic standard flow that calls Azure OpenAI with builtin llm tool.

Tools used in this flow：

• prompt tool
• built-in llm tool

Connections used in this flow:

• azure_open_ai connection

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Setup connection

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

Note in this example, we are using chat api, please use gpt-35-turbo or gpt-4 model deployment.

Ensure you have created open_ai_connection connection before.

pf connection show -n open_ai_connection

Create connection if you haven't done that. Ensure you have put your azure OpenAI endpoint key in azure_openai.yml file.

# Override keys with --set to avoid yaml file changes
pf connection create -f ../../../connections/azure_openai.yml --name open_ai_connection --set api_key=<your_api_key> api_base=<your_api_base>

Run flow

Run with single line input

# test with default input value in flow.dag.yaml
pf flow test --flow .

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

run with multiple lines data

• create run

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_with_builtin_llm")) | .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