Minimal chat

A chat flow defined using function with minimal code. It demonstrates the minimal code to have a chat flow.

Prerequisites

Install promptflow sdk and other dependencies in this folder:

pip install -r requirements.txt

What you will learn

In this flow, you will learn

• how to compose a chat flow.
• prompt template format of LLM tool chat api. Message delimiter is a separate line containing role name and colon: "system:", "user:", "assistant:". See OpenAI Chat for more about message role.

  system:
  You are a chatbot having a conversation with a human.
  
  user:
  {{question}}
  

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

• Run as normal Python file

python flow.py

• Test flow

pf flow test --flow flow:chat --inputs question="What's the capital of France?"

• Test flow: multi turn

# start test in chat ui
pf flow test --flow flow:chat --ui 

• Create run with multiple lines data

pf run create --flow flow:chat --data ./data.jsonl --column-mapping question='${data.question}' --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("chat_minimal_")) | .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