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Basic chat

A basic chat flow defined using class entry. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.

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}}
  • how to consume chat history in prompt. 
    {% for item in chat_history %}
{{item.role}}:
{{item.content}}
{% endfor %}

Run flow

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

  • Setup connection

Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of prompty supported connection types and fill in the configurations.

Or use CLI to create connection:

# 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> --name open_ai_connection

Note in flow.flex.yaml we are using connection named open_ai_connection.

# show registered connection
pf connection show --name open_ai_connection
  • Run as normal Python file
python flow.py
  • Test flow
pf flow test --flow flow:ChatFlow --init init.json --inputs question="What's Azure Machine Learning?"
  • Test flow with yaml You'll need to write flow entry flow.flex.yaml to test with prompt flow.
# run chat flow with default question in flow.flex.yaml
pf flow test --flow . 
# run chat flow with new question
pf flow test --flow . --inputs question="What is ChatGPT? Please explain with consise statement."
# run chat flow with specific init and inputs
pf flow test --flow . --init init.json --inputs sample.json
  • Test flow: multi turn
# start test in chat UI
pf flow test --flow . --ui --init init.json
  • Create run with multiple lines data
pf run create --flow . --init init.json --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_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

  • 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 . --init init.json --data ./data.jsonl --column-mapping question='${data.question}' --stream
# run using yaml file
pfazure run create --file run.yml --init init.json --stream