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Conditional flow for switch scenario

This example is a conditional flow for switch scenario.

By following this example, you will learn how to create a conditional flow using the activate config.

Flow description

In this flow, we set the background to the search function of a certain mall, use activate config to implement switch logic and determine user intent based on the input queries to achieve dynamic processing and generate user-oriented output.

  • The classify_with_llm node analyzes user intent based on input query and provides one of the following results: "product_recommendation," "order_search," or "product_info".
  • The class_check node generates the correctly formatted user intent.
  • The product_recommendation, order_search, and product_info nodes are configured with activate config and are only executed when the output from class_check meets the specified conditions.
  • The generate_response node generates user-facing output.

For example, as the shown below, the input query is "When will my order be shipped" and the LLM node classifies the user intent as "order_search", resulting in both the product_info and product_recommendation nodes being bypassed and only the order_search node being executed, and then generating the outputs.

conditional_flow_for_switch

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Setup connection

Prepare your Azure Open AI 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.

Create connection if you haven't done that. Ensure you have put your azure open ai 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>

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

# show registered connection
pf connection show --name open_ai_connection

Run flow

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

# test with flow inputs
pf flow test --flow . --inputs query="When will my order be shipped?"
  • Create run with multiple lines of data
# create a random run name
run_name="conditional_flow_for_switch_"$(openssl rand -hex 12)

# create run
pf run create --flow . --data ./data.jsonl --column-mapping query='${data.query}' --stream --name $run_name
  • List and show run metadata
# list created run
pf run list

# show specific run detail
pf run show --name $run_name

# show output
pf run show-details --name $run_name

# visualize run in browser
pf run visualize --name $run_name