Function based flow#

Experimental feature

This is an experimental feature, and may change at any time. Learn more.

User can directly use a function as flow entry.

Function as a flow#

Assume we have a file flow_entry.py:

from promptflow.tracing import trace

class Reply(TypedDict):
    output: str

@trace
def my_flow(question: str) -> Reply:
    # flow logic goes here
    pass

Note function decorated with @trace will emit trace can be viewed in UI provided by PromptFlow. Check here for more information.

Flow test#

Test via function call#

Since flow’s definition is normal python function/callable class. We recommend user directly run it like running other scripts:

from flow_entry import my_flow

if __name__ == "__main__":
    output = my_flow(question="What's the capital of France?")
    print(output)

Convert to a flow and test#

It’s also supported to convert your function entry to a flow and test with prompt flow’s ability.

You can test with the following CLI:

# flow entry syntax: path.to.module:function_name
pf flow test --flow flow_entry:my_flow --inputs question="What's the capital of France?"

Note: currently this command will generate a flow.flex.yaml in your working directory. Which will become the flow’s entry.

Check out a full example here: basic

Chat with a flow#

Start a UI to chat with a flow:

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

Check here for more information.

Batch run#

User can also batch run a flow.

pf run create --flow "path.to.module:function_name" --data "./data.jsonl"


from path.to.module import my_flow
# Note directly run function in `pf.run` is only supported in local PFClient for now 
pf.run(flow=my_flow, data="./data.jsonl")

# user can also directly use entry in `flow` param for batch run
pf.run(flow="path.to.module:function_name", data="./data.jsonl")

Learn more on this topic on Run and evaluate a flow

Define a flow yaml#

User can write a YAML file with name flow.flex.yaml manually or save a function/callable entry to YAML file. This is required for advanced scenario like deployment or run in cloud. A flow YAML may look like this:

$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
entry: path.to.module:function_name
sample:
    inputs:
        question: "what's the capital of France?"

Batch run with YAML#

User can batch run a flow with YAML.

# against flow file
pf run create --flow "path/to/flow/flow.flex.yaml" --data "./data.jsonl"
# against a folder if it has a flow.flex.yaml file
pf run create --flow "path/to/flow" --data "./data.jsonl"

pf = PFClient()
pf.run(flow="./flow.flex.yaml", data="./data.jsonl")

Deploy a flow#

User can serve a flow as a http endpoint locally or deploy it to multiple platforms.

# serve locally from a folder if it has a flow.flex.yaml file
pf flow serve --source "path/to/flow/dir"  --port 8088 --host localhost

# serve locally from certain file
pf flow serve --source "./flow.flex.yaml"  --port 8088 --host localhost

Learn more: Deploy a flow.

Next steps#