Prompt Flow (Part 6)

Neil Haddley • September 1, 2024

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I used Prompt flow, Semantic Kernel and Planner to create an AI application.

I used the Visual Studio Code Prompt flow Extension to create a Chat flow

I used the Visual Studio Code Prompt flow Extension to create a Chat flow

I accepted the default name

I accepted the default name

I ran the flow and asked "what is 2134.567 * 653.23?"It provided an answer that was close but incorrect (the correct result is 1,394,363.20141)

I ran the flow and asked "what is 2134.567 * 653.23?"It provided an answer that was close but incorrect (the correct result is 1,394,363.20141)

The correct result

The correct result

I replaced the large language model chat node with a Python node.The answer to every question I ask is "hello "+the question I asked (not very useful)

I replaced the large language model chat node with a Python node.The answer to every question I ask is "hello "+the question I asked (not very useful)

I added Semantic Kernel to the Python codeI added the Joke function from the FunPlugin.

I added Semantic Kernel to the Python codeI added the Joke function from the FunPlugin.

Now the answer to every question I ask is a Joke (not always useful)

Now the answer to every question I ask is a Joke (not always useful)

I added the MathPlugin and a PlannerNow I can ask fun questions or math questions and the bot can decide which Plugin to call

I added the MathPlugin and a PlannerNow I can ask fun questions or math questions and the bot can decide which Plugin to call

python_node_v8t9.py

TEXT
1from promptflow import tool
2from semantic_kernel import Kernel
3from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
4
5@tool
6async def my_python_tool(input1: str) -> str:
7
8    kernel = Kernel()
9    kernel.remove_all_services()
10    service_id = "default"
11    kernel.add_service(
12        OpenAIChatCompletion(
13            service_id=service_id,
14        ),
15    )
16    plugin = kernel.add_plugin(parent_directory="./prompt_template_samples/", plugin_name="FunPlugin")
17
18    from semantic_kernel.functions import KernelArguments
19
20    joke_function = plugin["Joke"]
21
22    # time travel to dinosaur age
23    
24    joke = await kernel.invoke(
25        joke_function,
26        KernelArguments(input=input1, style="super silly"),
27    )
28    print(joke)
29
30    return str(joke)

python_node_v8t9.py updated

TEXT
1# from promptflow import tool
2from promptflow.core import tool
3from semantic_kernel import Kernel
4from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
5
6@tool
7async def my_python_tool(input1: str) -> str:
8
9    kernel = Kernel()
10    kernel.remove_all_services()
11    service_id = "default"
12    kernel.add_service(
13        OpenAIChatCompletion(
14            service_id=service_id,
15        ),
16    )
17
18    fun_plugin = kernel.add_plugin(parent_directory="./prompt_template_samples/", plugin_name="FunPlugin")
19    math_plugin = kernel.add_plugin(parent_directory="./plugins", plugin_name="MathPlugin")
20
21    from semantic_kernel.planners.function_calling_stepwise_planner import (
22        FunctionCallingStepwisePlanner,
23        FunctionCallingStepwisePlannerOptions,
24    )
25
26    planner = FunctionCallingStepwisePlanner(service_id="default")
27
28    # goal = "Figure out how much I have if first, my investment of 2130.23 dollars increased by 23%, and then I spend $5 on a coffee"  # noqa: E501
29    # goal = "what is 2134.567 * 653.23?" 
30    # goal = "tell me a limeric about cats"
31    goal = input1
32
33    # Execute the plan
34    result = await planner.invoke(kernel=kernel, question=goal)
35
36    print(f"The goal: {goal}")
37    print(f"Plan result: {result.final_answer}")
38
39    return result.final_answer