Files
semantic-kernel/python/samples/kernel-syntax-examples/azure_openai_function_calling_stepwise_planner.py
T
Eduard van Valkenburg 6751f9d819 Python: Updated plugins (#5827)
### Motivation and Context

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  1. Why is this change required?
  2. What problem does it solve?
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The loading of plugins and functions into Kernel was not fully
consistent and clear, this is now simplified.

Adding a plugin can now be done in two ways:
- multiple: `kernel.add_plugins[plugin1, plugin2]`
- single plugin: `kernel.add_plugin(plugin)`
- if the plugin here is a class, with methods that have the
kernel_function decorator then that is parsed into a plugin

Adding a function can now be done in three ways:
- multiple: `kernel.add_functions([func1, func2]` or
`kernel.add_function({'func1': func1})`
- single: `kernel.add_function('plugin_name', func1)`
- from a prompt: `kernel.add_function(function_name='func1',
prompt='test prompt', ...)`

In other words, all the different methods that were available have been
simplified, these methods also return the created plugin (or updated
plugin for the `add_functions` method. The `add_function` (singular)
method has a parameter `return_plugin` to control whether you get the
created or updated plugin, instead of the created function.

**An important callout:**

One big internal change that this introduces is that a function that
gets added to a plugin (new or existing) is automatically copied, the
metadata is deep-copied and the plugin_name in the metadata is updated,
so this sequence is valid now:

```python
@kernel_function(name='test')
def f(...):
   etc

func = KernelFunctionFromMethod(method=f)
func2 = kernel.add_function('plugin', func)
assert func != func2
```

Also closes: #5855

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
Removed KernelPluginCollection
Updated KernelPlugin
   added from_... method to load from different sources
Updated KernelFunctionFromPrompt
   added from_yaml and from_directory methods.

Added and updated tests to 99% coverage of KernelPlugin and
KernelFunctionFromPrompt

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
2024-04-15 13:51:59 +00:00

60 lines
1.9 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import (
AzureChatCompletion,
)
from semantic_kernel.core_plugins.math_plugin import MathPlugin
from semantic_kernel.core_plugins.time_plugin import TimePlugin
from semantic_kernel.planners.function_calling_stepwise_planner.function_calling_stepwise_planner import (
FunctionCallingStepwisePlanner,
)
from semantic_kernel.planners.function_calling_stepwise_planner.function_calling_stepwise_planner_options import (
FunctionCallingStepwisePlannerOptions,
)
from semantic_kernel.utils.settings import azure_openai_settings_from_dot_env_as_dict
async def main():
kernel = sk.Kernel()
service_id = "planner"
kernel.add_service(
AzureChatCompletion(
service_id=service_id, **azure_openai_settings_from_dot_env_as_dict(include_api_version=True)
),
)
cur_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "resources")
kernel.import_native_plugin_from_directory(cur_dir, "email_plugin")
kernel.add_plugin(MathPlugin(), "MathPlugin")
kernel.add_plugin(TimePlugin(), "TimePlugin")
questions = [
"What is the current hour number, plus 5?",
"What is 387 minus 22? Email the solution to John and Mary.",
"Write a limerick, translate it to Spanish, and send it to Jane",
]
options = FunctionCallingStepwisePlannerOptions(
max_iterations=10,
max_tokens=4000,
)
planner = FunctionCallingStepwisePlanner(service_id=service_id, options=options)
for question in questions:
result = await planner.invoke(kernel, question)
print(f"Q: {question}\nA: {result.final_answer}\n")
# Uncomment the following line to view the planner's process for completing the request
# print(f"Chat history: {result.chat_history}\n")
if __name__ == "__main__":
asyncio.run(main())