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### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Major work to replace the context with Kernel Arguments on the front-end and Function Results on the back. Updated the function decorator to a new approach, in line with dotnet. Revamps the way function are called, allowing native functions to be completely ignorant of SK, other then the decorator. This also moves things into the new folder structure in sync with dotnet. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Adds: - KernelArguments, a dict-like class that replaces the variables in KernelContext. Closes #4565 - FunctionResult, a class to hold the results of function executions, includes the function, the value and metadata, as well as two convenient function to get the value out of it (str and get_inner_content) the first is generic, the second specifically for KernelContent responses (from AI services). - AI Service Selector class, has a default, which is based on the order in the arguments followed by the order in the functions, can be overridden to implement your own strategy. Closes #4631 - Introduces ChatHistory and refactors the PromptTemplateConfig. Closes #4856, #4630 - Improves parsing of templates, will now all validate during creation and throw an error then, instead of some that do not check for validaty until used. - Introduces named_args block and thereby the ability to have multiple arguments for a function call in a template. Closes #5003 Updates: - kernel_function decorators, the parameter decorator was removed and instead we now use Annotated to add a description of a field and we get the type and required from the function definition. - core plugins, use the new approach to kernel_function decorators. - planners, template engines have all been updated to use the kernel and kernelarguments instead of Context. - Events have been updated, now use kernelarguments and function_result - Tokenizers support for named_args and improvements on parsing and checking. - Kernel examples and notebooks to use the latest code. - All unit and integration tests. There is more code coverage now than before. Removed: - kernelContext - kernel_function_parameter_decorator - delegate handling code for native functions - file_io_plugin and tests - SemanticFunctionConfig - ChatPromptTemplate ### 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 😄 --------- Co-authored-by: Evan Mattson <[email protected]> Co-authored-by: Evan Mattson <[email protected]> Co-authored-by: Evan Mattson <[email protected]>
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import os
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from dotenv import load_dotenv
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import semantic_kernel as sk
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from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
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from semantic_kernel.connectors.search_engine import BingConnector
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from semantic_kernel.core_plugins import WebSearchEnginePlugin
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load_dotenv()
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async def main():
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kernel = sk.Kernel()
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deployment, key, endpoint, api_version = sk.azure_openai_settings_from_dot_env(include_api_version=True)
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service_id = "chat-gpt"
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kernel.add_service(
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AzureChatCompletion(
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service_id=service_id,
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deployment_name=deployment,
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api_key=key,
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endpoint=endpoint,
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api_version=api_version,
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),
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)
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connector = BingConnector(api_key=os.getenv("BING_API_KEY"))
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web_plugin = kernel.import_plugin(WebSearchEnginePlugin(connector), "WebSearch")
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print("---------------- Question 1 -----------------\n")
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query = "Which country receives the most rain per year?"
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search = web_plugin["search"]
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result = await kernel.invoke(search, query=query)
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print(f"Question: {query}\n")
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print(f"Answer: {result}\n")
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print("---------------- Question 2 -----------------\n")
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prompt = """
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Answer the question using only the data that is provided in the data section.
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Do not use any prior knowledge to answer the question.
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Data: {{WebSearch.search "What is semantic kernel?"}}
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Question: {{$question}}?
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Answer:
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"""
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req_settings = kernel.get_service("chat-gpt").get_prompt_execution_settings_class()(service_id=service_id)
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req_settings.temperature = 0.2
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prompt_template_config = sk.PromptTemplateConfig(
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template=prompt,
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name="qna",
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template_format="semantic-kernel",
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execution_settings=req_settings,
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)
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question = "What is Semantic Kernel?"
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qna = kernel.create_function_from_prompt(prompt_template_config=prompt_template_config)
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result = await qna.invoke(kernel, question=question, num_results=10, offset=0)
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print(f"Question: {question}\n")
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print(f"Answer: {result}\n")
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"""
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Output:
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Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI,
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Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python.
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By doing so, you can create AI apps that combine the best of both worlds.
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Semantic Kernel is at the center of the copilot stack.
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"""
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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