mirror of
https://github.com/microsoft/semantic-kernel.git
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Eduard van Valkenburg
11cf8eacd4
### 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. --> As we are preparing to move towards v1 where the context no longer is used, we need a better way of handling the responses from models and capture them in a more structured way, also in preparation of multi-modal models and the like. Closes #4598 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Added KernelContent and StreamingKernelContent base classs, consistent with the dotnet versions of those. Subclasses of this class for specific services, for instance, the OpenAI class has parsing for the content, function_calls, tool_calls in it, and the Azure OpenAI one has parsing for the context in it. ### 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 😄
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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import semantic_kernel as sk
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import semantic_kernel.connectors.ai.open_ai as sk_oai
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from semantic_kernel.connectors.ai.open_ai.models.chat.open_ai_chat_message import (
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OpenAIChatMessage,
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)
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from semantic_kernel.core_plugins import MathPlugin
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system_message = """
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You are a chat bot. Your name is Mosscap and
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you have one goal: figure out what people need.
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Your full name, should you need to know it, is
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Splendid Speckled Mosscap. You communicate
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effectively, but you tend to answer with long
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flowery prose. You are also a math wizard,
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especially for adding and subtracting.
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You also excel at joke telling, where your tone is often sarcastic.
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Once you have the answer I am looking for,
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you will return a full answer to me as soon as possible.
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"""
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kernel = sk.Kernel()
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api_key, org_id = sk.openai_settings_from_dot_env()
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kernel.add_chat_service(
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"gpt-3.5-turbo",
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sk_oai.OpenAIChatCompletion(
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ai_model_id="gpt-3.5-turbo-1106",
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api_key=api_key,
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),
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)
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plugins_directory = os.path.join(__file__, "../../../../samples/plugins")
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# adding plugins to the kernel
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# the joke plugin in the FunPlugins is a semantic plugin and has the function calling disabled.
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kernel.import_semantic_plugin_from_directory(plugins_directory, "FunPlugin")
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# the math plugin is a core plugin and has the function calling enabled.
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kernel.import_plugin(MathPlugin(), plugin_name="math")
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# enabling or disabling function calling is done by setting the function_call parameter for the completion.
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# when the function_call parameter is set to "auto" the model will decide which function to use, if any.
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# if you only want to use a specific function, set the name of that function in this parameter,
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# the format for that is 'PluginName-FunctionName', (i.e. 'math-Add').
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# if the model or api version do not support this you will get an error.
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tools = [
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{
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"type": "function",
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"function": {
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"name": "search_hotels",
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"description": "Retrieves hotels from the search index based on the parameters provided",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The location of the hotel (i.e. Seattle, WA)",
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},
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"max_price": {
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"type": "number",
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"description": "The maximum price for the hotel",
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},
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"features": {
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"type": "string",
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"description": "A comma separated list of features (i.e. beachfront, free wifi, etc.)",
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},
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},
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"required": ["location"],
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},
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},
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}
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]
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prompt_config = sk.PromptTemplateConfig.from_execution_settings(
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max_tokens=2000,
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temperature=0.7,
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top_p=0.8,
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tool_choice="auto",
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tools=tools,
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)
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prompt_template = sk.ChatPromptTemplate[OpenAIChatMessage](
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"{{$user_input}}", kernel.prompt_template_engine, prompt_config
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)
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prompt_template.add_system_message(system_message)
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prompt_template.add_user_message("Hi there, who are you?")
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prompt_template.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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function_config = sk.SemanticFunctionConfig(prompt_config, prompt_template)
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chat_function = kernel.register_semantic_function("ChatBot", "Chat", function_config)
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# define the functions available
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async def main() -> None:
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context = kernel.create_new_context()
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context.variables["user_input"] = "I want to find a hotel in Seattle with free wifi and a pool."
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messages = []
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tool_call = None
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response = chat_function.invoke_stream_async(context=context)
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async for message in response:
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current = message[0]
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messages.append(current)
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if current.tool_calls:
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if tool_call is None:
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tool_call = current.tool_calls[0]
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else:
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tool_call += current.tool_calls[0]
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if tool_call:
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print(f"Function to be called: {tool_call.function.name}")
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print(f"Function parameters: \n{tool_call.function.parse_arguments()}")
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return
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print("No function was called")
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print(f"Output was: {str(context)}")
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if __name__ == "__main__":
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asyncio.run(main())
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