wehub-resource-sync
136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from collections.abc import Awaitable, Callable
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai import FunctionChoiceBehavior
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from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
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from semantic_kernel.connectors.brave import BraveSearch
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from semantic_kernel.contents import ChatHistory
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from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
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from semantic_kernel.functions import KernelArguments, KernelParameterMetadata
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"""
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This project demonstrates how to integrate the Brave Search API as a plugin into the Semantic Kernel
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framework to enable conversational AI capabilities with real-time web information.
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To use Brave Search, you need an API key, which can be obtained by login to
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https://api-dashboard.search.brave.com/ and creating a subscription key.
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After that store it under the name `BRAVE_API_KEY` in a .env file or your environment variables.
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"""
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kernel = Kernel()
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kernel.add_service(OpenAIChatCompletion(service_id="chat"))
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kernel.add_function(
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plugin_name="brave",
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function=BraveSearch().create_search_function(
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function_name="brave_search",
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description="Get details about Semantic Kernel concepts.",
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parameters=[
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KernelParameterMetadata(
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name="query",
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description="The search query.",
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type="str",
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is_required=True,
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type_object=str,
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),
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KernelParameterMetadata(
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name="top",
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description="The number of results to return.",
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type="int",
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is_required=False,
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default_value=2,
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type_object=int,
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),
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KernelParameterMetadata(
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name="skip",
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description="The number of results to skip.",
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type="int",
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is_required=False,
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default_value=0,
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type_object=int,
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),
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],
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),
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)
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chat_function = kernel.add_function(
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prompt="{{$chat_history}}{{$user_input}}",
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plugin_name="ChatBot",
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function_name="Chat",
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)
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execution_settings = OpenAIChatPromptExecutionSettings(
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service_id="chat",
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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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function_choice_behavior=FunctionChoiceBehavior.Auto(auto_invoke=True),
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)
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history = ChatHistory()
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system_message = """
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You are a chat bot, specialized in Semantic Kernel, Microsoft LLM orchestration SDK.
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Assume questions are related to that, and use the Brave search plugin to find answers.
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"""
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history.add_system_message(system_message)
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history.add_user_message("Hi there, who are you?")
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history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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arguments = KernelArguments(settings=execution_settings)
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@kernel.filter(filter_type=FilterTypes.FUNCTION_INVOCATION)
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async def log_brave_filter(
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context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]]
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):
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if context.function.plugin_name == "brave":
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print("Calling Brave search with arguments:")
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if "query" in context.arguments:
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print(f' Query: "{context.arguments["query"]}"')
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if "count" in context.arguments:
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print(f' Count: "{context.arguments["count"]}"')
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if "skip" in context.arguments:
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print(f' Skip: "{context.arguments["skip"]}"')
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await next(context)
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print("Brave search completed.")
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else:
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await next(context)
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async def chat() -> bool:
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try:
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user_input = input("User:> ")
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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if user_input == "exit":
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print("\n\nExiting chat...")
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return False
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arguments["user_input"] = user_input
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arguments["chat_history"] = history
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result = await kernel.invoke(chat_function, arguments=arguments)
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print(f"Mosscap:> {result}")
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history.add_user_message(user_input)
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history.add_assistant_message(str(result))
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return True
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async def main():
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chatting = True
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print(
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"Welcome to the chat bot!\
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\n Type 'exit' to exit.\
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\n Try to find out more about the inner workings of Semantic Kernel."
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)
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while chatting:
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chatting = await chat()
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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