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python/samples/concepts/search/google_text_search_as_plugin.py
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| # Copyright (c) Microsoft. All rights reserved. | ||
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| from collections.abc import Coroutine | ||
| from typing import Any | ||
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| from semantic_kernel import Kernel | ||
| from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior | ||
| from semantic_kernel.connectors.ai.open_ai import ( | ||
| OpenAIChatCompletion, | ||
| OpenAIChatPromptExecutionSettings, | ||
| ) | ||
| from semantic_kernel.connectors.search.google import GoogleSearch | ||
| from semantic_kernel.contents import ChatHistory | ||
| from semantic_kernel.filters.filter_types import FilterTypes | ||
| from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext | ||
| from semantic_kernel.functions import KernelArguments, KernelParameterMetadata, KernelPlugin | ||
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| # This sample shows how to setup Google Search as a plugin in the Semantic Kernel. | ||
| # With that plugin you can do function calling to augment your chat bot capabilities. | ||
| # The plugin uses the search function of the GoogleSearch instance, | ||
| # which returns only the snippet of the search results. | ||
| # It also shows how the Parameters of the function can be used to pass arguments to the plugin, | ||
| # this is shown with the siteSearch parameter. | ||
| # The LLM can choose to override that but it will take the default value otherwise. | ||
| # You can also set this up with the 'get_search_results', this returns a object with the full results of the search | ||
| # and then you can add a `string_mapper` to the function to return the desired string of information | ||
| # that you want to pass to the LLM. | ||
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| kernel = Kernel() | ||
| kernel.add_service(OpenAIChatCompletion(service_id="chat")) | ||
| kernel.add_plugin( | ||
| KernelPlugin.from_text_search_with_search( | ||
| GoogleSearch(), | ||
| plugin_name="google", | ||
| description="Get details about Semantic Kernel concepts.", | ||
| parameters=[ | ||
| KernelParameterMetadata( | ||
| name="query", | ||
| description="The search query.", | ||
| type="str", | ||
| is_required=True, | ||
| type_object=str, | ||
| ), | ||
| KernelParameterMetadata( | ||
| name="top", | ||
| description="The number of results to return.", | ||
| type="int", | ||
| is_required=False, | ||
| default_value=2, | ||
| type_object=int, | ||
| ), | ||
| KernelParameterMetadata( | ||
| name="skip", | ||
| description="The number of results to skip.", | ||
| type="int", | ||
| is_required=False, | ||
| default_value=0, | ||
| type_object=int, | ||
| ), | ||
| KernelParameterMetadata( | ||
| name="siteSearch", | ||
| description="The site to search.", | ||
| default_value="https://github.com/", | ||
| type="str", | ||
| is_required=False, | ||
| type_object=str, | ||
| ), | ||
| ], | ||
| ) | ||
| ) | ||
| chat_function = kernel.add_function( | ||
| prompt="{{$chat_history}}{{$user_input}}", | ||
| plugin_name="ChatBot", | ||
| function_name="Chat", | ||
| ) | ||
| execution_settings = OpenAIChatPromptExecutionSettings( | ||
| service_id="chat", | ||
| max_tokens=2000, | ||
| temperature=0.7, | ||
| top_p=0.8, | ||
| function_choice_behavior=FunctionChoiceBehavior.Auto(auto_invoke=True), | ||
| ) | ||
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| history = ChatHistory() | ||
| system_message = """ | ||
| You are a chat bot, specialized in Semantic Kernel, Microsoft LLM orchestration SDK. | ||
| Assume questions are related to that, and use the Bing search plugin to find answers. | ||
| """ | ||
| history.add_system_message(system_message) | ||
| history.add_user_message("Hi there, who are you?") | ||
| 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) | ||
| async def log_google_filter(context: FunctionInvocationContext, next: Coroutine[FunctionInvocationContext, Any, None]): | ||
| if context.function.plugin_name == "google": | ||
| print("Calling Google search with arguments:") | ||
| if "query" in context.arguments: | ||
| print(f' Query: "{context.arguments["query"]}"') | ||
| if "top" in context.arguments: | ||
| print(f' Top: "{context.arguments["top"]}"') | ||
| if "skip" in context.arguments: | ||
| print(f' Skip: "{context.arguments["skip"]}"') | ||
| await next(context) | ||
| print("Google search completed.") | ||
| else: | ||
| await next(context) | ||
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| async def chat() -> bool: | ||
| try: | ||
| user_input = input("User:> ") | ||
| except KeyboardInterrupt: | ||
| print("\n\nExiting chat...") | ||
| return False | ||
| except EOFError: | ||
| print("\n\nExiting chat...") | ||
| return False | ||
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| if user_input == "exit": | ||
| print("\n\nExiting chat...") | ||
| return False | ||
| arguments["user_input"] = user_input | ||
| arguments["chat_history"] = history | ||
| result = await kernel.invoke(chat_function, arguments=arguments) | ||
| print(f"Mosscap:> {result}") | ||
| history.add_user_message(user_input) | ||
| history.add_assistant_message(str(result)) | ||
| return True | ||
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| async def main(): | ||
| chatting = True | ||
| print( | ||
| "Welcome to the chat bot!\ | ||
| \n Type 'exit' to exit.\ | ||
| \n Try to find out more about the inner workings of Semantic Kernel." | ||
| ) | ||
| while chatting: | ||
| chatting = await chat() | ||
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| if __name__ == "__main__": | ||
| import asyncio | ||
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| asyncio.run(main()) |
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