chore: import upstream snapshot with attribution

python/samples/concepts/search/brave_text_search_as_plugin.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
+from semantic_kernel.connectors.brave import BraveSearch
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
+from semantic_kernel.functions import KernelArguments, KernelParameterMetadata
+
+"""
+This project demonstrates how to integrate the Brave Search API as a plugin into the Semantic Kernel 
+framework to enable conversational AI capabilities with real-time web information. 
+
+To use Brave Search, you need an API key, which can be obtained by login to
+https://api-dashboard.search.brave.com/ and creating a subscription key.
+After that store it under the name `BRAVE_API_KEY` in a .env file or your environment variables.
+"""
+
+kernel = Kernel()
+kernel.add_service(OpenAIChatCompletion(service_id="chat"))
+kernel.add_function(
+    plugin_name="brave",
+    function=BraveSearch().create_search_function(
+        function_name="brave_search",
+        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,
+            ),
+        ],
+    ),
+)
+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),
+)
+
+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 Brave 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.")
+
+arguments = KernelArguments(settings=execution_settings)
+
+
+@kernel.filter(filter_type=FilterTypes.FUNCTION_INVOCATION)
+async def log_brave_filter(
+    context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]]
+):
+    if context.function.plugin_name == "brave":
+        print("Calling Brave search with arguments:")
+        if "query" in context.arguments:
+            print(f'  Query: "{context.arguments["query"]}"')
+        if "count" in context.arguments:
+            print(f'  Count: "{context.arguments["count"]}"')
+        if "skip" in context.arguments:
+            print(f'  Skip: "{context.arguments["skip"]}"')
+        await next(context)
+        print("Brave search completed.")
+    else:
+        await next(context)
+
+
+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
+
+    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
+
+
+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()
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

python/samples/concepts/search/google_text_search_as_plugin.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
+from semantic_kernel.connectors.google_search import GoogleSearch
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
+from semantic_kernel.functions import KernelParameterMetadata
+
+"""
+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.
+"""
+
+kernel = Kernel()
+service = OpenAIChatCompletion()
+kernel.add_service(service)
+kernel.add_function(
+    plugin_name="google",
+    function=GoogleSearch().create_search_function(
+        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",
+)
+settings = OpenAIChatPromptExecutionSettings(
+    service_id="chat",
+    max_tokens=2000,
+    temperature=0.7,
+    top_p=0.8,
+    function_choice_behavior=FunctionChoiceBehavior.Auto(),
+)
+
+system_message = """
+You are a chat bot, specialized in Semantic Kernel, Microsoft LLM orchestration SDK.
+Assume questions are related to that, and use the Google search plugin to find answers.
+"""
+history = ChatHistory(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.")
+
+
+@kernel.filter(filter_type=FilterTypes.FUNCTION_INVOCATION)
+async def log_google_filter(
+    context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[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 "siteSearch" in context.arguments:
+            print(f'  siteSearch: "{context.arguments["siteSearch"]}"')
+        await next(context)
+        print("Google search completed.")
+    else:
+        await next(context)
+
+
+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
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+
+    history.add_user_message(user_input)
+    result = await service.get_chat_message_content(history, settings, kernel=kernel)
+    if result:
+        print(f"Mosscap:> {result}")
+        history.add_message(result)
+    return True
+
+
+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()
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())