chore: import upstream snapshot with attribution

python/samples/concepts/on_your_data/azure_chat_gpt_with_data_api.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.open_ai import (
+    AzureAISearchDataSource,
+    AzureChatCompletion,
+    AzureChatPromptExecutionSettings,
+    ExtraBody,
+)
+from semantic_kernel.connectors.azure_ai_search import AzureAISearchSettings
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
+
+kernel = Kernel()
+logging.basicConfig(level=logging.INFO)
+
+# For example, AI Search index may contain the following document:
+
+# Emily and David, two passionate scientists, met during a research expedition to Antarctica.
+# Bonded by their love for the natural world and shared curiosity, they uncovered a
+# groundbreaking phenomenon in glaciology that could potentially reshape our understanding of climate change.
+
+# Depending on the index that you use, you might need to enable the below
+# and adapt it so that it accurately reflects your index.
+
+# azure_ai_search_settings["fieldsMapping"] = {
+#     "titleField": "source_title",
+#     "urlField": "source_url",
+#     "contentFields": ["source_text"],
+#     "filepathField": "source_file",
+# }
+
+# Create the data source settings
+azure_ai_search_settings = AzureAISearchSettings()
+
+az_source = AzureAISearchDataSource.from_azure_ai_search_settings(azure_ai_search_settings=azure_ai_search_settings)
+extra = ExtraBody(data_sources=[az_source])
+req_settings = AzureChatPromptExecutionSettings(service_id="default", extra_body=extra)
+
+# When using data, use the 2024-02-15-preview API version.
+chat_service = AzureChatCompletion(service_id="chat-gpt", credential=AzureCliCredential())
+kernel.add_service(chat_service)
+
+prompt_template_config = PromptTemplateConfig(
+    template="{{$chat_history}}{{$user_input}}",
+    name="chat",
+    template_format="semantic-kernel",
+    input_variables=[
+        InputVariable(name="chat_history", description="The chat history", is_required=True),
+        InputVariable(name="request", description="The user input", is_required=True),
+    ],
+    execution_settings={"default": req_settings},
+)
+chat_function = kernel.add_function(
+    plugin_name="ChatBot", function_name="Chat", prompt_template_config=prompt_template_config
+)
+
+chat_history = ChatHistory()
+chat_history.add_system_message("I am an AI assistant here to answer your questions.")
+
+
+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 = KernelArguments(chat_history=chat_history, user_input=user_input, execution_settings=req_settings)
+
+    stream = False
+    if stream:
+        # streaming
+        full_message = None
+        print("Assistant:> ", end="")
+        async for message in kernel.invoke_stream(chat_function, arguments=arguments):
+            print(str(message[0]), end="")
+            full_message = message[0] if not full_message else full_message + message[0]
+        print("\n")
+
+        # The tool message containing cited sources is available in the context
+        chat_history.add_user_message(user_input)
+        for message in AzureChatCompletion.split_message(full_message):
+            chat_history.add_message(message)
+        return True
+
+    # Non streaming
+    answer = await kernel.invoke(chat_function, arguments=arguments)
+    print(f"Assistant:> {answer}")
+    chat_history.add_user_message(user_input)
+    for message in AzureChatCompletion.split_message(answer.value[0]):
+        chat_history.add_message(message)
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/on_your_data/azure_chat_gpt_with_data_api_function_calling.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import os
+
+from azure.identity import AzureCliCredential
+
+import semantic_kernel as sk
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import (
+    AzureAISearchDataSource,
+    AzureChatCompletion,
+    AzureChatPromptExecutionSettings,
+    ExtraBody,
+)
+from semantic_kernel.connectors.memory.azure_cognitive_search.azure_ai_search_settings import AzureAISearchSettings
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.core_plugins import TimePlugin
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
+
+logging.basicConfig(level=logging.DEBUG)
+# NOTE:
+# AzureOpenAI function calling requires the following models: gpt-35-turbo (1106) or gpt-4 (1106-preview)
+# along with the API version: 2024-02-15-preview
+# https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/function-calling?tabs=python
+
+kernel = sk.Kernel()
+
+# Create the data source settings
+azure_ai_search_settings = AzureAISearchSettings()
+az_source = AzureAISearchDataSource(parameters=azure_ai_search_settings.model_dump())
+extra = ExtraBody(data_sources=[az_source])
+req_settings = AzureChatPromptExecutionSettings(service_id="chat-gpt", extra_body=extra, tool_choice="auto")
+
+# For example, AI Search index may contain the following document:
+
+# Emily and David, two passionate scientists, met during a research expedition to Antarctica.
+# Bonded by their love for the natural world and shared curiosity, they uncovered a
+# groundbreaking phenomenon in glaciology that could potentially reshape our understanding of climate change.
+
+chat_service = AzureChatCompletion(service_id="chat-gpt", credential=AzureCliCredential())
+kernel.add_service(
+    chat_service,
+)
+
+plugins_directory = os.path.join(__file__, "../../../../../prompt_template_samples/")
+# adding plugins to the kernel
+# the joke plugin in the FunPlugins is a semantic plugin and has the function calling disabled.
+kernel.add_plugin(parent_directory=plugins_directory, plugin_name="FunPlugin")
+# the math plugin is a core plugin and has the function calling enabled.
+kernel.add_plugin(TimePlugin(), plugin_name="time")
+
+# enabling or disabling function calling is done by setting the tool_choice parameter for the completion.
+# when the tool_choice parameter is set to "auto" the model will decide which function to use, if any.
+# if you only want to use a specific tool, set the name of that tool in this parameter,
+# the format for that is 'PluginName-FunctionName', (i.e. 'math-Add').
+# if the model or api version do not support this you will get an error.
+prompt_template_config = PromptTemplateConfig(
+    template="{{$chat_history}}{{$user_input}}",
+    name="chat",
+    template_format="semantic-kernel",
+    input_variables=[
+        InputVariable(name="chat_history", description="The history of the conversation", is_required=True),
+        InputVariable(name="user_input", description="The user input", is_required=True),
+    ],
+)
+
+history = ChatHistory()
+
+history.add_user_message("Hi there, who are you?")
+history.add_assistant_message("I am an AI assistant here to answer your questions.")
+
+chat_function = kernel.add_function(
+    plugin_name="ChatBot", function_name="Chat", prompt_template_config=prompt_template_config
+)
+
+# calling the chat, you could add a overloaded version of the settings here,
+# to enable or disable function calling or set the function calling to a specific plugin.
+# see the openai_function_calling example for how to use this with a unrelated function definition
+req_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(filters={"excluded_plugins": ["ChatBot"]})
+
+arguments = KernelArguments(settings=req_settings)
+
+
+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["chat_history"] = history
+    arguments["user_input"] = user_input
+    answer = await kernel.invoke(
+        function=chat_function,
+        arguments=arguments,
+    )
+    print(f"Mosscap:> {answer}")
+    history.add_user_message(user_input)
+    history.add_assistant_message(str(answer))
+    return True
+
+
+async def main() -> None:
+    print(
+        "Welcome to the chat bot!\
+        \n  Type 'exit' to exit.\
+        \n  Try a time question to see the function calling in action (i.e. what day is it?)."
+    )
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/on_your_data/azure_chat_gpt_with_data_api_vector_search.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.connectors.ai.open_ai import (
+    AzureAISearchDataSource,
+    AzureChatCompletion,
+    AzureChatPromptExecutionSettings,
+    ExtraBody,
+)
+from semantic_kernel.connectors.memory.azure_cognitive_search.azure_ai_search_settings import AzureAISearchSettings
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
+
+kernel = Kernel()
+logging.basicConfig(level=logging.DEBUG)
+
+# For example, AI Search index may contain the following document:
+
+# Emily and David, two passionate scientists, met during a research expedition to Antarctica.
+# Bonded by their love for the natural world and shared curiosity, they uncovered a
+# groundbreaking phenomenon in glaciology that could potentially reshape our understanding of climate change.
+
+azure_ai_search_settings = AzureAISearchSettings()
+azure_ai_search_settings = azure_ai_search_settings.model_dump()
+
+# This example index has fields "title", "chunk", and "vector".
+# Add fields mapping to the settings.
+azure_ai_search_settings["fieldsMapping"] = {
+    "titleField": "title",
+    "contentFields": ["chunk"],
+    "vectorFields": ["vector"],
+}
+
+# Add Ada embedding deployment name to the settings and use vector search.
+azure_ai_search_settings["embeddingDependency"] = {
+    "type": "DeploymentName",
+    "deploymentName": "ada-002",
+}
+azure_ai_search_settings["query_type"] = "vector"
+
+# Create the data source settings
+az_source = AzureAISearchDataSource(parameters=azure_ai_search_settings)
+extra = ExtraBody(data_sources=[az_source])
+service_id = "chat-gpt"
+req_settings = AzureChatPromptExecutionSettings(service_id=service_id, extra_body=extra)
+
+# When using data, use the 2024-02-15-preview API version.
+chat_service = AzureChatCompletion(
+    service_id="chat-gpt", api_version="2024-02-15-preview", credential=AzureCliCredential()
+)
+kernel.add_service(chat_service)
+
+prompt_template_config = PromptTemplateConfig(
+    template="{{$chat_history}}{{$user_input}}",
+    name="chat",
+    template_format="semantic-kernel",
+    input_variables=[
+        InputVariable(name="chat_history", description="The history of the conversation", is_required=True, default=""),
+        InputVariable(name="request", description="The user input", is_required=True),
+    ],
+    execution_settings=req_settings,
+)
+
+chat_history = ChatHistory()
+
+chat_history.add_user_message("Hi there, who are you?")
+chat_history.add_assistant_message("I am an AI assistant here to answer your questions.")
+
+chat_function = kernel.add_function(
+    plugin_name="ChatBot", function_name="Chat", prompt_template_config=prompt_template_config
+)
+
+
+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
+
+    # Non streaming
+    # answer = await kernel.invoke(chat_function, input_vars=context_vars)
+    # print(f"Assistant:> {answer}")
+    arguments = KernelArguments(chat_history=chat_history, user_input=user_input, execution_settings=req_settings)
+
+    full_message = None
+    print("Assistant:> ", end="")
+    async for message in kernel.invoke_stream(chat_function, arguments=arguments):
+        print(str(message[0]), end="")
+        full_message = message[0] if not full_message else full_message + message[0]
+    chat_history.add_assistant_message(str(full_message))
+    print("\n")
+
+    # The tool message containing cited sources is available in the context
+    if full_message:
+        chat_history.add_user_message(user_input)
+        for message in AzureChatCompletion.split_message(full_message):
+            chat_history.add_message(message)
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())