chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from azure.core.credentials import TokenCredential
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.agents import AgentGroupChat, AzureAssistantAgent, ChatCompletionAgent
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureOpenAISettings
+from semantic_kernel.contents import AnnotationContent, AuthorRole
+from semantic_kernel.kernel import Kernel
+
+"""
+The following sample demonstrates how to create an OpenAI
+assistant using either Azure OpenAI or OpenAI, a chat completion
+agent and have them participate in a group chat working on
+an uploaded file.
+
+Note: This sample use the `AgentGroupChat` feature of Semantic Kernel, which is
+no longer maintained. For a replacement, consider using the `GroupChatOrchestration`.
+
+Read more about the `GroupChatOrchestration` here:
+https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/group-chat?pivots=programming-language-python
+
+Here is a migration guide from `AgentGroupChat` to `GroupChatOrchestration`:
+https://learn.microsoft.com/semantic-kernel/support/migration/group-chat-orchestration-migration-guide?pivots=programming-language-python
+"""
+
+
+def _create_kernel_with_chat_completion(service_id: str, credential: TokenCredential) -> Kernel:
+    kernel = Kernel()
+    kernel.add_service(AzureChatCompletion(service_id=service_id, credential=credential))
+    return kernel
+
+
+async def main():
+    credential = AzureCliCredential()
+    file_path = os.path.join(
+        os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))),
+        "resources",
+        "mixed_chat_files",
+        "user-context.txt",
+    )
+
+    # Create the client using Azure OpenAI resources and configuration
+    client = AzureAssistantAgent.create_client(credential=credential)
+
+    # If desired, create using OpenAI resources
+    # client = OpenAIAssistantAgent.create_client()
+
+    # Load the text file as a FileObject
+    with open(file_path, "rb") as file:
+        file = await client.files.create(file=file, purpose="assistants")
+
+    code_interpreter_tool, code_interpreter_tool_resource = AzureAssistantAgent.configure_code_interpreter_tool(
+        file_ids=file.id
+    )
+
+    definition = await client.beta.assistants.create(
+        model=AzureOpenAISettings().chat_deployment_name,
+        instructions="Create charts as requested without explanation.",
+        name="ChartMaker",
+        tools=code_interpreter_tool,
+        tool_resources=code_interpreter_tool_resource,
+    )
+
+    # Create the AzureAssistantAgent instance using the client and the assistant definition
+    analyst_agent = AzureAssistantAgent(client=client, definition=definition)
+
+    service_id = "summary"
+    summary_agent = ChatCompletionAgent(
+        kernel=_create_kernel_with_chat_completion(service_id=service_id, credential=credential),
+        instructions="Summarize the entire conversation for the user in natural language.",
+        name="SummaryAgent",
+    )
+
+    # Create the AgentGroupChat object, which will manage the chat between the agents
+    # We don't always need to specify the agents in the chat up front
+    # As shown below, calling `chat.invoke(agent=<agent>)` will automatically add the
+    # agent to the chat
+    chat = AgentGroupChat()
+
+    try:
+        user_and_agent_inputs = (
+            (
+                "Create a tab delimited file report of the ordered (descending) frequency distribution of "
+                "words in the file 'user-context.txt' for any words used more than once.",
+                analyst_agent,
+            ),
+            (None, summary_agent),
+        )
+
+        for input, agent in user_and_agent_inputs:
+            if input:
+                await chat.add_chat_message(input)
+                print(f"# {AuthorRole.USER}: '{input}'")
+
+            async for content in chat.invoke(agent=agent):
+                print(f"# {content.role} - {content.name or '*'}: '{content.content}'")
+                if len(content.items) > 0:
+                    for item in content.items:
+                        if (
+                            isinstance(agent, AzureAssistantAgent)
+                            and isinstance(item, AnnotationContent)
+                            and item.file_id
+                        ):
+                            print(f"\n`{item.quote}` => {item.file_id}")
+                            response_content = await agent.client.files.content(item.file_id)
+                            print(response_content.text)
+    finally:
+        await client.files.delete(file_id=file.id)
+        await client.beta.assistants.delete(analyst_agent.id)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())