chore: import upstream snapshot with attribution

python/samples/demos/assistants_group_chat/group_chat.py

@@ -0,0 +1,159 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+import re
+
+from semantic_kernel.agents import AgentGroupChat, OpenAIAssistantAgent
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+
+"""
+The following sample demonstrates how to create a Semantic Kernel
+OpenAIAssistantAgent, and leverage the assistant's
+code interpreter or file search capabilities. The user interacts
+with the AI assistant by uploading files and chatting.
+
+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+no longer maintained.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
+"""
+
+
+# region Helper Functions
+
+
+def display_intro_message():
+    print(
+        """
+    Chat with an AI assistant backed by a Semantic Kernel OpenAIAssistantAgent.
+
+    To start: you can upload files to the assistant using the command (brackets included):
+
+    [upload code_interpreter | file_search file_path]
+
+    where `code_interpreter` or `file_search` is the purpose of the file and
+    `file_path` is the path to the file. For example:
+
+    [upload code_interpreter file.txt]
+
+    This will upload file.txt to the assistant for use with the code interpreter tool.
+
+    Type "exit" to exit the chat.
+    """
+    )
+
+
+def parse_upload_command(user_input: str):
+    """Parse the user input for an upload command."""
+    match = re.search(r"\[upload\s+(code_interpreter|file_search)\s+(.+)\]", user_input)
+    if match:
+        return match.group(1), match.group(2)
+    return None, None
+
+
+async def handle_file_upload(assistant_agent: OpenAIAssistantAgent, purpose: str, file_path: str):
+    """Handle the file upload command."""
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    file_id = await assistant_agent.add_file(file_path, purpose="assistants")
+    print(f"File uploaded: {file_id}")
+
+    if purpose == "code_interpreter":
+        await enable_code_interpreter(assistant_agent, file_id)
+    elif purpose == "file_search":
+        await enable_file_search(assistant_agent, file_id)
+
+
+async def enable_code_interpreter(assistant_agent: OpenAIAssistantAgent, file_id: str):
+    """Enable the file for code interpreter."""
+    assistant_agent.code_interpreter_file_ids.append(file_id)
+    tools = [{"type": "file_search"}, {"type": "code_interpreter"}]
+    tool_resources = {"code_interpreter": {"file_ids": assistant_agent.code_interpreter_file_ids}}
+    await assistant_agent.modify_assistant(
+        assistant_id=assistant_agent.assistant.id, tools=tools, tool_resources=tool_resources
+    )
+    print("File enabled for code interpreter.")
+
+
+async def enable_file_search(assistant_agent: OpenAIAssistantAgent, file_id: str):
+    """Enable the file for file search."""
+    if assistant_agent.vector_store_id is not None:
+        await assistant_agent.client.beta.vector_stores.files.create(
+            vector_store_id=assistant_agent.vector_store_id, file_id=file_id
+        )
+        assistant_agent.file_search_file_ids.append(file_id)
+    else:
+        vector_store = await assistant_agent.create_vector_store(file_ids=file_id)
+        assistant_agent.file_search_file_ids.append(file_id)
+        assistant_agent.vector_store_id = vector_store.id
+        tools = [{"type": "file_search"}, {"type": "code_interpreter"}]
+        tool_resources = {"file_search": {"vector_store_ids": [vector_store.id]}}
+        await assistant_agent.modify_assistant(
+            assistant_id=assistant_agent.assistant.id, tools=tools, tool_resources=tool_resources
+        )
+    print("File enabled for file search.")
+
+
+async def cleanup_resources(assistant_agent: OpenAIAssistantAgent):
+    """Cleanup the resources used by the assistant."""
+    if assistant_agent.vector_store_id:
+        await assistant_agent.delete_vector_store(assistant_agent.vector_store_id)
+    for file_id in assistant_agent.code_interpreter_file_ids:
+        await assistant_agent.delete_file(file_id)
+    for file_id in assistant_agent.file_search_file_ids:
+        await assistant_agent.delete_file(file_id)
+    await assistant_agent.delete()
+
+
+# endregion
+
+
+async def main():
+    assistant_agent = None
+    try:
+        display_intro_message()
+
+        # Create the OpenAI Assistant Agent
+        assistant_agent = await OpenAIAssistantAgent.create(
+            service_id="AIAssistant",
+            description="An AI assistant that helps with everyday tasks.",
+            instructions="Help the user with their task.",
+            enable_code_interpreter=True,
+            enable_file_search=True,
+        )
+
+        # Define an agent group chat, which drives the conversation
+        # We add messages to the chat and then invoke the agent to respond.
+        chat = AgentGroupChat()
+
+        while True:
+            try:
+                user_input = input("User:> ")
+            except (KeyboardInterrupt, EOFError):
+                print("\n\nExiting chat...")
+                break
+
+            if user_input.strip().lower() == "exit":
+                print("\n\nExiting chat...")
+                break
+
+            purpose, file_path = parse_upload_command(user_input)
+            if purpose and file_path:
+                await handle_file_upload(assistant_agent, purpose, file_path)
+                continue
+
+            await chat.add_chat_message(message=ChatMessageContent(role=AuthorRole.USER, content=user_input))
+            async for content in chat.invoke(agent=assistant_agent):
+                print(f"Assistant:> # {content.role} - {content.name or '*'}: '{content.content}'")
+    finally:
+        if assistant_agent:
+            await cleanup_resources(assistant_agent)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())