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
@@ -0,0 +1,157 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import os
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
+from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
+from semantic_kernel.contents import StreamingFileReferenceContent
+
+logging.basicConfig(level=logging.ERROR)
+
+"""
+The following sample demonstrates how to create a simple,
+OpenAI assistant agent that utilizes the code interpreter
+to analyze uploaded files.
+
+This is the full code sample for the Semantic Kernel Learn Site: How-To: Open AI Assistant Agent Code Interpreter
+
+https://learn.microsoft.com/semantic-kernel/frameworks/agent/examples/example-assistant-code?pivots=programming-language-python
+"""  # noqa: E501
+
+# Let's form the file paths that we will later pass to the assistant
+csv_file_path_1 = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
+    "resources",
+    "PopulationByAdmin1.csv",
+)
+
+csv_file_path_2 = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
+    "resources",
+    "PopulationByCountry.csv",
+)
+
+
+async def download_file_content(agent: AzureAssistantAgent, file_id: str):
+    try:
+        # Fetch the content of the file using the provided method
+        response_content = await agent.client.files.content(file_id)
+
+        # Get the current working directory of the file
+        current_directory = os.path.dirname(os.path.abspath(__file__))
+
+        # Define the path to save the image in the current directory
+        file_path = os.path.join(
+            current_directory,  # Use the current directory of the file
+            f"{file_id}.png",  # You can modify this to use the actual filename with proper extension
+        )
+
+        # Save content to a file asynchronously
+        with open(file_path, "wb") as file:
+            file.write(response_content.content)
+
+        print(f"File saved to: {file_path}")
+    except Exception as e:
+        print(f"An error occurred while downloading file {file_id}: {str(e)}")
+
+
+async def download_response_image(agent: AzureAssistantAgent, file_ids: list[str]):
+    if file_ids:
+        # Iterate over file_ids and download each one
+        for file_id in file_ids:
+            await download_file_content(agent, file_id)
+
+
+async def main():
+    # Create the client using Azure OpenAI resources and configuration
+    client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
+
+    # Upload the files to the client
+    file_ids: list[str] = []
+    for path in [csv_file_path_1, csv_file_path_2]:
+        with open(path, "rb") as file:
+            file = await client.files.create(file=file, purpose="assistants")
+            file_ids.append(file.id)
+
+    # Get the code interpreter tool and resources
+    code_interpreter_tools, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool(
+        file_ids=file_ids
+    )
+
+    # Create the assistant definition
+    definition = await client.beta.assistants.create(
+        model=AzureOpenAISettings().chat_deployment_name,
+        instructions="""
+            Analyze the available data to provide an answer to the user's question.
+            Always format response using markdown.
+            Always include a numerical index that starts at 1 for any lists or tables.
+            Always sort lists in ascending order.
+            """,
+        name="SampleAssistantAgent",
+        tools=code_interpreter_tools,
+        tool_resources=code_interpreter_tool_resources,
+    )
+
+    # Create the agent using the client and the assistant definition
+    agent = AzureAssistantAgent(
+        client=client,
+        definition=definition,
+    )
+
+    thread: AssistantAgentThread = None
+
+    try:
+        is_complete: bool = False
+        file_ids: list[str] = []
+        while not is_complete:
+            user_input = input("User:> ")
+            if not user_input:
+                continue
+
+            if user_input.lower() == "exit":
+                is_complete = True
+                break
+
+            is_code = False
+            last_role = None
+            async for response in agent.invoke_stream(messages=user_input, thread=thread):
+                current_is_code = response.metadata.get("code", False)
+
+                if current_is_code:
+                    if not is_code:
+                        print("\n\n```python")
+                        is_code = True
+                    print(response.content, end="", flush=True)
+                else:
+                    if is_code:
+                        print("\n```")
+                        is_code = False
+                        last_role = None
+                    if hasattr(response, "role") and response.role is not None and last_role != response.role:
+                        print(f"\n# {response.role}: ", end="", flush=True)
+                        last_role = response.role
+                    print(response.content, end="", flush=True)
+                file_ids.extend([
+                    item.file_id for item in response.items if isinstance(item, StreamingFileReferenceContent)
+                ])
+                thread = response.thread
+            if is_code:
+                print("```\n")
+            print()
+
+            await download_response_image(agent, file_ids)
+            file_ids.clear()
+
+    finally:
+        print("\nCleaning up resources...")
+        [await client.files.delete(file_id) for file_id in file_ids]
+        await thread.delete() if thread else None
+        await client.beta.assistants.delete(agent.id)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())