Repository: microsoft/semantic-kernel
File: python/samples/concepts/agents/openai_responses/responses_agent_binary_content_upload.py (Python)

Code before:

        # Create BinaryContent from text file
        text_binary_content = BinaryContent.from_file(file_path=text_file_path, mime_type="text/plain")
        print(f"Created text BinaryContent: {text_binary_content.mime_type}, can_read: {text_binary_content.can_read}")

        # Alternative: Create BinaryContent directly from data
        # You can also create BinaryContent directly from bytes or string data
        alternative_text_content = BinaryContent(
            data=text_content.encode("utf-8"), mime_type="text/plain", data_format="base64"
        )
        print(f"Alternative text BinaryContent: {alternative_text_content.mime_type}")

        # 4. Create a Semantic Kernel agent for the OpenAI Responses API
        agent = OpenAIResponsesAgent(
            ai_model_id=model,
            client=client,
            instructions=(
                "You are a helpful assistant that can analyze uploaded files. "
                "When users upload files, examine their content and provide helpful insights. "
                "You can identify file types, summarize content, and answer questions about the files."
            ),
            name="FileAnalyzer",
        )

        # 5. Create initial chat history with uploaded files
        chat_history = ChatHistory()

        # Add a message with the binary content attachments
        from semantic_kernel.contents.text_content import TextContent

        chat_history.add_user_message([
            TextContent(text="I'm uploading a PDF document and a text file for you to analyze."),
            pdf_binary_content,
            text_binary_content,
        ])

        # 6. Have a conversation with the agent about the uploaded files
        thread = None

        for user_input in USER_INPUTS:
            print(f"\n# User: '{user_input}'")

            # Get the agent's response using the user input
            first_chunk = True
            async for response in agent.invoke_stream(messages=user_input, thread=thread):
                thread = response.thread
                if first_chunk:
                    print(f"# {response.name}: ", end="", flush=True)
                    first_chunk = False
                print(response.content, end="", flush=True)
            print()  # New line after response

Review comment (suggestion):
I'm not sure I understand the use of the `ChatHistory` here. Once the messages are added to it, it's not used for anything else? 

All messages to an agent should be passed in via the `messages` kwarg. They will then be added to the thread.

Code after:
        text_content = create_sample_text_content()
        text_file.write(text_content)
        text_file_path = text_file.name

    try:
        # 3. Create BinaryContent objects from files using different methods
        print("Creating BinaryContent from files...")

        # Method 1: Create BinaryContent from an existing PDF file
        pdf_binary_content = BinaryContent.from_file(file_path=pdf_file_path, mime_type="application/pdf")
        print(f"Created PDF BinaryContent: {pdf_binary_content.mime_type}, can_read: {pdf_binary_content.can_read}")

        # Method 2: Create BinaryContent from the temporary text file
        text_binary_content = BinaryContent.from_file(file_path=text_file_path, mime_type="text/plain")
        print(f"Created text BinaryContent: {text_binary_content.mime_type}, can_read: {text_binary_content.can_read}")

        # Method 3: Create BinaryContent directly from in-memory data
        # This approach allows creating BinaryContent without file I/O operations
        alternative_text_content = BinaryContent(
            data=text_content.encode("utf-8"), mime_type="text/plain", data_format="base64"
        )
        print(f"Alternative text BinaryContent: {alternative_text_content.mime_type}")

        # 4. Initialize the OpenAI Responses Agent with file analysis capabilities
        # Configure the AI model for responses
        settings = OpenAISettings()
        responses_model = settings.responses_model_id or "gpt-4o"

        agent = OpenAIResponsesAgent(
            ai_model_id=responses_model,
            client=client,
            instructions=(
                "You are a helpful assistant that can analyze uploaded files. "
                "When users upload files, examine their content and provide helpful insights. "
                "You can identify file types, summarize content, and answer questions about the files."
            ),
            name="FileAnalyzer",
        )

        # 5. Demonstrate multi-modal message composition
        # This showcases combining text and binary content in a single message

        # Compose a message containing both text instructions and file attachments
        # This pattern is ideal for scenarios requiring rich, mixed-content interactions
        initial_message = ChatMessageContent(
            role=AuthorRole.USER,
            items=[
                TextContent(text="I'm uploading a PDF document and a text file for you to analyze."),
                pdf_binary_content,
                text_binary_content,
            ],