Bagikan melalui

Cara Melakukan Streaming Tanggapan Agen

Apa itu Respons Streaming?

Respons yang dialirkan menyampaikan konten pesan secara bertahap dalam potongan kecil. Pendekatan ini meningkatkan pengalaman pengguna dengan memungkinkan mereka melihat dan berinteraksi dengan pesan saat pesan itu terungkap, daripada menunggu seluruh respons dimuat. Pengguna dapat segera mulai memproses informasi, meningkatkan rasa responsivitas dan interaktivitas. Akibatnya, ini meminimalkan penundaan dan membuat pengguna lebih terlibat sepanjang proses komunikasi.

Referensi Streaming

Streaming pada Kernel Semantik

Layanan AI yang mendukung streaming di Kernel Semantik menggunakan jenis konten yang berbeda dibandingkan dengan yang digunakan untuk pesan yang sepenuhnya terbentuk. Jenis konten ini dirancang khusus untuk menangani sifat inkremental data streaming. Jenis konten yang sama juga digunakan dalam Kerangka Kerja Agen untuk tujuan serupa. Ini memastikan konsistensi dan efisiensi di kedua sistem saat berhadapan dengan informasi streaming.

Petunjuk / Saran

Referensi API:

Petunjuk / Saran

Referensi API:

Fitur saat ini tidak tersedia di Java.

Respons yang dialirkan dari ChatCompletionAgent

Saat memanggil respons yang dialirkan dari ChatCompletionAgent, ChatHistory diperbarui di AgentThread setelah respons penuh diterima. Meskipun respons dialirkan secara bertahap, riwayat hanya mencatat pesan lengkap. Ini memastikan bahwa ChatHistory mencerminkan respons yang sepenuhnya terbentuk untuk konsistensi.

// Define agent
ChatCompletionAgent agent = ...;

ChatHistoryAgentThread agentThread = new();

// Create a user message
var message = ChatMessageContent(AuthorRole.User, "<user input>");

// Generate the streamed agent response(s)
await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
{
  // Process streamed response(s)...
}

// It's also possible to read the messages that were added to the ChatHistoryAgentThread.
await foreach (ChatMessageContent response in agentThread.GetMessagesAsync())
{
  // Process messages...
}

from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread

# Define agent
agent = ChatCompletionAgent(...)

# Create a thread object to maintain the conversation state.
# If no thread is provided one will be created and returned with
# the initial response.
thread: ChatHistoryAgentThread = None

# Generate the streamed agent response(s)
async for response in agent.invoke_stream(messages="user input", thread=thread)
{
  # Process streamed response(s)...
  thread = response.thread
}

Fitur saat ini tidak tersedia di Java.

Respons yang dialirkan dari OpenAIAssistantAgent

Saat memanggil respons yang dialirkan dari OpenAIAssistantAgent, asisten mempertahankan kondisi percakapan sebagai thread jarak jauh. Anda dapat membaca pesan dari utas jarak jauh jika diinginkan.

// Define agent
OpenAIAssistantAgent agent = ...;

// Create a thread for the agent conversation.
OpenAIAssistantAgentThread agentThread = new(assistantClient);

// Create a user message
var message = new ChatMessageContent(AuthorRole.User, "<user input>");

// Generate the streamed agent response(s)
await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
{
  // Process streamed response(s)...
}

// It's possible to read the messages from the remote thread.
await foreach (ChatMessageContent response in agentThread.GetMessagesAsync())
{
  // Process messages...
}

// Delete the thread when it is no longer needed
await agentThread.DeleteAsync();

Untuk membuat utas menggunakan Id yang sudah ada, operkan ke konstruktor OpenAIAssistantAgentThread.

// Define agent
OpenAIAssistantAgent agent = ...;

// Create a thread for the agent conversation.
OpenAIAssistantAgentThread agentThread = new(assistantClient, "your-existing-thread-id");

// Create a user message
var message = new ChatMessageContent(AuthorRole.User, "<user input>");

// Generate the streamed agent response(s)
await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
{
  // Process streamed response(s)...
}

// It's possible to read the messages from the remote thread.
await foreach (ChatMessageContent response in agentThread.GetMessagesAsync())
{
  // Process messages...
}

// Delete the thread when it is no longer needed
await agentThread.DeleteAsync();

from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent, OpenAIAssistantAgent

# Define agent
agent = OpenAIAssistantAgent(...)  # or = AzureAssistantAgent(...)

# Create a thread for the agent conversation.
# If no thread is provided one will be created and returned with
# the initial response.
thread: AssistantAgentThread = None

# Generate the streamed agent response(s)
async for response in agent.invoke_stream(messages="user input", thread=thread):
  # Process streamed response(s)...
  thread = response.thread

# Read the messages from the remote thread
async for response in thread.get_messages():
  # Process messages

# Delete the thread
await thread.delete()

Untuk membuat utas menggunakan thread_id yang sudah ada, operkan ke konstruktor AssistantAgentThread.

from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent, OpenAIAssistantAgent

# Define agent
agent = OpenAIAssistantAgent(...)  # or = AzureAssistantAgent(...)

# Create a thread for the agent conversation.
# If no thread is provided one will be created and returned with
# the initial response.
thread = AssistantAgentThread(client=client, thread_id="your-existing-thread-id")

# Generate the streamed agent response(s)
async for response in agent.invoke_stream(messages="user input", thread=thread):
  # Process streamed response(s)...
  thread = response.thread

# Delete the thread
await thread.delete()

Fitur saat ini tidak tersedia di Java.

Menangani Pesan Perantara dengan Respons Streaming

Sifat respons streaming memungkinkan model LLM mengembalikan potongan teks bertahap, memungkinkan penyajian yang lebih cepat di UI atau konsol tanpa menunggu seluruh respons selesai. Selain itu, pemanggil mungkin ingin menangani konten yang bersifat perantara, seperti hasil dari pemanggilan fungsi. Ini dapat dicapai dengan menyediakan fungsi panggilan balik saat memanggil respons streaming. Fungsi panggilan balik menerima pesan lengkap yang dienkapsulasi dalam ChatMessageContent.

Dokumentasi callback untuk AzureAIAgent akan segera tersedia.

Mengonfigurasi on_intermediate_message panggilan balik dalam agent.invoke_stream(...) memungkinkan pemanggil untuk menerima pesan perantara yang dihasilkan selama proses merumuskan respons akhir agen.

import asyncio
from typing import Annotated

from semantic_kernel.agents import AzureResponsesAgent
from semantic_kernel.contents import ChatMessageContent, FunctionCallContent, FunctionResultContent
from semantic_kernel.functions import kernel_function


# Define a sample plugin for the sample
class MenuPlugin:
    """A sample Menu Plugin used for the concept sample."""

    @kernel_function(description="Provides a list of specials from the menu.")
    def get_specials(self, menu_item: str) -> Annotated[str, "Returns the specials from the menu."]:
        return """
        Special Soup: Clam Chowder
        Special Salad: Cobb Salad
        Special Drink: Chai Tea
        """

    @kernel_function(description="Provides the price of the requested menu item.")
    def get_item_price(
        self, menu_item: Annotated[str, "The name of the menu item."]
    ) -> Annotated[str, "Returns the price of the menu item."]:
        return "$9.99"

# This callback function will be called for each intermediate message,
# which will allow one to handle FunctionCallContent and FunctionResultContent.
# If the callback is not provided, the agent will return the final response
# with no intermediate tool call steps.
async def handle_streaming_intermediate_steps(message: ChatMessageContent) -> None:
    for item in message.items or []:
        if isinstance(item, FunctionResultContent):
            print(f"Function Result:> {item.result} for function: {item.name}")
        elif isinstance(item, FunctionCallContent):
            print(f"Function Call:> {item.name} with arguments: {item.arguments}")
        else:
            print(f"{item}")

# Simulate a conversation with the agent
USER_INPUTS = [
    "Hello",
    "What is the special soup?",
    "What is the special drink?",
    "How much is it?",
    "Thank you",
]


async def main():
    # 1. Create the client using OpenAI resources and configuration
    client, model = AzureResponsesAgent.setup_resources()

    # 2. Create a Semantic Kernel agent for the OpenAI Responses API
    agent = AzureResponsesAgent(
        ai_model_id=model,
        client=client,
        instructions="Answer questions about the menu.",
        name="Host",
        plugins=[MenuPlugin()],
    )

    # 3. Create a thread for the agent
    # If no thread is provided, a new thread will be
    # created and returned with the initial response
    thread = None

    try:
        for user_input in user_inputs:
            print(f"# {AuthorRole.USER}: '{user_input}'")

            first_chunk = True
            async for response in agent.invoke_stream(
                messages=user_input,
                thread=thread,
                on_intermediate_message=handle_streaming_intermediate_steps,
            ):
                thread = response.thread
                if first_chunk:
                    print(f"# {response.name}: ", end="", flush=True)
                    first_chunk = False
                print(response.content, end="", flush=True)
            print()
    finally:
        await thread.delete() if thread else None

if __name__ == "__main__":
    asyncio.run(main())

Berikut ini menunjukkan sampel output dari proses pemanggilan agen:

Sample Output:

# AuthorRole.USER: 'Hello'
# Host: Hello! How can I assist you with the menu today?
# AuthorRole.USER: 'What is the special soup?'
Function Call:> MenuPlugin-get_specials with arguments: {}
Function Result:>
        Special Soup: Clam Chowder
        Special Salad: Cobb Salad
        Special Drink: Chai Tea
        for function: MenuPlugin-get_specials
# Host: The special soup today is Clam Chowder. Would you like to know more about it or hear about other specials?
# AuthorRole.USER: 'What is the special drink?'
# Host: The special drink today is Chai Tea. Would you like more details or are you interested in ordering it?
# AuthorRole.USER: 'How much is that?'
Function Call:> MenuPlugin-get_item_price with arguments: {"menu_item":"Chai Tea"}
Function Result:> $9.99 for function: MenuPlugin-get_item_price
# Host: The special drink, Chai Tea, is $9.99. Would you like to order one or need information on something else?
# AuthorRole.USER: 'Thank you'
# Host: You're welcome! If you have any more questions or need help with the menu, just let me know. Enjoy your day!

Fitur saat ini tidak tersedia di Java.

Langkah Selanjutnya

Menggunakan templat dengan agen

Pengelolaan Agen

  • Last updated on