chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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## OpenAI Assistant Agents
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The following getting started samples show how to use OpenAI Assistant agents with Semantic Kernel.
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## Assistants API Overview
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The Assistants API is a robust solution from OpenAI that empowers developers to integrate powerful, purpose-built AI assistants into their applications. It streamlines the development process by handling conversation histories, managing threads, and providing seamless access to advanced tools.
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### Key Features
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- **Purpose-Built AI Assistants:**
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Assistants are specialized AIs that leverage OpenAI’s models to interact with users, access files, maintain persistent threads, and call additional tools. This enables highly tailored and effective user interactions.
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- **Simplified Conversation Management:**
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The concept of a **thread** -- a dedicated conversation session between an assistant and a user -- ensures that message history is managed automatically. Threads optimize the conversation context by storing and truncating messages as needed.
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- **Integrated Tool Access:**
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The API provides built-in tools such as:
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- **Code Interpreter:** Allows the assistant to execute code, enhancing its ability to solve complex tasks.
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- **File Search:** Implements best practices for retrieving data from uploaded files, including advanced chunking and embedding techniques.
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- **Enhanced Function Calling:**
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With improved support for third-party tool integration, the Assistants API enables assistants to extend their capabilities beyond native functions.
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For more detailed technical information, refer to the [Assistants API](https://platform.openai.com/docs/assistants/overview).
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### Semantic Kernel OpenAI Assistant Agents
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OpenAI Assistant Agents are created in the following way:
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```python
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from semantic_kernel.agents import OpenAIAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import OpenAISettings
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# Create the client using OpenAI resources and configuration
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client = OpenAIAssistantAgent.create_client()
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# Create the assistant definition
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definition = await client.beta.assistants.create(
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model=OpenAISettings().chat_model_id,
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instructions="<instructions>",
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name="<name>",
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)
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# Define the Semantic Kernel OpenAI Assistant Agent
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agent = OpenAIAssistantAgent(
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client=client,
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definition=definition,
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)
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# Define a thread to hold the conversation's context
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# If a thread is not created initially it will be created
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# and returned as part of the first response
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thread = None
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# Get the agent response
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response = await agent.get_response(messages="Why is the sky blue?", thread=thread)
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thread = response.thread
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# or use the agent.invoke(...) method
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async for response in agent.invoke(messages="Why is the sky blue?", thread=thread):
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print(f"# {response.role}: {response.content}")
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thread = response.thread
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```
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### Semantic Kernel Azure Assistant Agents
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Azure Assistant Agents are currently in preview and require a `-preview` API version (minimum version: `2024-05-01-preview`). As new features are introduced, API versions will be updated accordingly. For the latest versioning details, please refer to the [Azure OpenAI API preview lifecycle](https://learn.microsoft.com/azure/ai-services/openai/api-version-deprecation).
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To specify the correct API version, set the following environment variable (for example, in your `.env` file):
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```bash
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AZURE_OPENAI_API_VERSION="2025-01-01-preview"
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```
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Alternatively, you can pass the `api_version` parameter when creating an `AzureAssistantAgent`:
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```python
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from semantic_kernel.agents import AzureAssistantAgent
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# Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client()
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# Create the assistant definition
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definition = await client.beta.assistants.create(
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model=model,
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instructions="<instructions>",
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name="<name>",
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)
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# Define the Semantic Kernel Azure OpenAI Assistant Agent
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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)
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# Define a thread to hold the conversation's context
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# If a thread is not created initially it will be created
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# and returned as part of the first response
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thread = None
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# Get the agent response
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response = await agent.get_response(messages="Why is the sky blue?", thread=thread)
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thread = response.thread
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# or use the agent.invoke(...) method
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async for response in agent.invoke(messages="Why is the sky blue?", thread=thread):
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print(f"# {response.role}: {response.content}")
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thread = response.thread
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```
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
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"""
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The following sample demonstrates how to create an OpenAI assistant using either
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Azure OpenAI or OpenAI. The sample shows how to have the assistant answrer
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questions about the world.
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The interaction with the agent is via the `get_response` method, which sends a
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user input to the agent and receives a response from the agent. The conversation
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history is maintained by the agent service, i.e. the responses are automatically
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associated with the thread. Therefore, client code does not need to maintain the
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conversation history.
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"""
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# Simulate a conversation with the agent
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USER_INPUTS = [
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"Why is the sky blue?",
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"What is the speed of light?",
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"What have we been talking about?",
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]
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async def main():
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# 1. Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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# 2. Create the assistant on the Azure OpenAI service
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definition = await client.beta.assistants.create(
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model=AzureOpenAISettings().chat_deployment_name,
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instructions="Answer questions about the world in one sentence.",
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name="Assistant",
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)
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# 3. Create a Semantic Kernel agent for the Azure OpenAI assistant
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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)
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# 4. Create a new thread for use with the assistant
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# If no thread is provided, a new thread will be
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# created and returned with the initial response
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thread: AssistantAgentThread = None
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try:
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for user_input in USER_INPUTS:
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print(f"# User: '{user_input}'")
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# 6. Invoke the agent for the current thread and print the response
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response = await agent.get_response(messages=user_input, thread=thread)
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print(f"# {response.name}: {response}")
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thread = response.thread
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finally:
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# 7. Clean up the resources
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await thread.delete() if thread else None
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await agent.client.beta.assistants.delete(assistant_id=agent.id)
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"""
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You should see output similar to the following:
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# User: 'Why is the sky blue?'
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# Agent: The sky appears blue because molecules in the atmosphere scatter sunlight in all directions, and blue
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light is scattered more than other colors because it travels in shorter, smaller waves.
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# User: 'What is the speed of light?'
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# Agent: The speed of light in a vacuum is approximately 299,792,458 meters per second
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(about 186,282 miles per second).
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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+100
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from typing import Annotated
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
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from semantic_kernel.functions import kernel_function
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"""
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The following sample demonstrates how to create an OpenAI
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assistant using either Azure OpenAI or OpenAI. The sample
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shows how to use a Semantic Kernel plugin as part of the
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OpenAI Assistant.
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"""
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# Define a sample plugin for the sample
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class MenuPlugin:
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"""A sample Menu Plugin used for the concept sample."""
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@kernel_function(description="Provides a list of specials from the menu.")
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def get_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
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return """
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Special Soup: Clam Chowder
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Special Salad: Cobb Salad
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Special Drink: Chai Tea
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"""
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@kernel_function(description="Provides the price of the requested menu item.")
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def get_item_price(
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self, menu_item: Annotated[str, "The name of the menu item."]
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) -> Annotated[str, "Returns the price of the menu item."]:
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return "$9.99"
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# Simulate a conversation with the agent
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USER_INPUTS = [
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"Hello",
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"What is the special soup?",
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"What is the special drink?",
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"How much is it?",
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"Thank you",
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]
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async def main():
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# 1. Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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# 2. Create the assistant on the Azure OpenAI service
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definition = await client.beta.assistants.create(
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model=AzureOpenAISettings().chat_deployment_name,
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instructions="Answer questions about the menu.",
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name="Host",
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)
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# 3. Create a Semantic Kernel agent for the Azure OpenAI assistant
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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plugins=[MenuPlugin()], # The plugins can be passed in as a list to the constructor
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)
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# Note: plugins can also be configured on the Kernel and passed in as a parameter to the OpenAIAssistantAgent
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# 4. Create a new thread for use with the assistant
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# If no thread is provided, a new thread will be
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# created and returned with the initial response
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thread: AssistantAgentThread = None
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try:
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for user_input in USER_INPUTS:
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print(f"# User: '{user_input}'")
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# 6. Invoke the agent for the current thread and print the response
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async for response in agent.invoke(messages=user_input, thread=thread):
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print(f"# Agent: {response}")
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thread = response.thread
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finally:
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# 7. Clean up the resources
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await thread.delete() if thread else None
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await agent.client.beta.assistants.delete(assistant_id=agent.id)
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"""
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You should see output similar to the following:
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# User: 'Hello'
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# Agent: Hello! How can I assist you today?
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# User: 'What is the special soup?'
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# Agent: The special soup today is Clam Chowder. Would you like to know more about any other menu items?
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# User: 'What is the special drink?'
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# Agent: The special drink today is Chai Tea. Would you like more information on anything else?
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# User: 'Thank you'
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# Agent: You're welcome! If you have any more questions or need further assistance, feel free to ask.
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Enjoy your day!
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from semantic_kernel.agents import AssistantAgentThread, OpenAIAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import OpenAISettings
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from semantic_kernel.contents import AuthorRole, ChatMessageContent, FileReferenceContent, ImageContent, TextContent
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"""
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The following sample demonstrates how to create an OpenAI
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assistant using OpenAI configuration, and leverage the
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multi-modal content types to have the assistant describe images
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and answer questions about them. This sample uses non-streaming responses.
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"""
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async def main():
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# 1. Create the OpenAI Assistant Agent client
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# Note Azure OpenAI doesn't support vision files yet
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client = OpenAIAssistantAgent.create_client()
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# 2. Load a sample image of a cat used for the assistant to describe
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file_path = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "resources", "cat.jpg")
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with open(file_path, "rb") as file:
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file = await client.files.create(file=file, purpose="assistants")
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# 3. Create the assistant on the OpenAI service
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definition = await client.beta.assistants.create(
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model=OpenAISettings().chat_model_id,
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instructions="Answer questions about the provided images.",
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name="Vision",
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)
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# 4. Create a Semantic Kernel agent for the OpenAI assistant
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agent = OpenAIAssistantAgent(
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client=client,
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definition=definition,
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)
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# 5. Create a new thread for use with the assistant
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# If no thread is provided, a new thread will be
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# created and returned with the initial response
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thread: AssistantAgentThread = None
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# 6. Define the user messages with the image content to simulate the conversation
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user_messages = {
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ChatMessageContent(
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role=AuthorRole.USER,
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items=[
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TextContent(text="Describe this image."),
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ImageContent(
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uri="https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/New_york_times_square-terabass.jpg/1200px-New_york_times_square-terabass.jpg"
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),
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],
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),
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ChatMessageContent(
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role=AuthorRole.USER,
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items=[
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TextContent(text="What is the main color in this image?"),
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ImageContent(uri="https://upload.wikimedia.org/wikipedia/commons/5/56/White_shark.jpg"),
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],
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),
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ChatMessageContent(
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role=AuthorRole.USER,
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items=[
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TextContent(text="Is there an animal in this image?"),
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FileReferenceContent(file_id=file.id),
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],
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),
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}
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try:
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for message in user_messages:
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print(f"# User: {str(message)}") # type: ignore
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# 8. Invoke the agent for the current thread and print the response
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async for response in agent.invoke(messages=message, thread=thread):
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print(f"# Agent: {response}\n")
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thread = response.thread
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finally:
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# 9. Clean up the resources
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await client.files.delete(file.id)
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await thread.delete() if thread else None
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await agent.client.beta.assistants.delete(assistant_id=agent.id)
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if __name__ == "__main__":
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asyncio.run(main())
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+59
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from azure.identity import AzureCliCredential
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from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
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"""
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The following sample demonstrates how to create an OpenAI
|
||||
assistant using either Azure OpenAI or OpenAI and leverage the
|
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assistant's code interpreter functionality to have it write
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Python code to print Fibonacci numbers.
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"""
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TASK = "Use code to determine the values in the Fibonacci sequence that that are less than the value of 101?"
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async def main():
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# 1. Create the client using Azure OpenAI resources and configuration
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client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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# 2. Configure the code interpreter tool and resources for the Assistant
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code_interpreter_tool, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool()
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# 3. Create the assistant on the Azure OpenAI service
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definition = await client.beta.assistants.create(
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model=AzureOpenAISettings().chat_deployment_name,
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name="CodeRunner",
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instructions="Run the provided request as code and return the result.",
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tools=code_interpreter_tool,
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tool_resources=code_interpreter_tool_resources,
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)
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||||
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||||
# 4. Create a Semantic Kernel agent for the Azure OpenAI assistant
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agent = AzureAssistantAgent(
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client=client,
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definition=definition,
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||||
)
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||||
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||||
# 5. Create a new thread for use with the assistant
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||||
# If no thread is provided, a new thread will be
|
||||
# created and returned with the initial response
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thread: AssistantAgentThread = None
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||||
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print(f"# User: '{TASK}'")
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try:
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# 6. Invoke the agent for the current thread and print the response
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async for response in agent.invoke(messages=TASK, thread=thread):
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print(f"# Agent: {response}")
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thread = response.thread
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finally:
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# 7. Clean up the resources
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await thread.delete() if thread else None
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||||
await agent.client.beta.assistants.delete(agent.id)
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||||
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||||
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||||
if __name__ == "__main__":
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asyncio.run(main())
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+81
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# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import os
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||||
|
||||
from azure.identity import AzureCliCredential
|
||||
|
||||
from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
|
||||
from semantic_kernel.connectors.ai.open_ai import AzureOpenAISettings
|
||||
|
||||
"""
|
||||
The following sample demonstrates how to create an OpenAI
|
||||
Assistant using either Azure OpenAI or OpenAI and leverage the
|
||||
assistant's file search functionality.
|
||||
"""
|
||||
|
||||
# Simulate a conversation with the agent
|
||||
USER_INPUTS = {
|
||||
"Who is the youngest employee?",
|
||||
"Who works in sales?",
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||||
"I have a customer request, who can help me?",
|
||||
}
|
||||
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||||
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||||
async def main():
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||||
# 1. Create the client using Azure OpenAI resources and configuration
|
||||
client = AzureAssistantAgent.create_client(credential=AzureCliCredential())
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||||
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||||
# 2. Read and upload the file to the Azure OpenAI assistant service
|
||||
pdf_file_path = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "resources", "employees.pdf"
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||||
)
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||||
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||||
with open(pdf_file_path, "rb") as file:
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||||
file = await client.files.create(file=file, purpose="assistants")
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||||
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||||
vector_store = await client.vector_stores.create(
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||||
name="step4_assistant_file_search",
|
||||
file_ids=[file.id],
|
||||
)
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||||
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||||
# 3. Create file search tool with uploaded resources
|
||||
file_search_tool, file_search_tool_resources = AzureAssistantAgent.configure_file_search_tool(vector_store.id)
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||||
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||||
# 4. Create the assistant on the Azure OpenAI service with the file search tool
|
||||
definition = await client.beta.assistants.create(
|
||||
model=AzureOpenAISettings().chat_deployment_name,
|
||||
instructions="Find answers to the user's questions in the provided file.",
|
||||
name="FileSearch",
|
||||
tools=file_search_tool,
|
||||
tool_resources=file_search_tool_resources,
|
||||
)
|
||||
|
||||
# 5. Create a Semantic Kernel agent for the Azure OpenAI assistant
|
||||
agent = AzureAssistantAgent(
|
||||
client=client,
|
||||
definition=definition,
|
||||
)
|
||||
|
||||
# 6. Create a new thread for use with the assistant
|
||||
# If no thread is provided, a new thread will be
|
||||
# created and returned with the initial response
|
||||
thread: AssistantAgentThread = None
|
||||
|
||||
try:
|
||||
for user_input in USER_INPUTS:
|
||||
print(f"# User: '{user_input}'")
|
||||
# 7. Invoke the agent for the current thread and print the response
|
||||
async for response in agent.invoke(messages=user_input, thread=thread):
|
||||
print(f"# Agent: {response}")
|
||||
thread = response.thread
|
||||
finally:
|
||||
# 9. Clean up the resources
|
||||
await client.files.delete(file.id)
|
||||
await client.vector_stores.delete(vector_store.id)
|
||||
await client.beta.threads.delete(thread.id)
|
||||
await client.beta.assistants.delete(agent.id)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
+98
@@ -0,0 +1,98 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
import asyncio
|
||||
from typing import Annotated
|
||||
|
||||
from semantic_kernel.agents import AgentRegistry, OpenAIAssistantAgent
|
||||
from semantic_kernel.functions import kernel_function
|
||||
|
||||
"""
|
||||
The following sample demonstrates how to create an OpenAI Assistant agent that answers
|
||||
questions about a sample menu using a Semantic Kernel Plugin. The agent is created
|
||||
using a yaml declarative spec.
|
||||
"""
|
||||
|
||||
# Simulate a conversation with the agent
|
||||
USER_INPUTS = [
|
||||
"Hello",
|
||||
"What is the special soup?",
|
||||
"How much does that cost?",
|
||||
"Thank you",
|
||||
]
|
||||
|
||||
# Define the YAML string for the sample
|
||||
SPEC = """
|
||||
type: openai_assistant
|
||||
name: Host
|
||||
instructions: Respond politely to the user's questions.
|
||||
model:
|
||||
id: ${OpenAI:ChatModelId}
|
||||
tools:
|
||||
- id: MenuPlugin.get_specials
|
||||
type: function
|
||||
- id: MenuPlugin.get_item_price
|
||||
type: 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) -> 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"
|
||||
|
||||
|
||||
async def main():
|
||||
# 1. Create the client using Azure OpenAI resources and configuration
|
||||
client = OpenAIAssistantAgent.create_client()
|
||||
|
||||
# 2. Create the assistant on the Azure OpenAI service
|
||||
agent: OpenAIAssistantAgent = await AgentRegistry.create_from_yaml(
|
||||
SPEC,
|
||||
plugins=[MenuPlugin()],
|
||||
client=client,
|
||||
)
|
||||
|
||||
# 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"# User: {user_input}")
|
||||
# 4. Invoke the agent for the specified thread for response
|
||||
async for response in agent.invoke(
|
||||
messages=user_input,
|
||||
thread=thread,
|
||||
):
|
||||
print(f"# {response.name}: {response}")
|
||||
thread = response.thread
|
||||
finally:
|
||||
# 5. Clean up the resources
|
||||
await thread.delete() if thread else None
|
||||
await agent.client.beta.assistants.delete(assistant_id=agent.id)
|
||||
|
||||
"""
|
||||
Sample Output:
|
||||
|
||||
# User: Hello
|
||||
# Agent: Hello! How can I assist you today?
|
||||
# User: What is the special soup?
|
||||
# ...
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user