wehub-resource-sync
99 lines
3.9 KiB
Markdown
99 lines
3.9 KiB
Markdown
## 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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# 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=AzureOpenAISettings().chat_deployment_name
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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
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thread = None
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# Invoke the agent
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async for content in agent.invoke(messages="user input", thread=thread):
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print(f"# {content.role}: {content.content}")
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# Grab the thread from the response to continue with the current context
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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=AzureOpenAISettings().chat_deployment_name
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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
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thread = None
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# Invoke the agent
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async for content in agent.invoke(messages="user input", thread=thread):
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print(f"# {content.role}: {content.content}")
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# Grab the thread from the response to continue with the current context
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thread = response.thread
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``` |