wehub-resource-sync
124 lines
5.5 KiB
Markdown
124 lines
5.5 KiB
Markdown
# Semantic Kernel: Agent concept examples
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This project contains a step by step guide to get started with _Semantic Kernel Agents_ in Python.
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## PyPI
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- For the use of Chat Completion agents, the minimum allowed Semantic Kernel pypi version is 1.3.0.
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- For the use of OpenAI Assistant agents, the minimum allowed Semantic Kernel pypi version is 1.4.0.
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- For the use of Agent Group Chat, the minimum allowed Semantic kernel pypi version is 1.6.0.
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- For the use of Streaming OpenAI Assistant agents, the minimum allowed Semantic Kernel pypi version is 1.11.0.
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- For the use of AzureAI and Bedrock agents, the minimum allowed Semantic Kernel pypi version is 1.21.0.
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- For the use of Crew.AI as a plugin, the minimum allowed Semantic Kernel pypi version is 1.21.1.
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- For the use of OpenAI Responses agents, the minimum allowed Semantic Kernel pypi version is 1.27.0.
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## Source
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- [Semantic Kernel Agent Framework](../../../semantic_kernel/agents/)
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## Examples
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The concept agents examples are grouped by prefix:
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Prefix|Description
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autogen_conversable_agent| How to use [AutoGen 0.2 Conversable Agents](https://microsoft.github.io/autogen/0.2/docs/Getting-Started) within Semantic Kernel.
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azure_ai_agent|How to use an [Azure AI Agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure) within Semantic Kernel.
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chat_completion_agent|How to use Semantic Kernel Chat Completion agents that leverage AI Connector Chat Completion APIs.
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bedrock|How to use [AWS Bedrock agents](https://aws.amazon.com/bedrock/agents/) in Semantic Kernel.
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mixed_chat|How to combine different agent types.
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openai_assistant|How to use [OpenAI Assistants](https://platform.openai.com/docs/assistants/overview) in Semantic Kernel.
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openai_responses|How to use [OpenAI Responses](https://platform.openai.com/docs/api-reference/responses) in Semantic Kernel.
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## Configuring the Kernel
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Similar to the Semantic Kernel Python concept samples, it is necessary to configure the secrets
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and keys used by the kernel. See the follow "Configuring the Kernel" [guide](../README.md#configuring-the-kernel) for
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more information.
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## Running Concept Samples
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Concept samples can be run in an IDE or via the command line. After setting up the required api key or token authentication
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for your AI connector, the samples run without any extra command line arguments.
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## Managing Conversation Threads with AgentThread
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This section explains how to manage conversation context using the `AgentThread` base class. Each agent has its own thread implementation that preserves the context of a conversation. If you invoke an agent without specifying a thread, a new one is created automatically and returned as part of the `AgentItemResponse` object—which includes both the message (of type `ChatMessageContent`) and the thread (`AgentThread`). You also have the option to create a custom thread for a specific agent by providing a unique `thread_id`.
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## Overview
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**Automatic Thread Creation:**
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When an agent is invoked without a provided thread, it creates a new thread to manage the conversation context automatically.
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**Manual Thread Management:**
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You can explicitly create a specific implementation for the desired `Agent` that derives from the base class `AgentThread`. You have the option to assign a `thread_id` to manage the conversation session. This is particularly useful in complex scenarios or multi-user environments.
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## Code Example
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Below is a sample code snippet demonstrating thread management:
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```python
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from semantic_kernel.agents import ChatCompletionAgent
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from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
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USER_INPUTS = [
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"Why is the sky blue?",
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]
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# 1. Create the agent by specifying the service
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agent = ChatCompletionAgent(
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service=AzureChatCompletion(),
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name="Assistant",
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instructions="Answer the user's questions.",
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)
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# 2. Create a thread to hold the conversation
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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 = None
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for user_input in USER_INPUTS:
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print(f"# User: {user_input}")
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# 3. Invoke the agent for a response
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response = await agent.get_response(
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message=user_input,
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thread=thread,
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)
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print(f"# {response.name}: {response}")
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thread = response.thread
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# 4. Cleanup: Clear the thread
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await thread.end() if thread else None
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"""
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Sample output:
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# User: Hello, I am John Doe.
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# Assistant: Hello, John Doe! How can I assist you today?
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# User: What is your name?
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# Assistant: I don't have a personal name like a human does, but you can call me Assistant.?
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# User: What is my name?
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# Assistant: You mentioned that your name is John Doe. How can I assist you further, John?
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"""
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```
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## Detailed Explanation
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**Thread Initialization:**
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The thread is initially set to `None`. If no thread is provided, the agent creates a new one and includes it in the response.
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**Processing User Inputs:**
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A list of `user_inputs` simulates a conversation. For each input:
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- The code prints the user's message.
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- The agent is invoked using the `get_response` method, which returns the response asynchronously.
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**Handling Responses:**
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- The thread is updated with each response to maintain the conversation context.
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**Cleanup:**
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The code safely ends the thread if it exists.
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By leveraging the `AgentThread`, you ensure that each conversation maintains its context seamlessly -- whether the thread is automatically created or manually managed with a custom `thread_id`. This approach is crucial for developing agents that deliver coherent and context-aware interactions.
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