chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from azure.identity import AzureCliCredential
+
+from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.connectors.mcp import MCPStreamableHttpPlugin
+
+"""
+The following sample demonstrates how to create a chat completion agent that
+answers questions about Github using a Semantic Kernel Plugin from a MCP server.
+
+It uses the Azure OpenAI service to create a agent, so make sure to
+set the required environment variables for the Azure AI Foundry service:
+- AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+- Optionally: AZURE_OPENAI_API_KEY
+If this is not set, it's also possible to pass AsyncTokenCredential to the service, e.g. AzureCliCredential.
+"""
+
+
+# Simulate a conversation with the agent
+USER_INPUTS = [
+    "How do I make a Python chat completion request in Semantic Kernel using Azure OpenAI?",
+]
+
+
+async def main():
+    # 1. Create the agent
+    async with MCPStreamableHttpPlugin(
+        name="LearnSite",
+        description="Learn Docs Plugin",
+        url="https://learn.microsoft.com/api/mcp",
+    ) as learn_plugin:
+        agent = ChatCompletionAgent(
+            service=AzureChatCompletion(credential=AzureCliCredential()),
+            name="DocsAgent",
+            instructions="Answer questions about the Microsoft's Semantic Kernel SDK.",
+            plugins=[learn_plugin],
+        )
+
+        for user_input in USER_INPUTS:
+            # 2. Create a thread to hold the conversation
+            # If no thread is provided, a new thread will be
+            # created and returned with the initial response
+            thread: ChatHistoryAgentThread | None = None
+
+            print(f"# User: {user_input}")
+            # 3. Invoke the agent for a response
+            response = await agent.get_response(messages=user_input, thread=thread)
+            print(f"# {response.name}: {response} ")
+            thread = response.thread
+
+            # 4. Cleanup: Clear the thread
+            await thread.delete() if thread else None
+
+
+"""
+Sample output:
+
+# User: How do I make a Python chat completion request in Semantic Kernel using Azure OpenAI?
+# DocsAgent: To make a **Python chat completion request in Semantic Kernel using Azure OpenAI**, follow these steps:
+
+---
+
+### 1. Install Semantic Kernel
+
+```bash
+pip install semantic-kernel
+```
+
+---
+
+### 2. Import Necessary Libraries
+
+```python
+import semantic_kernel as sk
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+```
+
+---
+
+### 3. Initialize the Kernel and Add Azure OpenAI Service
+
+```python
+# Initialize the kernel
+kernel = sk.Kernel()
+
+# Set your Azure OpenAI details
+deployment_name = "your-chat-deployment"
+endpoint = "https://your-resource-name.openai.azure.com/"
+api_key = "your-azure-openai-api-key"
+
+# Add Azure Chat Completion service
+kernel.add_chat_service(
+    "azure_chat",
+    AzureChatCompletion(
+        deployment_name=deployment_name,
+        endpoint=endpoint,
+        api_key=api_key,
+    ),
+)
+```
+
+---
+
+### 4. Create a Chat History and Send a Request
+
+```python
+# Create an initial chat history
+history = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What can you do?"},
+]
+
+# Get chat completion
+result = kernel.chat.complete(
+    chat_history=history,
+    max_tokens=100,
+    temperature=0.7,
+    top_p=0.95,
+)
+
+print(result)
+```
+
+---
+
+## Example Summary
+
+This makes a chat completion request to Azure OpenAI through Semantic Kernel in Python. You can add more user/assistant 
+turns to `history`.
+"""
+
+
+if __name__ == "__main__":
+    asyncio.run(main())