chore: import upstream snapshot with attribution

2026-07-13 13:31:35 +08:00
commit c275ba2868
13613 changed files with 2980806 additions and 0 deletions
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+# Scenario 2: Integrate Docs MCP into a web development project
+# This script demonstrates how to use Chainlit to build a conversational web app
+# that queries the Microsoft Learn Docs MCP server.
+
+import chainlit as cl
+import logging
+import json
+from mcp.client.streamable_http import streamablehttp_client
+from mcp import ClientSession
+
+from semantic_kernel.kernel import Kernel
+from azure.core.credentials import AzureKeyCredential
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.functions import kernel_function
+from semantic_kernel.agents import ChatCompletionAgent
+
+
+MCP_SERVER_URL = "https://learn.microsoft.com/api/mcp"
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger('mcp_client')
+
+# MCP Docs Plugin as a Semantic Kernel plugin
+class MCPDocsPlugin:
+    def __init__(self, mcp_server_url):
+        self.mcp_server_url = mcp_server_url
+
+    @kernel_function(name="search_docs", description="Search Microsoft Docs using MCP")
+    async def search_docs(self, question: str) -> str:
+        async with streamablehttp_client(self.mcp_server_url) as (read_stream, write_stream, _):
+            async with ClientSession(read_stream, write_stream) as session:
+                await session.initialize()
+                result = await session.call_tool("microsoft_docs_search", {"question": question})
+                output = []
+                if hasattr(result, 'content'):
+                    for item in result.content:
+                        try:
+                            my_list = json.loads(item.text)
+                            for doc in my_list:
+                                title = doc.get('title', 'No title')
+                                content = doc.get('content', 'No content')
+                                output.append(f"**{title}**\n{content}")
+                        except Exception:
+                            output.append(item.text)
+                    return "\n".join(output)
+                else:
+                    return "No content returned from the search."
+
+# Register the MCP Docs search as a function
+async def mcp_docs_search(question: str):
+    async with streamablehttp_client(MCP_SERVER_URL) as (read_stream, write_stream, _):
+        async with ClientSession(read_stream, write_stream) as session:
+            await session.initialize()
+            result = await session.call_tool("microsoft_docs_search", {"question": question})
+            output = []
+            if hasattr(result, 'content'):
+                for item in result.content:
+                    try:
+                        my_list = json.loads(item.text)
+                        for doc in my_list:
+                            title = doc.get('title', 'No title')
+                            content = doc.get('content', 'No content')
+                            output.append(f"**{title}**\n{content}")
+                    except Exception:
+                        output.append(item.text)
+                return "\n".join(output)
+            else:
+                return "No content returned from the search."
+
+@cl.on_chat_start
+async def start():
+    await cl.Message(content="Welcome! Enter your Microsoft Docs search query below.").send()
+    kernel = Kernel()
+    service_id = "agent"
+    kernel.add_service(AzureChatCompletion(service_id=service_id))
+    from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+    settings = kernel.get_prompt_execution_settings_from_service_id(service_id=service_id)
+    settings.function_choice_behavior = FunctionChoiceBehavior.Auto()
+    # Register the MCPDocsPlugin
+    mcp_plugin = MCPDocsPlugin(MCP_SERVER_URL)
+    kernel.add_plugin(mcp_plugin, plugin_name="MCPDocs")
+    # Create the agent
+    agent = ChatCompletionAgent(
+        service=AzureChatCompletion(),
+        name="DocsAgent",
+        instructions="You are a helpful assistant that uses the MCPDocs plugin to answer Microsoft Docs questions. Format your answers clearly.",
+        plugins=[mcp_plugin]
+    )
+    cl.user_session.set("kernel", kernel)
+    cl.user_session.set("agent", agent)
+    cl.user_session.set("settings", settings)
+
+@cl.on_message
+async def handle_message(message: cl.Message):
+    agent = cl.user_session.get("agent")
+    user_query = message.content.strip()
+    if not user_query:
+        await cl.Message(content="Query cannot be empty. Please try again.").send()
+        return
+    answer = cl.Message(content="Processing your request...")
+    await answer.send()
+    try:
+        response_printed = False
+        async for content in agent.invoke(user_query):
+            msg = content.content
+            if hasattr(msg, "content"):
+                msg = msg.content
+            if msg:
+                await answer.stream_token(str(msg))
+                response_printed = True
+        if not response_printed:
+            await answer.stream_token("No response generated by the agent.\n")
+        await answer.update()
+    except Exception as e:
+        await answer.stream_token(f"\n\n❌ Error: {str(e)}\n\n")
+        await answer.update()