Merge pull request #5 from madebygps/main

small refactor on framework examples

Author: madebygps

agentframework_mcp_learn.py

@@ -4,19 +4,31 @@
 import logging
 import os
 
-
+from azure.identity import DefaultAzureCredential
 from dotenv import load_dotenv
 from rich import print
 from rich.logging import RichHandler
+
 from agent_framework import ChatAgent, MCPStreamableHTTPTool
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.openai import OpenAIChatClient
-from azure.identity import DefaultAzureCredential
 
-logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
+# Configure logging
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(message)s",
+    datefmt="[%X]",
+    handlers=[RichHandler()]
+)
 logger = logging.getLogger("learn_mcp_lang")
 
+# Load environment variables
 load_dotenv(override=True)
+
+# Constants
+LEARN_MCP_URL = "https://learn.microsoft.com/api/mcp"
+
+# Configure chat client based on API_HOST
 API_HOST = os.getenv("API_HOST", "github")
 
 if API_HOST == "azure":
@@ -44,11 +56,17 @@
     )
 
 
-async def http_mcp_example():
+async def http_mcp_example() -> None:
+    """
+    Demonstrate MCP integration with Microsoft Learn documentation.
+    
+    Creates an agent that can answer questions about Microsoft documentation
+    using the Microsoft Learn MCP server.
+    """
     async with (
         MCPStreamableHTTPTool(
             name="Microsoft Learn MCP",
-            url="https://learn.microsoft.com/api/mcp",
+            url=LEARN_MCP_URL,
             headers={"Authorization": "Bearer your-token"},
         ) as mcp_server,
         ChatAgent(
@@ -57,11 +75,10 @@ async def http_mcp_example():
             instructions="You help with Microsoft documentation questions.",
         ) as agent,
     ):
-        result = await agent.run(
-            "How to create an Azure storage account using az cli?",
-            tools=mcp_server
-        )
+        query = "How to create an Azure storage account using az cli?"
+        result = await agent.run(query, tools=mcp_server)
         print(result)
 
+
 if __name__ == "__main__":
     asyncio.run(http_mcp_example())

langchainv1_mcp_http.py

@@ -1,25 +1,40 @@
 import asyncio
 import logging
 import os
+from datetime import datetime
 
 import azure.identity
 from dotenv import load_dotenv
 from langchain.agents import create_agent
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import HumanMessage, SystemMessage
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_openai import AzureChatOpenAI, ChatOpenAI
 from pydantic import SecretStr
 from rich.logging import RichHandler
 
-logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
+# Configure logging
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(message)s",
+    datefmt="[%X]",
+    handlers=[RichHandler()]
+)
 logger = logging.getLogger("itinerario_lang")
 
+# Load environment variables
 load_dotenv(override=True)
+
+# Constants
+MCP_SERVER_URL = "http://localhost:8000/mcp/"
+AZURE_COGNITIVE_SERVICES_SCOPE = "https://cognitiveservices.azure.com/.default"
+
+# Configure language model based on API_HOST
 API_HOST = os.getenv("API_HOST", "github")
 
 if API_HOST == "azure":
     token_provider = azure.identity.get_bearer_token_provider(
-        azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+        azure.identity.DefaultAzureCredential(),
+        AZURE_COGNITIVE_SERVICES_SCOPE
     )
     base_model = AzureChatOpenAI(
         azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
@@ -43,38 +58,43 @@
     base_model = ChatOpenAI(model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"))
 
 
-async def run_agent():
-    from datetime import datetime
-    from langchain_core.messages import SystemMessage
-    
+async def run_agent() -> None:
+    """
+    Run the agent to process expense-related queries using MCP tools.
+    """
+    # Initialize MCP client
     client = MultiServerMCPClient(
         {
-            "itinerary": {
-                "url": "http://localhost:8000/mcp/",
+            "expenses": {
+                "url": MCP_SERVER_URL,
                 "transport": "streamable_http",
             }
         }
     )
 
+    # Get tools and create agent
     tools = await client.get_tools()
     agent = create_agent(base_model, tools)
 
+    # Prepare query with context
     today = datetime.now().strftime("%Y-%m-%d")
-    user_query = (
-        "yesterday I bought a laptop for $1200 using my visa. "
-    )
+    user_query = "yesterday I bought a laptop for $1200 using my visa."
 
+    # Invoke agent
     response = await agent.ainvoke({
         "messages": [
             SystemMessage(content=f"Today's date is {today}."),
             HumanMessage(content=user_query)
         ]
     })
-    final = response["messages"][-1].content
-    print(final)
+    
+    # Display result
+    final_response = response["messages"][-1].content
+    print(final_response)
 
 
-def main():
+def main() -> None:
+    """Main entry point for the application."""
     asyncio.run(run_agent())