Merge pull request #16 from Azure-Samples/pamelaschangesforsomereason

Agents + MCP examples from live stream

Author: pamelafox

.vscode/launch.json

@@ -0,0 +1,13 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": false
+        }
+    ]
+}

example_data/plants.pdf

@@ -0,0 +1,26 @@
+import logging
+import os
+
+import azure.identity
+from azure.ai.inference import ChatCompletionsClient
+from azure.ai.inference.models import SystemMessage, UserMessage
+from dotenv import load_dotenv
+
+logging.basicConfig(level=logging.DEBUG)
+load_dotenv(override=True)
+
+client = ChatCompletionsClient(
+    endpoint=f'{os.environ["AZURE_OPENAI_ENDPOINT"]}/openai/deployments/{os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"]}',
+    credential=azure.identity.AzureDeveloperCliCredential(tenant_id=os.getenv("AZURE_TENANT_ID")),
+    credential_scopes=["https://cognitiveservices.azure.com/.default"],
+    api_version=os.environ["AZURE_OPENAI_VERSION"],
+)
+
+response = client.complete(
+    messages=[
+        SystemMessage(content="You are a helpful assistant."),
+        UserMessage(content="What is the capital of France?"),
+    ],
+    model=os.environ["AZURE_OPENAI_CHAT_MODEL"],
+)
+print(response.choices[0].message.content)

examples/azureai_azureopenai.py

@@ -0,0 +1,12 @@
+
+    digraph G {
+        graph [splines=true];
+        node [fontname="Arial"];
+        edge [penwidth=1.5];
+    "__start__" [label="__start__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"__end__" [label="__end__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"Triage agent" [label="Triage agent", shape=box, style=filled, fillcolor=lightyellow, width=1.5, height=0.8];"Spanish agent" [label="Spanish agent", shape=box, style=filled, style=rounded, fillcolor=lightyellow, width=1.5, height=0.8];"__start__" [label="__start__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"__end__" [label="__end__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"Spanish agent" [label="Spanish agent", shape=box, style=filled, fillcolor=lightyellow, width=1.5, height=0.8];"get_weather" [label="get_weather", shape=ellipse, style=filled, fillcolor=lightgreen, width=0.5, height=0.3];"English agent" [label="English agent", shape=box, style=filled, style=rounded, fillcolor=lightyellow, width=1.5, height=0.8];"__start__" [label="__start__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"__end__" [label="__end__", shape=ellipse, style=filled, fillcolor=lightblue, width=0.5, height=0.3];"English agent" [label="English agent", shape=box, style=filled, fillcolor=lightyellow, width=1.5, height=0.8];"get_weather" [label="get_weather", shape=ellipse, style=filled, fillcolor=lightgreen, width=0.5, height=0.3];"__start__" -> "Triage agent";
+            "Triage agent" -> "Spanish agent";
+        "Spanish agent" -> "get_weather" [style=dotted, penwidth=1.5];
+        "get_weather" -> "Spanish agent" [style=dotted, penwidth=1.5];"Spanish agent" -> "__end__";
+            "Triage agent" -> "English agent";
+        "English agent" -> "get_weather" [style=dotted, penwidth=1.5];
+        "get_weather" -> "English agent" [style=dotted, penwidth=1.5];"English agent" -> "__end__";}

examples/images/langgraph_mcp_http_graph.png

@@ -0,0 +1,49 @@
+# https://github.com/JRAlexander/IntroToAgents1-Oxford/blob/main/intro-langgraph/time-travel.ipynb
+
+import os
+
+import azure.identity
+from dotenv import load_dotenv
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langgraph.prebuilt import create_react_agent  # REACT
+
+# Setup the client to use either Azure OpenAI or GitHub Models
+load_dotenv(override=True)
+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")
+    model = AzureChatOpenAI(
+        azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+        azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
+        azure_ad_token_provider=token_provider,
+    )
+else:
+    model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
+
+
+async def setup_agent():
+    client = MultiServerMCPClient(
+        {
+            "itinerary": {
+                # Make sure you start your itinerary server on port 8000
+                "url": "http://localhost:8000/mcp/",
+                "transport": "streamable_http",
+            }
+        }
+    )
+
+    tools = await client.get_tools()
+    agent = create_react_agent(model, tools)
+    hotel_response = await agent.ainvoke({"messages": "Find me a hotel in San Francisco for 2 nights starting from 2024-01-01. I need a hotel with free WiFi and a pool."})
+    print(hotel_response["messages"][-1].content)
+
+
+if __name__ == "__main__":
+    import asyncio
+    import logging
+
+    logging.basicConfig(level=logging.WARNING)
+    asyncio.run(setup_agent())

examples/images/openai_agents_handoffs.dot

@@ -0,0 +1,64 @@
+import os
+
+import azure.identity
+from dotenv import load_dotenv
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langgraph.graph import START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode, tools_condition
+
+# Setup the client to use either Azure OpenAI or GitHub Models
+load_dotenv(override=True)
+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")
+    model = AzureChatOpenAI(
+        azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+        azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
+        azure_ad_token_provider=token_provider,
+    )
+else:
+    model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
+
+
+async def setup_agent():
+    client = MultiServerMCPClient(
+        {
+            "weather": {
+                # make sure you start your weather server on port 8000
+                "url": "http://localhost:8000/mcp/",
+                "transport": "streamable_http",
+            }
+        }
+    )
+    tools = await client.get_tools()
+
+    def call_model(state: MessagesState):
+        response = model.bind_tools(tools).invoke(state["messages"])
+        return {"messages": response}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node(call_model)
+    builder.add_node(ToolNode(tools))
+    builder.add_edge(START, "call_model")
+    builder.add_conditional_edges(
+        "call_model",
+        tools_condition,
+    )
+    builder.add_edge("tools", "call_model")
+    graph = builder.compile()
+    hotel_response = await graph.ainvoke({"messages": "Find me a hotel in San Francisco for 2 nights starting from 2024-01-01. I need a hotel with free WiFi and a pool."})
+    print(hotel_response["messages"][-1].content)
+    image_bytes = graph.get_graph().draw_mermaid_png()
+    with open("examples/images/langgraph_mcp_http_graph.png", "wb") as f:
+        f.write(image_bytes)
+
+
+if __name__ == "__main__":
+    import asyncio
+    import logging
+
+    logging.basicConfig(level=logging.WARNING)
+    asyncio.run(setup_agent())

examples/images/openai_agents_handoffs.png

@@ -0,0 +1,129 @@
+import random
+import re
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Annotated
+
+from faker import Faker
+from mcp.server.fastmcp import FastMCP
+from pydantic import Field
+
+app = FastMCP()
+fake = Faker()
+
+
+@dataclass
+class Hotel:
+    name: str
+    address: str
+    location: str
+    rating: float
+    price_per_night: float
+    hotel_type: str
+    amenities: list[str]
+    available_rooms: int
+
+
+@dataclass
+class HotelSuggestions:
+    hotels: list[Hotel]
+
+
+def validate_iso_date(date_str: str, param_name: str):
+    """
+    Validates that a string is in ISO format (YYYY-MM-DD) and returns the parsed date.
+
+    Args:
+        date_str: The date string to validate
+        param_name: Name of the parameter for error messages
+
+    Returns:
+        The parsed date object
+
+    Raises:
+        ValueError: If the date is not in ISO format or is invalid
+    """
+    iso_pattern = re.compile(r"^\d{4}-\d{2}-\d{2}$")
+    if not iso_pattern.match(date_str):
+        raise ValueError(f"{param_name} must be in ISO format (YYYY-MM-DD), got: {date_str}")
+
+    try:
+        return datetime.strptime(date_str, "%Y-%m-%d").date()
+    except ValueError as e:
+        raise ValueError(f"Invalid {param_name}: {e}")
+
+
+@app.tool()
+async def suggest_hotels(
+    location: Annotated[str, Field(description="Location (city or area) to search for hotels")],
+    check_in: Annotated[str, Field(description="Check-in date in ISO format (YYYY-MM-DD)")],
+    check_out: Annotated[str, Field(description="Check-out date in ISO format (YYYY-MM-DD)")],
+) -> HotelSuggestions:
+    """
+    Suggest hotels based on location and dates.
+    """
+    # Validate dates
+    check_in_date = validate_iso_date(check_in, "check_in")
+    check_out_date = validate_iso_date(check_out, "check_out")
+
+    # Ensure check_out is after check_in
+    if check_out_date <= check_in_date:
+        raise ValueError("check_out date must be after check_in date")
+
+    # Create realistic mock data for hotels
+    hotel_types = ["Luxury", "Boutique", "Budget", "Business"]
+    amenities = ["Free WiFi", "Pool", "Spa", "Gym", "Restaurant", "Bar", "Room Service", "Parking"]
+
+    # Generate a rating between 3.0 and 5.0
+    def generate_rating():
+        return round(random.uniform(3.0, 5.0), 1)
+
+    # Generate a price based on hotel type
+    def generate_price(hotel_type):
+        price_ranges = {
+            "Luxury": (250, 600),
+            "Boutique": (180, 350),
+            "Budget": (80, 150),
+            "Resort": (200, 500),
+            "Business": (150, 300),
+        }
+        min_price, max_price = price_ranges.get(hotel_type, (100, 300))
+        return round(random.uniform(min_price, max_price))
+
+    # Generate between 3 and 8 hotels
+    num_hotels = random.randint(3, 8)
+    hotels = []
+
+    neighborhoods = [
+        "Downtown",
+        "Historic District",
+        "Waterfront",
+        "Business District",
+        "Arts District",
+        "University Area",
+    ]
+
+    for i in range(num_hotels):
+        hotel_type = random.choice(hotel_types)
+        hotel_amenities = random.sample(amenities, random.randint(3, 6))
+        neighborhood = random.choice(neighborhoods)
+
+        hotel = Hotel(
+            name=f"{hotel_type} {['Hotel', 'Inn', 'Suites', 'Resort', 'Plaza'][random.randint(0, 4)]}",
+            address=fake.street_address(),
+            location=f"{neighborhood}, {location}",
+            rating=generate_rating(),
+            price_per_night=generate_price(hotel_type),
+            hotel_type=hotel_type,
+            amenities=hotel_amenities,
+            available_rooms=random.randint(1, 15),
+        )
+        hotels.append(hotel)
+
+    # Sort by rating to show best hotels first
+    hotels.sort(key=lambda x: x.rating, reverse=True)
+    return HotelSuggestions(hotels=hotels)
+
+
+if __name__ == "__main__":
+    app.run(transport="streamable-http")

examples/langgraph_mcp_http.py

@@ -1,11 +1,13 @@
 import asyncio
+import logging
 import os
 
 import azure.identity
 import openai
 from agents import Agent, OpenAIChatCompletionsModel, Runner, set_tracing_disabled
 from dotenv import load_dotenv
 
+logging.basicConfig(level=logging.WARNING)
 # Disable tracing since we're not connected to a supported tracing provider
 set_tracing_disabled(disabled=True)
 
@@ -23,7 +25,9 @@
         azure_ad_token_provider=token_provider,
     )
     MODEL_NAME = os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"]
-
+elif API_HOST == "ollama":
+    client = openai.AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="none")
+    MODEL_NAME = "llama3.1:latest"
 
 agent = Agent(
     name="Spanish tutor",