DeepSeek Function Calling in Action: Building an AI Weather Assistant

What Problem Does This Tutorial Solve?

You will build a complete AI weather assistant from scratch that can:

• Understand natural language queries from users
• Automatically invoke weather APIs to fetch real-time data
• Generate friendly responses based on the data

This tutorial is your first step toward understanding AI Agents. Function Calling is the cornerstone of AI application development.

What Is Function Calling?

Function Calling enables AI to use external tools. Here is the workflow:

User: "What's the weather like in Beijing today?"
        ↓
AI determines: I need to call the get_weather function
        ↓
AI returns function call request: {name: "get_weather", args: {city: "Beijing"}}
        ↓
Your code executes the function → retrieves weather data
        ↓
Return the result to the AI
        ↓
AI: "Beijing is sunny today, 22°C, great for outdoor activities."

Step 1: Define the Tool

import os, json
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("DEEPSEEK_API_KEY"),
    base_url="https://api.deepseek.com/v1",
)

# Define the get_weather tool
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Query the weather information for a specified city on the current day",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "City name, in either Chinese or English"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "Temperature unit"
                }
            },
            "required": ["city"]
        }
    }
}]

Step 2: Mock the Weather Query Function

def get_weather(city: str, unit: str = "celsius") -> dict:
    """Mock weather query (use a real API in production)"""
    weather_data = {
        "Beijing": {"temp": 22, "condition": "Sunny", "humidity": 45},
        "Shanghai": {"temp": 28, "condition": "Cloudy", "humidity": 65},
        "Tokyo": {"temp": 25, "condition": "Light Rain", "humidity": 70},
        "Hangzhou": {"temp": 30, "condition": "Sunny", "humidity": 55},
    }

    data = weather_data.get(city, {"temp": 20, "condition": "Unknown", "humidity": 50})
    if unit == "fahrenheit":
        data["temp"] = data["temp"] * 9 / 5 + 32

    data["city"] = city
    data["unit"] = unit
    return data

Step 3: Build the Complete Assistant

def chat_with_weather_assistant(user_query: str) -> str:
    messages = [{"role": "user", "content": user_query}]

    # First call: AI decides whether a tool is needed
    response = client.chat.completions.create(
        model="deepseek-v4-pro",
        messages=messages,
        tools=tools,
        tool_choice="auto",
        extra_body={"thinking_mode": "thinking"},
    )

    msg = response.choices[0].message

    # If the AI does not need a tool, return the content directly
    if not msg.tool_calls:
        return msg.content

    # Process each tool call
    for tool_call in msg.tool_calls:
        func_name = tool_call.function.name
        func_args = json.loads(tool_call.function.arguments)

        print(f"Tool invoked: {func_name}")
        print(f"Arguments: {func_args}")

        # Execute the function
        if func_name == "get_weather":
            result = get_weather(**func_args)

        # Return the execution result to the AI
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result, ensure_ascii=False)
        })

    # Second call: AI generates the final response based on tool results
    final_response = client.chat.completions.create(
        model="deepseek-v4-pro",
        messages=messages,
    )

    return final_response.choices[0].message.content


# Test
print(chat_with_weather_assistant("What's the weather like in Beijing today?"))
print(chat_with_weather_assistant("Which city is hotter, Shanghai or Hangzhou?"))

Full Workflow Walkthrough

Input: “Which city is hotter, Shanghai or Hangzhou?”

1st API call:
  → AI returns: need to call get_weather("Shanghai") and get_weather("Hangzhou")

Code executes get_weather → retrieves weather data for both cities

2nd API call (with weather data):
  → AI returns: "Hangzhou (30°C) is 2 degrees hotter than Shanghai (28°C).
             Shanghai has higher humidity (65% vs 55%), so it may feel similar.
             Neither city has rain today — great for going out."

Advanced: Defining Multiple Tools

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Query weather information",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"}
                },
                "required": ["city"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_news",
            "description": "Query the latest news",
            "parameters": {
                "type": "object",
                "properties": {
                    "topic": {"type": "string"},
                    "count": {"type": "integer", "default": 3}
                },
                "required": ["topic"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "send_email",
            "description": "Send an email",
            "parameters": {
                "type": "object",
                "properties": {
                    "to": {"type": "string"},
                    "subject": {"type": "string"},
                    "body": {"type": "string"}
                },
                "required": ["to", "subject", "body"]
            }
        }
    }
]

Frequently Asked Questions (FAQ)

Q: When should I use Function Calling?

A: Use it when the AI needs access to real-time data (weather, stocks, news), needs to perform actions (send emails, create files), or needs to invoke your business logic.

Q: How detailed should tool definitions be?

A: The more detailed, the better. The description field for each parameter directly affects whether the AI can invoke the function correctly. Use natural language to describe what each parameter should be.

Next Steps

• DeepSeek API Getting Started Guide
• China AI Model Ultimate Comparison

Tutorial version note: Based on DeepSeek V4 API, verified working as of June 2026.