Unleashing the Power of Function Calling with DeepSeek API

Large language models (LLMs) are rapidly evolving, and function calling is a game-changing feature that significantly enhances their capabilities. With DeepSeek API, function calling allows the model to interact with external tools and services, making it more versatile and practical for real-world applications. This article delves into the concept of function calling within the DeepSeek API, explores its benefits, and provides a practical example to get you started.

What is Function Calling?

Function calling is a feature that enables an LLM to dynamically request information from and utilize external tools. Instead of merely generating text, the model can now trigger specific actions based on the user's input. In essence, the model identifies when it needs external data or functionality and formulates a request to use a specific tool or function.

Benefits of Function Calling

• Enhanced Capabilities: By integrating with external tools, the model can perform tasks beyond its built-in knowledge, such as retrieving real-time data, interacting with APIs, and automating workflows.
• Improved Accuracy: Function calling can provide the model with access to up-to-date, reliable information, thus reducing hallucinations and producing more accurate responses.
• Greater Flexibility: Integrate a wider range of existing systems and services using the DeepSeek API.
• Automation: Automate complex tasks, such as scheduling appointments, sending emails, or generating reports, all through natural language interactions.

DeepSeek API: Function Calling in Action

The DeepSeek API offers a robust function calling feature, allowing developers to seamlessly integrate external tools into their applications.

Sample Code & Explanation

Let's examine a practical example of using function calling with the DeepSeek API to get the current weather information for a user's location. Note: This example requires you to have an OpenAI library installed.

from openai import OpenAI
def send_messages(messages):
    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=messages,
        tools=tools
    )
    return response.choices[0].message

client = OpenAI(
    api_key="<your api key>", # Replace with your actual API Key
    base_url="https://api.deepseek.com",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather of an location, the user shoud supply a location first",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["location"]
            },
        }
    },
]

messages = [{"role": "user", "content": "How's the weather in Hangzhou?"}]
message = send_messages(messages)
print(f"User>\t {messages[0]['content']}")
tool = message.tool_calls[0]
messages.append(message)
messages.append({"role": "tool", "tool_call_id": tool.id, "content": "24℃"}) # Tool returns the current temperature
message = send_messages(messages)
print(f"Model>\t {message.content}")

Explanation:

1. Define the Tool: The tools list defines available functions. In this case, it's get_weather, which requires a location parameter.
2. User Query: The user asks, "How's the weather in Hangzhou?".
3. Model Request: The DeepSeek model recognizes the need for weather information and returns a function call request: get_weather({location: 'Hangzhou'}).
4. Tool Execution (User-Provided): Crucially, the user (developer) is responsible for implementing the get_weather function, which fetches the weather data and returns it to the model. In this example, the function returns "24℃".
5. Model Response: The model receives the weather data and generates a natural language response: "The current temperature in Hangzhou is 24°C."

This showcases the power of function calling, where the LLM intelligently determines the need for external data and seamlessly integrates it to provide a comprehensive answer. Please refer to the DeepSeek API Chat Completion documentation for detailed information on the API format for Function Calling.

Important Considerations

• Tool Implementation: The developer must implement the actual functionality of the tools being called. The DeepSeek model only determines when and how to call the tools.
• API Keys: Remember to secure your API keys and follow best practices for authentication.
• Error Handling: Implement robust error handling to gracefully manage potential issues with tool execution or API responses.
• Instability Notice: Bear in mind the notice from DeepSeek: The current version of the deepseek-chat model's Function Calling capability is unstable, which may result in looped calls or empty responses. The DeepSeek team stated they are actively working on a fix, which is expected to be resolved in the next version.

Conclusion

Function calling is a significant advancement in LLM technology, and the DeepSeek API provides a powerful platform for leveraging this feature. By enabling models to interact with external tools, function calling unlocks new possibilities for building intelligent applications across various domains.