Large Language Models (LLMs) are revolutionizing the way we interact with technology. One of the most exciting advancements is the ability for these models to not just understand and generate text, but also to interact with external tools. This capability, known as Function Calling, dramatically expands the possibilities of what LLMs can achieve. DeepSeek API offers robust Function Calling capabilities, allowing developers to build intelligent applications that can perform real-world tasks. Let's delve into how it works.
Function Calling empowers LLMs, like DeepSeek's models, to call external tools or functions to enhance their core abilities. Imagine a chatbot that can not only answer questions but also:
Function Calling enables a more dynamic and interactive user experience. Instead of being limited to pre-programmed responses, the LLM can actively gather information and take action based on user input using external tools.
DeepSeek API stands out as a platform that allows easy implementation of Function Calling into various projects. The core advantage lies in its capacity to let the LLM determine when and how to use specific functions to fulfill user requests.
Let's break down the example provided in the DeepSeek API documentation:
User Query: The user asks, "How's the weather in Hangzhou?"
Model Analysis: The DeepSeek model analyzes the query and recognizes the need for external information. It determines that the get_weather function is relevant.
Function Call: The model returns a request to call the get_weather({location: 'Hangzhou'}) function. This request includes the function name and any necessary parameters (in this case, the location).
External Tool Execution: This is where the developer comes in. You, the developer, are responsible for implementing the backend logic of the get_weather function. This function would typically:
Model Response: The DeepSeek model receives the weather data (e.g., "24°C"). Using this information, it generates a natural language response: "The current temperature in Hangzhou is 24°C."
The following Python example demonstrates how to set up function calling using the OpenAI library, adapted for DeepSeek API:
from openai import OpenAI
client = OpenAI(
api_key="<your 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"]
},
}
},
]
def send_messages(messages):
response = client.chat.completions.create(
model="deepseek-chat",
messages=messages,
tools=tools
)
return response.choices[0].message
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] # Access the tool call object from the message
messages.append(message)
# Simulate call the external get_weather tools
weather_info = "24°C"
messages.append({"role": "tool", "tool_call_id": tool.id, "content": weather_info})
message = send_messages(messages)
print(f"Model>\t {message.content}")
Key takeaways from the code:
tools definition: This defines the functions that the DeepSeek model can call. It includes the function name (get_weather), a description, and the expected parameters (in this example, location).send_messages function: This function sends the messages to the DeepSeek API and receives the model's response.get_weather function: The code doesn't include the actual implementation of get_weather. You would need to write this function yourself to retrieve weather data from an external source.Function Calling unlocks a wide range of potential applications:
The DeepSeek API documentation notes that the Function Calling capability of the deepseek-chat model is currently unstable. This might manifest as:
While these issues are being addressed, developers should be aware of these limitations and implement error handling and retry mechanisms in their code. Keep an eye on the DeepSeek API Change Log for updates on the stability improvements.
Function Calling is a game-changing feature that enhances the power and versatility of LLMs like DeepSeek's models. Although there might currently be some instability, understanding how to implement and utilize function calling effectively opens up a world of possibilities for building intelligent and interactive applications. By carefully designing functions, handling errors, and prioritizing security, you can leverage DeepSeek API to create innovative solutions that solve real-world problems. Be sure to reference the official DeepSeek API Documentation for the most up-to-date information and guidelines. You can also explore other features like JSON Output to enhance your projects.
