​
1. Use Cases

The Function Calling feature enables models to invoke external tools, thereby enhancing their capabilities.

This feature allows models to act as a “brain” that utilizes external tools (e.g., searching for external knowledge, checking itineraries, or accessing specialized domain tools) to effectively address issues such as hallucinations and knowledge recency.

​
2. How to Use

​
2.1 Add tools as a request parameter via REST API

Include the following in the request body:

"tools": [
    {
        'type': 'function',
        'function': {
            'name': 'function_name',
            'description': 'function description',
            'parameters': {
                '_comments': 'function parameters'
            },
        }
    },
    {
        '_comments': 'other function descriptions'
    }
]

for example, payload:

payload = {
    "model": "deepseek-ai/DeepSeek-V2.5",
    "messages": [
        {
            "role": "user",
            "content": "How is the weather in Tokyo?"
        }
    ],
    "tools": [
    {
        'type': 'function',
        'function': {
            'name': 'function name',
            'description': 'function description',
            'parameters': {
                '_comments': 'function parameters'
            },
        }
    },
    {
        '_comments': 'other function descriptions'
    }
    ]
    '_comments': 'other function descriptions'
}

​
2.2 Requesting via the OpenAI Library

This feature is compatible with OpenAI. When using the OpenAI library, add the parameter tools=[corresponding tools] in the request. For example:

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V2.5",
    messages = messages,
    tools=[
        {
            'type': 'function',
            'function': {
                'name': 'function name',
                'description': 'function description',
                'parameters': {
                    // function parameters
                },
            }
        },
        {
            // other function descriptions
        }
    ]
    // chat.completions other parameters
)

​
3. Supported Models

The currently supported models are:

  • Deepseek Series:

    • deepseek-ai/DeepSeek-V2.5

  • Qwen Series：

    • Qwen/Qwen2.5-72B-Instruct
    • Qwen/Qwen2.5-7B-Instruct

​
4. Examples

​
4.1. Example 1: Extending numerical computation capabilities

This code demonstrates the use of four functions: addition, subtraction, comparison of values, and counting repeated letters in a string. The example illustrates how function calling can address execution challenges in areas where large language models are less proficient, such as numerical operations within token predictions.


from openai import OpenAI

client = OpenAI(
    api_key="Your API KEY", # get from https://dash.horay.ai/account/ak
    base_url="https://api.horay.ai/v1"
)

def add(a: float, b: float):
    return a + b

def mul(a: float, b: float):
    return a * b

def compare(a: float, b: float):
    if a > b:
        return f'{a} is greater than {b}'
    elif a < b:
        return f'{b} is greater than {a}'
    else:
        return f'{a} is equal to {b}'

def count_letter_in_string(a: str, b: str):
    string = a.lower()
    letter = b.lower()
    
    count = string.count(letter)
    return(f"The letter '{letter}' appears {count} times in the string.")


tools = [
{
    'type': 'function',
    'function': {
        'name': 'add',
        'description': 'Compute the sum of two numbers',
        'parameters': {
            'type': 'object',
            'properties': {
                'a': {
                    'type': 'int',
                    'description': 'A number',
                },
                'b': {
                    'type': 'int',
                    'description': 'A number',
                },
            },
            'required': ['a', 'b'],
        },
    }
}, 
{
    'type': 'function',
    'function': {
        'name': 'mul',
        'description': 'Calculate the product of two numbers',
        'parameters': {
            'type': 'object',
            'properties': {
                'a': {
                    'type': 'int',
                    'description': 'A number',
                },
                'b': {
                    'type': 'int',
                    'description': 'A number',
                },
            },
            'required': ['a', 'b'],
        },
    }
},
{
    'type': 'function',
    'function': {
        'name': 'count_letter_in_string',
        'description': 'Count letter number in a string',
        'parameters': {
            'type': 'object',
            'properties': {
                'a': {
                    'type': 'str',
                    'description': 'source string',
                },
                'b': {
                    'type': 'str',
                    'description': 'letter',
                },
            },
            'required': ['a', 'b'],
        },
    }
},
{
    'type': 'function',
    'function': {
        'name': 'compare',
        'description': 'Compare two number, which one is bigger',
        'parameters': {
            'type': 'object',
            'properties': {
                'a': {
                    'type': 'float',
                    'description': 'A number',
                },
                'b': {
                    'type': 'float',
                    'description': 'A number',
                },
            },
            'required': ['a', 'b'],
        },
    }
}
]

def function_call_playground(prompt):
    messages = [{'role': 'user', 'content': prompt}]
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V2.5",
        messages = messages,
        temperature=0.01,
        top_p=0.95,
        stream=False,
        tools=tools)

    # print(response)
    func1_name = response.choices[0].message.tool_calls[0].function.name
    func1_args = response.choices[0].message.tool_calls[0].function.arguments
    func1_out = eval(f'{func1_name}(**{func1_args})')
    # print(func1_out)

    messages.append(response.choices[0].message)
    messages.append({
        'role': 'tool',
        'content': f'{func1_out}',
        'tool_call_id': response.choices[0].message.tool_calls[0].id
    })
    # print(messages)
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V2.5",
        messages=messages,
        temperature=0.01,
        top_p=0.95,
        stream=False,
        tools=tools)
    return response.choices[0].message.content
  
prompts = [
    "How many “r”s are in “strawberry”?", 
    "Which is smaller, 9.11 or 9.9?"
]

for prompt in prompts:
    print(function_call_playground(prompt))

Output might look like:

There are 3 “r”s in “strawberry.”
9.11 is smaller than 9.9.

​
4.2. Example： Extending a large language model’s understanding of the external environment

This code uses a single function to query external information via an external API.

import requests
from openai import OpenAI

client = OpenAI(
    api_key="Your API KEY", # get from https://dash.horay.ai/account/ak
    base_url="https://api.horay.ai/v1"
)

# Use the WeatherAPI’s API to fetch weather information.
def get_weather(city: str):
    api_key = "your WeatherAPI APIKEY"  # your WeatherAPI APIKEY
    base_url = "http://api.weatherapi.com/v1/current.json"
    params = {
        'key': api_key,
        'q': city,
        'aqi': 'no' 
    }
    
    response = requests.get(base_url, params=params)
    
    if response.status_code == 200:
        data = response.json()
        weather = data['current']['condition']['text']
        temperature = data['current']['temp_c']
        return f"The weather in {city} is {weather} with a temperature of {temperature}°C."
    else:
        return f"Could not retrieve weather information for {city}."

# Define function calling tools
tools = [
    {
        'type': 'function',
        'function': {
            'name': 'get_weather',
            'description': 'Get the current weather for a given city.',
            'parameters': {
                'type': 'object',
                'properties': {
                    'city': {
                        'type': 'string',
                        'description': 'The name of the city to query weather for.',
                    },
                },
                'required': ['city'],
            },
        }
    }
]

# send request and handle function calling
def function_call_playground(prompt):
    messages = [{'role': 'user', 'content': prompt}]
    
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V2.5",
        messages=messages,
        temperature=0.01,
        top_p=0.95,
        stream=False,
        tools=tools
    )

    func1_name = response.choices[0].message.tool_calls[0].function.name
    func1_args = response.choices[0].message.tool_calls[0].function.arguments
    func1_out = eval(f'{func1_name}(**{func1_args})')

    messages.append(response.choices[0].message)
    messages.append({
        'role': 'tool',
        'content': f'{func1_out}',
        'tool_call_id': response.choices[0].message.tool_calls[0].id
    })
    
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V2.5",
        messages=messages,
        temperature=0.01,
        top_p=0.95,
        stream=False,
        tools=tools
    )
    
    return response.choices[0].message.content

prompt = "how is the weather today in New York?"
print(function_call_playground(prompt))

Output might look like:

The weather in New York today is sunny with a temperature of 21.4°C.
Fill In the Middle