# Function Calling - Documentation

> For the complete documentation index, see [llms.txt](/llms.txt). Markdown is available with `Accept: text/markdown` and `.md` URL variants.

Source: /docs/guides/llm-function-calling

# Function Calling

`Function Calling` empowers AI models to interact with external tools and APIs, enabling them to perform specific actions and access real-time information. This capability extends the functionality of AI models beyond simple text generation, allowing for more dynamic and practical applications.

##

[​](#supported-models)

Supported Models

The following models support `Function Calling`:

##

[​](#quick-start-guide)

Quick Start Guide

This guide demonstrates how to use Function Calling to retrieve current weather information for a user’s specified location. We will walk through a complete Python code example.
For the specific API format of Function Calling, please refer to the API reference [Create Chat Completion](/docs/api-reference/model-apis-llm-create-chat-completion).

###

[​](#1-initialize-the-client)

1. Initialize the Client

First, you need to initialize the client with your Novita API key.

```
from openai import OpenAI
import json

client = OpenAI(
base_url="https://api.novita.ai/openai",
# Get the Novita AI API Key from: https://novita.ai/settings/key-management.
api_key="",
)

model = "deepseek/deepseek_v3"
```

###

[​](#2-define-the-function-to-be-called)

2. Define the Function to Be Called

Next, define the Python function that the model can call. In this example, it’s a function to get weather information.

```
# Example function to simulate fetching weather data.
def get_weather(location):
"""Retrieves the current weather for a given location."""
print("Calling get_weather function with location: ", location)
# In a real application, you would call an external weather API here.
# This is a simplified example returning hardcoded data.
return json.dumps({"location": location, "temperature": "60 degrees Fahrenheit"})
```

###

[​](#3-construct-the-api-request-with-tools-and-user-message)

3. Construct the API Request with Tools and User Message

Now, create the API request to the Novita endpoint. This request includes the `tools` parameter, defining the functions the model can use, and the user’s message.

```
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": "What is the weather in San Francisco?"
}
]

# Let's send the request and print the response.
response = client.chat.completions.create(
model=model,
messages=messages,
tools=tools,
)

# Please check if the response contains tool calls if in production.
tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.model_dump())
```

Output:

```
{'id': '0', 'function': {'arguments': '{"location": "San Francisco, CA"}', 'name': 'get_weather'}, 'type': 'function'}
```

###

[​](#4-respond-with-the-function-call-result-and-get-the-final-answer)

4. Respond with the Function Call Result and Get the Final Answer

The next step is to process the function call, execute the `get_weather` function, and send the result back to the model to generate the final response to the user.

```
# Ensure tool_call is defined from the previous step
if tool_call:
# Extend conversation history with the assistant's tool call message
messages.append(response.choices[0].message)

function_name = tool_call.function.name
if function_name == "get_weather":
function_args = json.loads(tool_call.function.arguments)
# Execute the function and get the response
function_response = get_weather(
location=function_args.get("location"))
# Append the function response to the messages
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"content": function_response,
}
)

# Get the final response from the model, now with the function result
answer_response = client.chat.completions.create(
model=model,
messages=messages,
# Note: Do not include tools parameter here.
)
print(answer_response.choices[0].message)
```

Output:

```
ChatCompletionMessage(content="The weather in San Francisco, CA is currently **60 degrees Fahrenheit**. For more detailed information, such as specific conditions (e.g., sunny, cloudy, rainy), you might want to check a local weather app or website. Let me know if you'd like help with anything else!", refusal=None, role='assistant', function_call=None, tool_calls=None)
```

###

[​](#the-complete-code)

The Complete Code

```
from openai import OpenAI
import json

client = OpenAI(
base_url="https://api.novita.ai/openai",
# Get the Novita AI API Key from: https://novita.ai/settings/key-management.
api_key="",
)

model = "deepseek/deepseek_v3"

# Example function to simulate fetching weather data.
def get_weather(location):
"""Retrieves the current weather for a given location."""
print("Calling get_weather function with location: ", location)
# In a real application, you would call an external weather API here.
# This is a simplified example returning hardcoded data.
return json.dumps({"location": location, "temperature": "60 degrees Fahrenheit"})

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": "What is the weather in San Francisco?"
}
]

# Let's send the request and print the response.
response = client.chat.completions.create(
model=model,
messages=messages,
tools=tools,
)

# Please check if the response contains tool calls if in production.
tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.model_dump())

# Ensure tool_call is defined from the previous step
if tool_call:
# Extend conversation history with the assistant's tool call message
messages.append(response.choices[0].message)

function_name = tool_call.function.name
if function_name == "get_weather":
function_args = json.loads(tool_call.function.arguments)
# Execute the function and get the response
function_response = get_weather(
location=function_args.get("location"))
# Append the function response to the messages
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"content": function_response,
}
)

# Get the final response from the model, now with the function result
answer_response = client.chat.completions.create(
model=model,
messages=messages,
# Note: Do not include tools parameter here
)
print(answer_response.choices[0].message)
```

Last modified on August 12, 2025