Chat Completions: Tools & Signatures

Function Calling

Function calling allows models to invoke defined functions during conversations, enabling them to perform specific actions or retrieve external information.

Basic Usage

Define functions that the model can call:

from openai import OpenAI
import json

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="{GATEWAY_BASE_URL}"
)

# Define a function
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"]
                }
            },
            "required": ["location"]
        }
    }
}]

# Make the request
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather in New York?"}],
    tools=tools
)

# Check if the model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    function_name = tool_call.function.name
    function_args = json.loads(tool_call.function.arguments)

    print(f"Function called: {function_name}")
    print(f"Arguments: {function_args}")

Function Definition Reference

Creating Well-Structured Function Definitions

When defining functions, you need to provide:

name: The function name
description: What the function does
parameters: JSON Schema object describing the parameters

function_schema = {
    "name": "get_weather",
    "description": "Get current weather information",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City name"
            }
        },
        "required": ["location"]
    }
}

Supported Parameter Types for Function Arguments

Functions support various parameter types:

function_schema = {
    "name": "process_data",
    "description": "Process data with various parameters",
    "parameters": {
        "type": "object",
        "properties": {
            "text": {
                "type": "string",
                "description": "Text to process"
            },
            "count": {
                "type": "integer",
                "description": "Number of items"
            },
            "confidence": {
                "type": "number",
                "description": "Threshold (0.0 to 1.0)"
            },
            "enabled": {
                "type": "boolean",
                "description": "Whether processing is enabled"
            },
            "categories": {
                "type": "array",
                "items": {"type": "string"},
                "description": "List of categories"
            }
        },
        "required": ["text"]
    }
}

Implementation Workflows

Working with Multiple Function Definitions

Define multiple functions for the model to choose from:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather information",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "max_results": {"type": "integer", "default": 5}
                },
                "required": ["query"]
            }
        }
    }
]

Processing and Responding to Function Calls

Process function calls and continue the conversation:

# Initial request
messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=messages,
    tools=tools
)

# Handle function call
if response.choices[0].message.tool_calls:
    messages.append(response.choices[0].message)

    for tool_call in response.choices[0].message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        # Execute your function (simulated here)
        if function_name == "get_weather":
            result = f"The weather in {function_args['location']} is 22°C and sunny"

        # Add the function result to the conversation
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": result
        })

    # Continue the conversation
    final_response = client.chat.completions.create(
        model="openai-main/gpt-4o-mini",
        messages=messages
    )

    print(final_response.choices[0].message.content)

Controlling When and How Functions Are Called

Control when and how functions are called:

# Force a specific function call
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "get_weather"}}
)

# Allow automatic function calling (default)
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="auto"
)

# Prevent function calling
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="none"
)

# Force any function call
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="required"
)

Thought Signatures

Thought signatures are encrypted representations of a model’s internal reasoning process that help maintain context and coherence across multi-turn interactions, particularly during function calling. When using certain Gemini 3 preview models, the API includes a thought_signature field in tool call responses.

from openai import OpenAI
import json

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="{GATEWAY_BASE_URL}"
)

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get weather for a location",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"]
        }
    }
}]

# First call - model requests tool
response = client.chat.completions.create(
    model="vertex-main/gemini-3-pro-preview",
    messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
    tools=tools
)

message = response.choices[0].message

if message.tool_calls:
    tool_call = message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    result = f"The weather in {args['location']} is 18°C and cloudy."

    # Convert message to dict (preserves thought_signature)
    assistant_message = message.model_dump(exclude_none=True)

    # Second call - send tool result back
    final_response = client.chat.completions.create(
        model="vertex-main/gemini-3-pro-preview",
        messages=[
            {"role": "user", "content": "What's the weather in San Francisco?"},
            assistant_message,  # Includes thought_signature
            {
                "role": "tool",
                "content": json.dumps(result),
                "tool_call_id": tool_call.id
            }
        ]
    )

    print(final_response.choices[0].message.content)

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Chat Completions: Tools & Signatures

Function Calling

Basic Usage

Function Definition Reference

Implementation Workflows

Thought Signatures

​Function Calling

​Basic Usage

​Function Definition Reference

​Implementation Workflows

​Thought Signatures

Function Calling

Basic Usage

Function Definition Reference

Implementation Workflows

Thought Signatures