Tool calling

Tool calling (function calling) lets a model decide to invoke a function you define and return structured arguments for it. Omnia supports the standard OpenAI tools interface through /v1/chat/completions, so any OpenAI SDK works unchanged.

Defining tools

A tool is an object with type: "function" and a function describing its name, description, and parameters (a JSON Schema for the arguments).

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"},
                },
                "required": ["city"],
            },
        },
    }
]

resp = client.chat.completions.create(
    model="Qwen/Qwen3-32B",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto",
)

tool_calls = resp.choices[0].message.tool_calls

const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "Get the current weather for a city.",
      parameters: {
        type: "object",
        properties: { city: { type: "string", description: "City name" } },
        required: ["city"],
      },
    },
  },
];

const resp = await client.chat.completions.create({
  model: "Qwen/Qwen3-32B",
  messages: [{ role: "user", content: "What's the weather in Paris?" }],
  tools,
  tool_choice: "auto",
});

curl https://gateway.omnia-voice.com/v1/chat/completions \
  -H "Authorization: Bearer $OMNIA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-32B",
    "messages": [{ "role": "user", "content": "What'\''s the weather in Paris?" }],
    "tool_choice": "auto",
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city.",
        "parameters": {
          "type": "object",
          "properties": { "city": { "type": "string", "description": "City name" } },
          "required": ["city"]
        }
      }
    }]
  }'

A full round trip

1. The model asks to call a tool

When the model decides to call a tool, finish_reason is tool_calls and the assistant message carries a tool_calls array. Each call has an id, the function name, and JSON-encoded arguments:

{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "call_abc123",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"city\": \"Paris\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

2. You run the function and send the result back

Read the tool call

Parse message.tool_calls[i].function.name and .arguments (a JSON string).

Run your function

Execute the function with those arguments in your own code.

Send the result back

Append the assistant’s tool-call message, then a role: "tool" message whose tool_call_id matches the call’s id and whose content is the result. Call the model again to get the final answer.

import json

tool_call = resp.choices[0].message.tool_calls[0]
args = json.loads(tool_call.function.arguments)  # {"city": "Paris"}
result = get_weather(**args)                      # your own function

messages.append(resp.choices[0].message)          # the assistant's tool-call
messages.append({
    "role": "tool",
    "tool_call_id": tool_call.id,
    "content": json.dumps(result),                # {"temp_c": 18, "condition": "cloudy"}
})

final = client.chat.completions.create(
    model="Qwen/Qwen3-32B",
    messages=messages,
)
print(final.choices[0].message.content)
# "It's currently 18°C and cloudy in Paris."

`tool_choice`

Value	Behaviour
`"auto"`	The model decides whether to call a tool.
`"required"`	The model must call a tool.
`"none"`	The model won’t call any tool.
`{ "type": "function", "function": { "name": "..." } }`	Force a specific tool.

Parallel tool calls

Set parallel_tool_calls: true to let the model request several tool calls in a single turn (the tool_calls array will have multiple entries). Execute each one, then append a matching role: "tool" message per tool_call_id before calling the model again.

Observability captures tool names only, never argument values. Omnia’s telemetry records which tools were called for metrics, but it does not store the arguments you pass or the results you send back.

Each round-trip (the tool-call request and the follow-up with the result) is a separate, independently-metered request, because Omnia is stateless and you resend the growing message history each time. Keep tool descriptions and schemas tight to minimize prompt tokens.

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Platform

Tool calling

Defining tools

A full round trip

1. The model asks to call a tool

2. You run the function and send the result back

`tool_choice`

Parallel tool calls

​Defining tools

​A full round trip

​1. The model asks to call a tool

​2. You run the function and send the result back

​tool_choice

​Parallel tool calls

Defining tools

A full round trip

1. The model asks to call a tool

2. You run the function and send the result back

`tool_choice`

Parallel tool calls