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Patterns where the model returns multiple tool calls in a single response, run together.

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Parallel function calling

In parallel function calling, the same function is called multiple times simultaneously with different parameters. This is more efficient than making sequential calls for similar operations. 
import json
from together import Together

client = Together()

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-7B-Instruct-Turbo",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant that can access external functions. The responses from these function calls will be appended to this dialogue. Please provide responses based on the information from these function calls.",
        },
        {
            "role": "user",
            "content": "What is the current temperature of New York, San Francisco and Chicago?",
        },
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_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"],
                        },
                    },
                },
            },
        }
    ],
)

print(
    json.dumps(
        response.choices[0].message.model_dump()["tool_calls"],
        indent=2,
    )
)
In response, the tool_calls key of the LLM’s response will look like this:
JSON
[
  {
    "index": 0,
    "id": "call_aisak3q1px3m2lzb41ay6rwf",
    "type": "function",
    "function": {
      "arguments": "{\"location\":\"New York, NY\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    }
  },
  {
    "index": 1,
    "id": "call_agrjihqjcb0r499vrclwrgdj",
    "type": "function",
    "function": {
      "arguments": "{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    }
  },
  {
    "index": 2,
    "id": "call_17s148ekr4hk8m5liicpwzkk",
    "type": "function",
    "function": {
      "arguments": "{\"location\":\"Chicago, IL\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    }
  }
]
The model returns three function calls. You can execute them programmatically to answer the user’s question.

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Parallel multiple function calling

This pattern combines parallel and multiple function calling: multiple different functions are available, and one user prompt triggers multiple different function calls simultaneously. The model chooses which functions to call AND calls them in parallel. 
import json
from together import Together

client = Together()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_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"],
                    },
                },
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_current_stock_price",
            "description": "Get the current stock price for a given stock symbol",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {
                        "type": "string",
                        "description": "The stock symbol, e.g. AAPL, GOOGL, TSLA",
                    },
                    "exchange": {
                        "type": "string",
                        "description": "The stock exchange (optional)",
                        "enum": ["NYSE", "NASDAQ", "LSE", "TSX"],
                    },
                },
                "required": ["symbol"],
            },
        },
    },
]

response = client.chat.completions.create(
    model="Qwen/Qwen2.5-7B-Instruct-Turbo",
    messages=[
        {
            "role": "user",
            "content": "What's the current price of Apple and Google stock? What is the weather in New York, San Francisco and Chicago?",
        },
    ],
    tools=tools,
)

print(
    json.dumps(
        response.choices[0].message.model_dump()["tool_calls"],
        indent=2,
    )
)
This produces five function calls: two for stock prices (Apple and Google) and three for weather (New York, San Francisco, and Chicago), all executed in parallel.
JSON
[
  {
    "id": "call_8b31727cf80f41099582a259",
    "type": "function",
    "function": {
      "name": "get_current_stock_price",
      "arguments": "{\"symbol\": \"AAPL\"}"
    },
    "index": null
  },
  {
    "id": "call_b54bcaadceec423d82f28611",
    "type": "function",
    "function": {
      "name": "get_current_stock_price",
      "arguments": "{\"symbol\": \"GOOGL\"}"
    },
    "index": null
  },
  {
    "id": "call_f1118a9601c644e1b78a4a8c",
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "arguments": "{\"location\": \"San Francisco, CA\"}"
    },
    "index": null
  },
  {
    "id": "call_95dc5028837e4d1e9b247388",
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "arguments": "{\"location\": \"New York, NY\"}"
    },
    "index": null
  },
  {
    "id": "call_1b8b58809d374f15a5a990d9",
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "arguments": "{\"location\": \"Chicago, IL\"}"
    },
    "index": null
  }
]