Function calling

The ZGI API supports function calling as introduced by OpenAI. You can describe the functions and arguments to make available to the LLM model. The model will output a JSON object containing arguments to call one or many functions. After calling any invoked functions, you can provide those results back to the model in subsequent Chat Completions API calls.

Tool Choice

By default, the model will automatically select which functions to call. However, you can direct the model to use a function by supplying the tool_choice parameter. In the examples below, tool_choice=auto can be replaced with tool_choice={"type": "function", "function": {"name": "get_current_weather"}}.

Supported Models

mistralai/Mixtral-8x7B-Instruct-v0.1
mistralai/Mistral-7B-Instruct-v0.1
togethercomputer/CodeLlama-34b-Instruct

Simple Example

import os
import json
import openai

client = openai.OpenAI(
    base_url="https://api.zgiai.com/v1", 
    api_key=os.environ['Own_API_KEY'],
)

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"
            ]
          }
        }
      }
    }
  }
]

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?"}
]
    
response = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

print(json.dumps(response.choices[0].message.model_dump()['tool_calls'], indent=2))

Simple Example Output

[
  {
    "id": "call_1p75qwks0etzfy1g6noxvsgs",
    "function": {
      "arguments": "{\"location\":\"New York, NY\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    },
    "type": "function"
  },
  {
    "id": "call_aqjfgn65d0c280fjd3pbzpc6",
    "function": {
      "arguments": "{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    },
    "type": "function"
  },
  {
    "id": "call_rsg8muko8hymb4brkycu3dm5",
    "function": {
      "arguments": "{\"location\":\"Chicago, IL\",\"unit\":\"fahrenheit\"}",
      "name": "get_current_weather"
    },
    "type": "function"
  }
]

Multi-turn Example

import os
import json
import openai

client = openai.OpenAI(
    base_url="https://api.zgiai.com/v1", 
    api_key=os.environ['TOGETHER_API_KEY'],
)

# Example function to make available to model
def get_current_weather(location, unit="fahrenheit"):
    """Get the weather for some location"""
    if "chicago" in location.lower():
        return json.dumps({"location": "Chicago", "temperature": "13", "unit": unit})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "55", "unit": unit})
    elif "new york" in location.lower():
        return json.dumps({"location": "New York", "temperature": "11", "unit": unit})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

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"
            ]
          }
        }
      }
    }
  }
]

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?"}
]
    
response = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

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

        if function_name == "get_current_weather":
            function_response = get_current_weather(
                location=function_args.get("location"),
                unit=function_args.get("unit"),
            )
            messages.append(
                {
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": function_response,
                }
            )

    function_enriched_response = client.chat.completions.create(
        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
        messages=messages,
    )
    print(json.dumps(function_enriched_response.choices[0].message.model_dump(), indent=2))

Multi-turn Example Output

{
  "content": "The current temperature in New York is 11 degrees Fahrenheit, in San Francisco it is 55 degrees Fahrenheit, and in Chicago it is 13 degrees Fahrenheit.",
  "role": "assistant"
}