Tool Use

import json
import os
from openai import OpenAI

client = OpenAI(base_url="https://api.flex.ai/v1", api_key=os.environ["FLEXAI_API_KEY"])

def get_weather(city: str) -> dict:
    # In a real app this would hit a weather API.
    return {"city": city, "temperature_c": 21, "conditions": "clear"}

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

messages = [{"role": "user", "content": "What's the weather in Paris?"}]

# First turn: model decides to call the tool.
first = client.chat.completions.create(
    model="Llama-3.3-70B-Instruct-FP8",
    messages=messages,
    tools=tools,
)
tool_call = first.choices[0].message.tool_calls[0]
args = json.loads(tool_call.function.arguments)
result = get_weather(**args)

# Second turn: feed the tool result back in.
messages.extend([
    first.choices[0].message,
    {
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps(result),
    },
])

second = client.chat.completions.create(
    model="Llama-3.3-70B-Instruct-FP8",
    messages=messages,
    tools=tools,
)
print(second.choices[0].message.content)

import OpenAI from "openai";
const client = new OpenAI({
  baseURL: "https://api.flex.ai/v1",
  apiKey: process.env.FLEXAI_API_KEY,
});

const getWeather = (city: string) => ({ city, temperature_c: 21, conditions: "clear" });

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

const messages: any[] = [{ role: "user", content: "What's the weather in Paris?" }];

const first = await client.chat.completions.create({
  model: "Llama-3.3-70B-Instruct-FP8",
  messages,
  tools,
});

const toolCall = first.choices[0].message.tool_calls![0];
const args = JSON.parse(toolCall.function.arguments);
const result = getWeather(args.city);

messages.push(first.choices[0].message, {
  role: "tool",
  tool_call_id: toolCall.id,
  content: JSON.stringify(result),
});

const second = await client.chat.completions.create({
  model: "Llama-3.3-70B-Instruct-FP8",
  messages,
  tools,
});
console.log(second.choices[0].message.content);