Tool Calling

const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "Get current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City and state" },
          unit: { type: "string", enum: ["celsius", "fahrenheit"] },
        },
        required: ["location"],
      },
    },
  },
];

const response = await openai.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "What's the weather in NYC?" }],
  tools,
  tool_choice: "auto",
});

// Handle tool calls
if (response.choices[0].message.tool_calls) {
  const toolCall = response.choices[0].message.tool_calls[0];
  const args = JSON.parse(toolCall.function.arguments);

  // Execute function
  const result = await getWeather(args.location, args.unit);

  // Continue conversation with result
  const finalResponse = await openai.chat.completions.create({
    model: "openai/gpt-4o",
    messages: [
      { role: "user", content: "What's the weather in NYC?" },
      response.choices[0].message,
      {
        role: "tool",
        tool_call_id: toolCall.id,
        content: JSON.stringify(result),
      },
    ],
  });
}

curl -X POST https://api.orq.ai/v2/proxy/chat/completions \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "What is the weather like in San Francisco?"
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
              },
              "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "The temperature unit"
              }
            },
            "required": ["location"]
          }
        }
      }
    ],
    "tool_choice": "auto"
  }'

from openai import OpenAI
import json
import os

openai = OpenAI(
  api_key=os.environ.get("ORQ_API_KEY"),
  base_url="https://api.orq.ai/v2/proxy"
)

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

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

# First API call - model decides to use the tool
response = openai.chat.completions.create(
    model="openai/gpt-4o",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

# Check if the model wants to use a tool
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]

    # Parse the arguments
    arguments = json.loads(tool_call.function.arguments)

    # Execute your function (mock response here)
    weather_result = {
        "temperature": 72,
        "unit": "fahrenheit",
        "description": "Sunny with light clouds"
    }

    # Add the assistant's message with tool call to history
    messages.append(response.choices[0].message)

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

    # Second API call - get final response with tool result
    final_response = openai.chat.completions.create(
        model="openai/gpt-4o",
        messages=messages
    )

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

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v2/proxy",
});

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

const messages = [
  { role: "user" as const, content: "What's the weather like in San Francisco?" },
];

// First API call - model decides to use the tool
const response = await openai.chat.completions.create({
  model: "openai/gpt-4o",
  messages,
  tools,
  tool_choice: "auto",
});

// Check if the model wants to use a tool
if (response.choices[0].message.tool_calls) {
  const toolCall = response.choices[0].message.tool_calls[0];

  // Parse the arguments
  const arguments = JSON.parse(toolCall.function.arguments);

  // Execute your function (mock response here)
  const weatherResult = {
    temperature: 72,
    unit: "fahrenheit",
    description: "Sunny with light clouds",
  };

  // Add the assistant's message with tool call to history
  messages.push(response.choices[0].message);

  // Add the tool result to messages
  messages.push({
    role: "tool" as const,
    tool_call_id: toolCall.id,
    content: JSON.stringify(weatherResult),
  });

  // Second API call - get final response with tool result
  const finalResponse = await openai.chat.completions.create({
    model: "openai/gpt-4o",
    messages,
  });

  console.log(finalResponse.choices[0].message.content);
}

class ToolHandler {
  constructor() {
    this.tools = new Map();
  }

  register(name, func, schema) {
    this.tools.set(name, { func, schema });
  }

  async execute(toolCall) {
    const tool = this.tools.get(toolCall.function.name);
    if (!tool) throw new Error(`Unknown tool: ${toolCall.function.name}`);

    const args = JSON.parse(toolCall.function.arguments);
    const result = await tool.func(args);

    return {
      role: "tool",
      tool_call_id: toolCall.id,
      content: JSON.stringify(result),
    };
  }
}

// Usage
const handler = new ToolHandler();
handler.register("get_weather", getWeatherAPI, weatherSchema);
handler.register("search_web", searchWebAPI, searchSchema);

// Execute tool calls
const toolResults = await Promise.all(
  response.choices[0].message.tool_calls.map((call) => handler.execute(call)),
);

import asyncio
import json

async def execute_tools_parallel(tool_calls):
    async def execute_single_tool(tool_call):
        function_name = tool_call.function.name
        arguments = json.loads(tool_call.function.arguments)

        # Route to appropriate function
        if function_name == "get_weather":
            result = await get_weather_async(arguments)
        elif function_name == "search_products":
            result = await search_products_async(arguments)
        elif function_name == "check_inventory":
            result = await check_inventory_async(arguments)
        else:
            result = {"error": f"Unknown function: {function_name}"}

        return {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        }

    # Execute all tools concurrently
    results = await asyncio.gather(
        *[execute_single_tool(call) for call in tool_calls]
    )

    return results

# Usage
if response.choices[0].message.tool_calls:
    tool_results = await execute_tools_parallel(
        response.choices[0].message.tool_calls
    )

    # Add to conversation
    messages.extend(tool_results)

tools = [
    {
        "type": "function",
        "function": {
            "name": "query_database",
            "description": "Query the customer database",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "SQL query to execute"
                    },
                    "limit": {
                        "type": "integer",
                        "description": "Maximum number of results"
                    }
                },
                "required": ["query"]
            }
        }
    }
]

async def query_database(args):
    # Sanitize and execute SQL safely
    query = sanitize_sql(args["query"])
    limit = args.get("limit", 10)

    results = await db.execute(f"{query} LIMIT {limit}")
    return {"results": results.fetchall()}

const apiTools = [
  {
    type: "function",
    function: {
      name: "send_email",
      description: "Send an email to a recipient",
      parameters: {
        type: "object",
        properties: {
          to: { type: "string", description: "Email address" },
          subject: { type: "string", description: "Email subject" },
          body: { type: "string", description: "Email content" },
        },
        required: ["to", "subject", "body"],
      },
    },
  },
  {
    type: "function",
    function: {
      name: "create_calendar_event",
      description: "Create a calendar event",
      parameters: {
        type: "object",
        properties: {
          title: { type: "string" },
          start_time: { type: "string", format: "date-time" },
          duration: { type: "integer", description: "Duration in minutes" },
          attendees: { type: "array", items: { type: "string" } },
        },
        required: ["title", "start_time"],
      },
    },
  },
];

const executeApiTool = async (toolCall) => {
  const { name } = toolCall.function;
  const args = JSON.parse(toolCall.function.arguments);

  switch (name) {
    case "send_email":
      return await emailAPI.send(args);
    case "create_calendar_event":
      return await calendarAPI.createEvent(args);
    default:
      throw new Error(`Unknown API tool: ${name}`);
  }
};

class WorkflowEngine:
    def __init__(self):
        self.tools = {}
        self.conversation = []

    def register_tool(self, name, func, schema):
        self.tools[name] = {"func": func, "schema": schema}

    async def execute_workflow(self, initial_prompt, max_steps=10):
        self.conversation = [{"role": "user", "content": initial_prompt}]

        for step in range(max_steps):
            response = await openai.chat.completions.create(
                model="openai/gpt-4o",
                messages=self.conversation,
                tools=list(self.tools.values()),
                tool_choice="auto"
            )

            self.conversation.append(response.choices[0].message)

            # Check if tools need to be executed
            if response.choices[0].message.tool_calls:
                for tool_call in response.choices[0].message.tool_calls:
                    result = await self.execute_tool(tool_call)
                    self.conversation.append(result)
            else:
                # No tools called, workflow complete
                return response.choices[0].message.content

        return "Workflow exceeded maximum steps"

    async def execute_tool(self, tool_call):
        tool_name = tool_call.function.name
        arguments = json.loads(tool_call.function.arguments)

        if tool_name in self.tools:
            result = await self.tools[tool_name]["func"](arguments)
        else:
            result = {"error": f"Unknown tool: {tool_name}"}

        return {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        }

const safeToolExecution = async (toolCall) => {
  try {
    const args = JSON.parse(toolCall.function.arguments);

    // Validate arguments
    const validation = validateArgs(toolCall.function.name, args);
    if (!validation.valid) {
      return {
        role: "tool",
        tool_call_id: toolCall.id,
        content: JSON.stringify({
          error: "Invalid arguments",
          details: validation.errors,
        }),
      };
    }

    // Execute with timeout
    const result = await Promise.race([
      executeFunction(toolCall.function.name, args),
      new Promise((_, reject) =>
        setTimeout(() => reject(new Error("Tool execution timeout")), 30000),
      ),
    ]);

    return {
      role: "tool",
      tool_call_id: toolCall.id,
      content: JSON.stringify(result),
    };
  } catch (error) {
    console.error(`Tool execution failed: ${error.message}`);

    return {
      role: "tool",
      tool_call_id: toolCall.id,
      content: JSON.stringify({
        error: "Tool execution failed",
        message: error.message,
      }),
    };
  }
};

{
  "type": "object",
  "properties": {
    "location": {
      "type": "string",
      "description": "City and state (e.g., 'San Francisco, CA')",
      "examples": ["New York, NY", "London, UK"]
    },
    "units": {
      "type": "string",
      "enum": ["metric", "imperial"],
      "description": "Temperature unit system",
      "default": "metric"
    }
  },
  "required": ["location"]
}

class CachedToolExecutor {
  constructor() {
    this.cache = new Map();
    this.cacheTTL = 300000; // 5 minutes
  }

  getCacheKey(toolCall) {
    return `${toolCall.function.name}:${toolCall.function.arguments}`;
  }

  async execute(toolCall) {
    const key = this.getCacheKey(toolCall);
    const cached = this.cache.get(key);

    if (cached && Date.now() - cached.timestamp < this.cacheTTL) {
      return cached.result;
    }

    const result = await this.executeFunction(toolCall);
    this.cache.set(key, { result, timestamp: Date.now() });

    return result;
  }
}

# Instead of multiple individual calls
def get_weather_batch(locations):
    return {loc: get_weather(loc) for loc in locations}

# Tool that accepts multiple inputs
{
    "name": "get_weather_batch",
    "parameters": {
        "type": "object",
        "properties": {
            "locations": {
                "type": "array",
                "items": {"type": "string"}
            }
        }
    }
}