How to use tool calling with Chat API?

Follow this quick guide to learn how to use tool calling with the Chat API in Langbase.

We will not use stream mode for this example.

Deprecation Notice

The Chat API endpoint has been deprecated. Learn how to use tool calling with Pipe run API here.

Step #0Create a Pipe

Create a new chat type Pipe or open an existing chat Pipe in your Langbase account. Go ahead and turn off the stream mode for this example and deploy the Pipe.

Alternatively, you can fork this tool call chat Pipe Pipe and skip to step 3.

Step #1Select OpenAI model

Tool calling is available with OpenAI models. So select any of the available OpenAI models in your Pipe.

Step #2Add a tool to the Pipe

Let's add a tool to get the current weather of a given location. Click on the Add button in the Tools section to add a new tool.

This will open a modal where you can define the tool. The tool we are defining will take two arguments:

location
unit.

The location argument is required and the unit argument is optional.

The tool definition will look something like the following.

{
    "type": "function",
    "function": {
        "name": "get_current_weather",
        "description": "Get the current weather of a given location",
        "parameters": {
            "type": "object",
            "required": [
                "location"
            ],
            "properties": {
                "unit": {
                    "enum": [
                        "celsius",
                        "fahrenheit"
                    ],
                    "type": "string"
                },
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                }
            }
        }
    }
}

Go ahead and deploy the Pipe to production.

Note

Playground is disabled

If a Pipe has tools, the playground will be disabled. You can only test tool calling with our Generate and Chat API.

Step #3User prompt to call the tool

Go ahead and copy your Pipe API key from the Pipe API page. You will need this key to call the Generate API.

Now let's create an index.js file where we will define get_current_weather function and also call the Pipe.

const get_current_weather = ({ location, unit }) => {
	// get weather for the location and return the temperature
};

const tools = {
	get_current_weather
};

(async () => {
	const messages = [
		{
			role: 'user',
			content: 'Whats the weather in SF?'
		}
	];

	// replace this with your Pipe API key
	const pipeApiKey = ``;

	const res = await fetch('https://api.langbase.com/beta/chat', {
		method: 'POST',
		headers: {
			'Content-Type': 'application/json',
			Authorization: `Bearer ${pipeApiKey}`
		},
		body: JSON.stringify({
			messages
		})
	});
})();

Because the user prompt requires the current weather of San Francisco, the model will respond with a tool call like the following:

{
	"role": "assistant",
	"content": null,
	"tool_calls": [
		{
			"id": "call_u28sPmmCAWkop0OdgDYDJ9OG",
			"type": "function",
			"function": {
				"name": "get_current_weather",
				"arguments": "{\"location\": \"San Francisco\"}"
			}
		}
	]
}

To check if the model has called the tool, you can check the tool_calls array in the model's response. If it exists, call the functions specified in the tool_calls array and send the response back to Langbase.

(async () => {
	const messages = [
		{
			role: 'user',
			content: 'Whats the weather in SF?',
		},
	];

	// replace this with your Pipe API key
	const pipeApiKey = ``;

	const res = await fetch('https://api.langbase.com/beta/chat', {
		method: 'POST',
		headers: {
			'Content-Type': 'application/json',
			Authorization: `Bearer ${pipeApiKey}`,
		},
		body: JSON.stringify({
			messages,
		}),
	});

	const data = await res.json();

	// get the threadId from the response headers
	const threadId = await res.headers.get('lb-thread-id');

	const { raw } = data;

	// get the response message from the model
	const responseMessage = raw.choices[0].message;

	// get the tool calls from the response message
	const toolCalls = responseMessage.tool_calls;

	if (toolCalls) {
		const toolMessages = [];

		// call all the functions in the tool_calls array
		toolCalls.forEach(toolCall => {
			const toolName = toolCall.function.name;
			const toolParameters = JSON.parse(toolCall.function.arguments);
			const toolFunction = tools[toolName];
			const toolResponse = toolFunction(toolParameters);

			toolMessages.push({
				tool_call_id: toolCall.id, // required: id of the tool call
				role: 'tool', // required: role of the message
				name: toolName, // required: name of the tool
				content: JSON.stringify(toolResponse), // required: response of the tool
			});
		});

		// send the tool responses back to the API
		const res = await fetch('https://api.langbase.com/beta/chat', {
			method: 'POST',
			headers: {
				'Content-Type': 'application/json',
				Authorization: `Bearer ${pipeApiKey}`,
			},
			body: JSON.stringify({
				messages: toolMessages,
				threadId,
			}),
		});

		const data = await res.json();
	}
})();

Note

Send threadId to Langbase

Unlike the Generate API, you don't need to send the assistant's response back to the Chat API. Instead, just send the role "tool" responses. Also, make sure to include the threadId in the request body of your next requests to the Chat API.

You can get the threadId from the response headers of the first request to the Chat API.

This is what a typical model response will look like after calling the tool:

{
	"completion": "The current temperature in San Francisco, CA is 25°C.",
	"raw": {
		"id": "chatcmpl-9hQG8k2pD1A6JoFKQ0O6BKKvJzogS",
		"object": "chat.completion",
		"created": 1720136072,
		"model": "gpt-4o-2024-05-13",
		"choices": [
			{
				"index": 0,
				"message": {
					"role": "assistant",
					"content": "The current temperature in San Francisco, CA is 25°C."
				},
				"logprobs": null,
				"finish_reason": "stop"
			}
		],
		"usage": {
			"prompt_tokens": 121,
			"completion_tokens": 14,
			"total_tokens": 135
		},
		"system_fingerprint": "fp_ce0793330f"
	}
}

And that's it! You have successfully used tool calling with the Chat API in Langbase.

Deprecated