Tool Agent

Using tools (formerly functions), you can define a built-in agent that can take decisions and reasoning based on the tools provided to it.

Let’s illustrate this concept using the llamaindex sum div sample: an agent that can sum or divide two numbers and needs to answer basic arithmetic questions.

Using tools

By declaring tools (and providing a descriptive description), you provide the opportunity for the LLM to requests a tool call during the output generation. In the snippet below, we declare a tool that can sum two numbers. It will be called by the LLM when a sum operation is required.

defTool(
    "sum",
    "Sum two numbers",
    {
        type: "object",
        properties: {
            a: {
                type: "number",
                description: "The first number",
            },
            b: {
                type: "number",
                description: "The second number",
            },
        },
        required: ["a", "b"],
    },
    ({ a, b }) => `${a + b}`
)

You can also simplify the parameter definition by provider an example object and the schema will be inferred._createMdxContent

defTool("sum", "Sum two numbers", { a: 1, b: 2 }, ({ a, b }) => `${a + b}`)

Parameters

The arithmetic question can be declared as a script parameter to be used in the agent script.

script({
    ...,
    parameters: {
        "question": {
            type: "string",
            default: "How much is 5 + 5? then divide by 2?"
        }
    }
})

The parameter value are populated in the env.vars object.

...
$`Answer the following arithmetic question:

    ${env.vars.question}
`

Putting it all together, we define another tool to divide two numbers and inline an arithmetic question.

script({
    title: "math-agent",
    model: "small",
    description: "A port of https://ts.llamaindex.ai/examples/agent",
    parameters: {
        question: {
            type: "string",
            default: "How much is 11 + 4? then divide by 3?",
        },
    },
    tests: {
        description: "Testing the default prompt",
        keywords: "5",
    },
})

defTool(
    "sum",
    "Use this function to sum two numbers",
    {
        type: "object",
        properties: {
            a: {
                type: "number",
                description: "The first number",
            },
            b: {
                type: "number",
                description: "The second number",
            },
        },
        required: ["a", "b"],
    },
    ({ a, b }) => `${a + b}`
)

defTool(
    "divide",
    "Use this function to divide two numbers",
    {
        type: "object",
        properties: {
            a: {
                type: "number",
                description: "The first number",
            },
            b: {
                type: "number",
                description: "The second number",
            },
        },
        required: ["a", "b"],
    },
    ({ a, b }) => `${a / b}`
)

$`Answer the following arithmetic question:

    ${env.vars.question}
`

👤 user

Answer the following arithmetic question:

How much is 11 + 4? then divide by 3?

🤖 assistant

• 📠 tool call divide({"a":15,"b":3}) (call_9p0oWdWpT6vGyxzwq2vJXHrq)

🛠️ tool call_9p0oWdWpT6vGyxzwq2vJXHrq

5

🤖 assistant

The result of (11 + 4) divided by 3 is 5.

Using system.math

The system prompt system.math wraps the parsers.math expression parser and evaluator and exposes it as a tool.

This simplifies the agent script as we do not have to define tools anymore.

math-agent.genai.mjs

script({
    title: "math-agent-system",
    model: "small",
    description: "A port of https://ts.llamaindex.ai/examples/agent",
    system: ["system.math"],
    parameters: {
        "question": {
            type: "string",
            default: "How much is 11 + 4? then divide by 3?"
        }
    },
    tests: {
        description: "Testing the default prompt",
        keywords: "5"
    }
})

$`Answer the following arithmetic question:

    ${env.vars.question}

`