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GenAIScript

Tools (Functions)

You can register tools (also known as functions) that the LLM may decide to call as part of assembling the answer. See OpenAI functions.

Definition

defTool is used to define a tool that can be called by the LLM. It takes a JSON schema to define the input and expects a string output. The LLM decides to call this tool on its own!

defTool(
    "current_weather",
    "get the current weather",
    {
        type: "object",
        properties: {
            location: {
                type: "string",
                description: "The city and state, e.g. San Francisco, CA",
            },
        },
        required: ["location"],
    },
    (args) => {
        const { location } = args
        if (location === "Brussels") return "sunny"
        else return "variable"
    }
)

In the example above, we define a tool called current_weather that takes a location as input and returns the weather.

Weather tool example

This example uses the current_weather tool to get the weather for Brussels.

weather.genai.js
script({
    model: "openai:gpt-3.5-turbo",
    title: "Weather as function",
    description:
        "Query the weather for each city using a dummy weather function",
    temperature: 0.5,
    files: "src/cities.md",
    tests: {
        files: "src/cities.md",
        keywords: "Brussels",
    },
})


$`Query the weather for each listed city and return the results as a table.`


def("CITIES", env.files)


defTool(
    "get_current_weather",
    "get the current weather",
    {
        type: "object",
        properties: {
            location: {
                type: "string",
                description: "The city and state, e.g. San Francisco, CA",
            },
        },
        required: ["location"],
    },
    (args) => {
        const { context, location } = args
        const { trace } = context


        trace.log(`Getting weather for ${location}...`)


        let content = "variable"
        if (location === "Brussels") content = "sunny"


        return content
    }
)

Math tool example

This example uses the math expression evaluator to evaluate a math expression.

math-agent.genai.js
script({
    title: "math-agent",
    model: "gpt-35-turbo",
    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}
`

Packaging as System scripts

To pick and choose which tools to include in a script, you can group them in system scripts. For example, the current_weather tool can be included the system.current_weather.genai.js script.

script({
    title: "Get the current weather",
})
defTool("current_weather", ...)

then use the script id in the tools field.

script({
    ...,
    tools: ["system.current_weather"],
})

Builtin tools

fs_find_files Finds file matching a glob pattern.
fs_read_file Reads a file as text from the file system.
fs_read_summary Reads a summary of a file from the file system.
math_eval Evaluates a math expression
retrieval_fuzz_search Search for keywords using the full text of files and a fuzzy distance.
retrieval_vector_search Search files using embeddings and similarity distance.
retrieval_web_search Search the web for a user query using Bing Search.

Example

Let’s illustrate how tools come together with a question answering script.

In the script below, we add the system.retrieval_web_search which registers the retrieval_web_search tool. This tool will call into retrieval.webSearch as needed.

script({
    title: "Answer questions",
    system: ["system", "system.retrieval_web_search"]
})


def("FILES", env.files)


$`Answer the questions in FILES using a web search.


- List a summary of the answers and the sources used to create the answers.

We can then apply this script to the questions.md file blow.

-   What is weather in Seattle?
-   What laws were voted in the USA congress last week?

After the first request, the LLM requests to call the web_search for each questions. The web search answers are then added to the LLM message history and the request is made again. The second yields the final result which includes the web search results.