Tools
You can register tools (also known as functions) that the LLM may decide to call as part of assembling the answer. See OpenAI functions, Ollama tools, or Anthropic tool use.
Not all LLM models support tools, in those cases, GenAIScript also support a fallback mechanism to implement tool call through system prompts (see Ad Hoc Tools).
defTool
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.
script({ model: "small", 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 ${location}...`)
let content = "variable" if (location === "Brussels") content = "sunny"
return content })Math tool example
This example uses the math expression evaluator to evaluate a math expression.
script({ title: "math-agent", model: "small", description: "A port of https://ts.llamaindex.ai/examples/agent", parameters: { question: { type: "string", default: "How much is 11 + 4? then divide by 3?", }, }, tests: { description: "Testing the default prompt", keywords: "5", },})
defTool( "sum", "Use this function to sum two numbers", { a: 1, b: 2 }, ({ a, b }) => { console.log(`${a} + ${b}`) return `${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 }) => { console.log(`${a} / ${b}`) return `${a / b}` })
$`Answer the following arithmetic question:
${env.vars.question}`Fallback Tools
Some LLM models do not have built-in model support. For those model, it is possible to enable tool support through system prompts. The performance may be lower than built-in tools, but it is still possible to use tools.
The tool support is implemented in system.tool_calls and “teaches” the LLM how to call tools. When this mode is enabled, you will see the tool call tokens being responded by the LLM.
GenAIScript maintains a list of well-known models that do not support tools so it will happen automatically for those models.
To enable this mode, you can either
- add the
fallbackToolsoption to the script
script({ fallbackTools: true,})- or add the
--fallack-toolsflag to the CLI
npx genaiscript run ... --fallback-toolsPackaging 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.mjs script.
script({ title: "Get the current weather",})defTool("current_weather", ...)then use the script id in the tools field.
script({ ..., tools: ["system.current_weather"],})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.