---
title: "Tool/function calling"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Tool/function calling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r}
#| include: false
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = ellmer:::eval_vignette()
)
vcr::setup_knitr()
```

## Introduction

One of the most interesting aspects of modern chat models is their ability to make use of external tools that are defined by the caller.

When making a chat request to the chat model, the caller advertises one or more tools (defined by their function name, description, and a list of expected arguments), and the chat model can choose to respond with one or more "tool calls". These tool calls are requests *from the chat model to the caller* to execute the function with the given arguments; the caller is expected to execute the functions and "return" the results by submitting another chat request with the conversation so far, plus the results. The chat model can then use those results in formulating its response, or, it may decide to make additional tool calls.

*Note that the chat model does not directly execute any external tools!* It only makes requests for the caller to execute them. It's easy to think that tool calling might work like this:

![Diagram showing the wrong mental model of tool calls: a user initiates a request that flows to the assistant, which then runs the code, and returns the result back to the user."](tool-calling-wrong.svg)

But in fact it works like this:

![Diagram showing the correct mental model for tool calls: a user sends a request that needs a tool call, the assistant requests that the user runs that tool, returns the result to the assistant, which uses it to generate the final answer.](tool-calling-right.svg)

The value that the chat model brings is not in helping with execution, but with knowing when it makes sense to call a tool, what values to pass as arguments, and how to use the results in formulating its response.

```{r setup}
library(ellmer)
```


### Motivating example

Let's take a look at an example where we really need an external tool. Chat models generally do not know the current time, which makes questions like these impossible.

```{r}
#| label: no-tool
#| cassette: true

chat <- chat_openai(model = "gpt-4o")
chat$chat("How long ago did Neil Armstrong touch down on the moon?")
```

Since the model doesn't know what day it is, the result is incorrect.

### Defining a tool function

The first thing we'll do is define an R function that returns the current time. 

```{r}
#' Gets the current time in the given time zone.
#'
#' @param tz The time zone to get the current time in.
#' @return The current time in the given time zone.
get_current_time <- function(tz = "UTC") {
  format(Sys.time(), tz = tz, usetz = TRUE)
}
```

Note that we've gone through the trouble of creating [roxygen2 comments](https://roxygen2.r-lib.org/). This isn't necessary, but as we'll see shortly, can make it a bit easier to generate a tool defintion.

```{r}
#| include: false

# Fake it for the vignette so we always get the same results
get_current_time <- function(tz = "UTC") {
  format(
    as.POSIXct("2025-06-25 11:53:23", tz = "America/Chicago"),
    tz = tz,
    usetz = TRUE
  )
}
```

To turn a function into a tool, we provide some additional metadata that the model will use:

```{r}
get_current_time <- tool(
  get_current_time,
  name = "get_current_time",
  description = "Returns the current time.",
  arguments = list(
    tz = type_string(
      "Time zone to display the current time in. Defaults to `\"UTC\"`.",
      required = FALSE
    )
  )
)
```

This is a fair amount of code to write, even for such a simple function. Fortunately, you don't have to write this by hand! I generated the above `tool()` call by calling `create_tool_def(get_current_time)`, which uses an LLM to generate the `tool()` call for you. `create_tool_def()` is not perfect, so you must review the generated code before using it, but it is a big time-saver.

Note that a tool is just a special type of function so we can still call it:

```{r}
get_current_time()
```

### Registering and using tools

Now we need to give our chat object access to our tool. We do this with `$register_tool()`:

```{r}
chat$register_tool(get_current_time)
```

That's all we need to do! Let's retry our query:

```{r}
#| label: with-tool
#| cassette: true

chat$chat("How long ago did Neil Armstrong touch down on the moon?")
```

That's correct! Without any further guidance, the chat model decided to call our tool function and successfully used its result in formulating its response. 

If we print the chat we can see where the model decided to use the tool:

```{r}
chat
```

(Full disclosure: I originally tried this example with the default model of `gpt-4o-mini` and it got the tool calling right but the date math wrong, hence the explicit `model="gpt-4o"`.)

This tool example was extremely simple, but you can imagine doing much more interesting things from tool functions: calling APIs, reading from or writing to a database, kicking off a complex simulation, or even calling a complementary GenAI model (like an image generator). Or if you are using ellmer in a Shiny app, you could use tools to set reactive values, setting off a chain of reactive updates.

### Tool inputs and outputs

Remember that tool arguments come from the LLM, and tool results are returned to the LLM. This implies that you should keep both as simple as possible.

Inputs to a tool call, must be defined by `type_boolean()`, `type_integer()`, `type_number()`, `type_string()`, `type_enum()`, `type_array()`, or `type_object()`. We recommend keeping them as simple as possible, focusing on basic scalar types as much as you can.

The output of the tool call will be interpreted by the LLM, just as if you had typed that information into the data. That means you'll generally want to produce text or other atomic vectors. For more complex data, ellmer will automatically serialize the result to JSON, which LLMs generally seem to be good at understanding.  If you must have more direct control of the structure of the JSON that's returned, you can return a JSON-serializable value wrapped in `I()`, which ellmer will leave alone until the entire request is JSON-serialized.

To show off these ideas, here's a slightly more complicated example simulating a weather API that returns data for multiple cities at once. The `get_weather()` function returns a data frame that ellmer will automatically convert into JSON in row-major format, which our experiments suggest is good for LLMs.

```{r}
get_weather <- tool(
  function(cities) {
    raining <- c(London = "heavy", Houston = "none", Chicago = "overcast")
    temperature <- c(London = "cool", Houston = "hot", Chicago = "warm")
    wind <- c(London = "strong", Houston = "weak", Chicago = "strong")

    data.frame(
      city = cities,
      raining = unname(raining[cities]),
      temperature = unname(temperature[cities]),
      wind = unname(wind[cities])
    )
  },
  name = "get_weather",
  description = "
    Report on weather conditions in multiple cities. For efficiency, request 
    all weather updates using a single tool call
  ",
  arguments = list(
    cities = type_array(type_string(), "City names")
  )
)
```

Now we register and use it:

```{r}
#| label: inputs-outputs
#| cassette: true

chat <- chat_openai()
chat$register_tool(get_weather)
chat$chat("Give me a weather udpate for London and Chicago")
```

We can print the chat to confirm that the model only performed a single tool call:

```{r}
chat
```
