---
title: "Modify prompt enhancements"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Modify prompt enhancements}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

There are a lot of parameters that need to be set or sent when communicating
with the LLM. To reduce the complexity of every single request, `chattr` processes
and stores those parameters in an R object. The object can be accessed via the
function `chattr_defaults()`:


```{r}
library(chattr)

chattr_use("gpt4")

chattr_defaults()
```

**NOTE:** - For most users, this change will not work because accessing GPT 4
via the REST API endpoints is currently restricted to a few developers. 

### Support for `glue` 

The `prompt` argument supports `glue`. This means that you can pass current values
of variables, or current output from functions within your current R session. Make
sure that such output, or value, is a character that is readable by the LLM.

Here is a simple example of a function that passes the variables currently in
your R session that contain "x":

```{r}
my_list <- function() {
  return(ls(pattern = "x", envir = globalenv()))
}
```

The `my_list()` function is passed to the `prompt` argument enclosed in braces:

```{r}
chattr_defaults(prompt = "{ my_list() }")
```

Now we can test it, by setting two variables that start with "x"

```{r}
x1 <- 1
x2 <- 2
```

To see what will be sent to the LLM, you can use `chattr(preview = TRUE)`. The two
variables will be listed as part of the prompt:

```{r}
chattr(preview = TRUE)
```

We can see how the adding a new variable will modify the prompt with out us
having to modify `chattr_defaults()`:

```{r}
x3 <- 3

chattr(preview = TRUE)
```




