From 5edbd44f671baa1945fc3fc13be5d833df014081 Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Thu, 28 Nov 2024 15:14:35 -0600 Subject: [PATCH] More vignettes (#135) Fixes #48 Defaults to GPT-4o (not mini) --- .Rbuildignore | 1 + .gitignore | 2 + NEWS.md | 2 + R/content.R | 2 +- R/provider-claude.R | 12 +- R/provider-gemini.R | 5 + R/provider-github.R | 2 +- R/provider-ollama.R | 4 +- R/provider-openai.R | 18 +- R/tools-def-auto.R | 2 +- R/turns.R | 5 +- README.Rmd | 113 +++-- README.md | 163 +++++--- man/chat_claude.Rd | 12 +- man/chat_gemini.Rd | 5 +- man/chat_ollama.Rd | 4 +- man/chat_openai.Rd | 16 +- man/create_tool_def.Rd | 2 +- tests/testthat/_snaps/provider-openai.md | 2 +- tests/testthat/_snaps/turns.md | 2 +- tests/testthat/test-content.R | 2 +- tests/testthat/test-provider-openai.R | 12 +- vignettes/elmer.Rmd | 138 +++++++ vignettes/prompt-design.Rmd | 390 ++++++++++++++++++ vignettes/prompt-design_cache/html/__packages | 1 + ...sic_23c2af49b209e7f837cf1d5796fe658a.RData 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a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,7 @@ # elmer (development version) +* The default `chat_openai()` model is now GPT-4o. + * New `Chat$set_turns()` to set turns. `Chat$turns()` is now `Chat$get_turns()`. `Chat$system_prompt()` is replaced with `Chat$set_system_prompt()` and `Chat$get_system_prompt()`. * Async and streaming async chat are now event-driven and use `later::later_fd()` to wait efficiently on curl socket activity (#157). diff --git a/R/content.R b/R/content.R index 184eecc..93f9061 100644 --- a/R/content.R +++ b/R/content.R @@ -163,7 +163,7 @@ as_content <- function(x, error_call = caller_env()) { if (is.null(x)) { list() } else if (is.character(x)) { - ContentText(paste0(x, collapse = "\n")) + ContentText(paste0(x, collapse = "\n\n")) } else if (S7_inherits(x, Content)) { x } else { diff --git a/R/provider-claude.R b/R/provider-claude.R index a66b272..70701a6 100644 --- a/R/provider-claude.R +++ b/R/provider-claude.R @@ -9,12 +9,14 @@ NULL #' @description #' [Anthropic](https://www.anthropic.com) provides a number of chat based #' models under the [Claude](https://www.anthropic.com/claude) moniker. +#' Note that a Claude Pro membership does not give you the ability to call +#' models via the API; instead, you will need to sign up (and pay for) a +#' [developer account](https://console.anthropic.com/) #' -#' Note that a Claude Prop membership does not give you the ability to call -#' models via the API. You will need to go to the -#' [developer console](https://console.anthropic.com/account/keys) to sign up -#' (and pay for) a developer account that will give you an API key that -#' you can use with this package. +#' To authenticate, we recommend saving your +#' [API key](https://console.anthropic.com/account/keys) to +#' the `ANTHROPIC_API_KEY` env var in your `.Renviron` +#' (which you can easily edit by calling `usethis::edit_r_environ()`). #' #' @inheritParams chat_openai #' @inherit chat_openai return diff --git a/R/provider-gemini.R b/R/provider-gemini.R index bbafcf3..293661e 100644 --- a/R/provider-gemini.R +++ b/R/provider-gemini.R @@ -6,6 +6,11 @@ NULL #' Chat with a Google Gemini model #' +#' To authenticate, we recommend saving your +#' [API key](https://aistudio.google.com/app/apikey) to +#' the `GOOGLE_API_KEY` env var in your `.Renviron` +#' (which you can easily edit by calling `usethis::edit_r_environ()`). +#' #' @param api_key The API key to use for authentication. You generally should #' not supply this directly, but instead set the `GOOGLE_API_KEY` environment #' variable. diff --git a/R/provider-github.R b/R/provider-github.R index d82bf62..18dff8b 100644 --- a/R/provider-github.R +++ b/R/provider-github.R @@ -27,7 +27,7 @@ chat_github <- function(system_prompt = NULL, check_installed("gitcreds") - model <- set_default(model, "gpt-4o-mini") + model <- set_default(model, "gpt-4o") chat_openai( system_prompt = system_prompt, diff --git a/R/provider-ollama.R b/R/provider-ollama.R index 3b5f26b..c464e17 100644 --- a/R/provider-ollama.R +++ b/R/provider-ollama.R @@ -1,8 +1,8 @@ -#' Chat with a local ollama model +#' Chat with a local Ollama model #' #' @description #' To use `chat_ollama()` first download and install -#' [ollama](https://ollama.com). Then install some models from the command line, +#' [Ollama](https://ollama.com). Then install some models from the command line, #' e.g. with `ollama pull llama3.1` or `ollama pull gemma2`. #' #' This function is a lightweight wrapper around [chat_openai()] with diff --git a/R/provider-openai.R b/R/provider-openai.R index 7b275a9..146ac09 100644 --- a/R/provider-openai.R +++ b/R/provider-openai.R @@ -7,14 +7,16 @@ NULL #' Chat with an OpenAI model #' #' @description -#' [OpenAI](https://openai.com/o1/) provides a number of chat based models under -#' the [ChatGPT](https://chatgpt.com) moniker. +#' [OpenAI](https://openai.com/) provides a number of chat-based models, +#' mostly under the [ChatGPT](https://chat.openai.com/) brand. +#' Note that a ChatGPT Plus membership does not grant access to the API. +#' You will need to sign up for a developer account (and pay for it) at the +#' [developer platform](https://platform.openai.com). #' -#' Note that a ChatGPT Plus membership does not give you the ability to call -#' models via the API. You will need to go to the -#' [developer platform](https://platform.openai.com) to sign up -#' (and pay for) a developer account that will give you an API key that -#' you can use with this package. +#' For authentication, we recommend saving your +#' [API key](https://platform.openai.com/account/api-keys) to +#' the `OPENAI_API_KEY` environment variable in your `.Renviron` file. +#' You can easily edit this file by calling `usethis::edit_r_environ()`. #' #' @param system_prompt A system prompt to set the behavior of the assistant. #' @param turns A list of [Turn]s to start the chat with (i.e., continuing a @@ -56,7 +58,7 @@ chat_openai <- function(system_prompt = NULL, api_args = list(), echo = c("none", "text", "all")) { turns <- normalize_turns(turns, system_prompt) - model <- set_default(model, "gpt-4o-mini") + model <- set_default(model, "gpt-4o") echo <- check_echo(echo) if (is_testing() && is.null(seed)) { diff --git a/R/tools-def-auto.R b/R/tools-def-auto.R index f75331f..04d4233 100644 --- a/R/tools-def-auto.R +++ b/R/tools-def-auto.R @@ -23,7 +23,7 @@ #' @param topic A symbol or string literal naming the function to create #' metadata for. Can also be an expression of the form `pkg::fun`. #' @param model The OpenAI model to use for generating the metadata. Defaults to -#' "gpt-4o", which is highly recommended over "gpt-4o-mini". +#' "gpt-4o". #' @param echo Emit the registration code to the console. Defaults to `TRUE` in #' interactive sessions. #' @param verbose If `TRUE`, print the input we send to the LLM, which may be diff --git a/R/turns.R b/R/turns.R index a95b647..93de9ad 100644 --- a/R/turns.R +++ b/R/turns.R @@ -92,7 +92,10 @@ normalize_turns <- function(turns = NULL, overwrite = FALSE, error_call = caller_env()) { - check_string(system_prompt, allow_null = TRUE, call = error_call) + check_character(system_prompt, allow_null = TRUE, call = error_call) + if (length(system_prompt) > 1) { + system_prompt <- paste(system_prompt, collapse = "\n\n") + } if (!is.null(turns)) { if (!is.list(turns) || is_named(turns)) { diff --git a/README.Rmd b/README.Rmd index 9f19e4c..8b40772 100644 --- a/README.Rmd +++ b/README.Rmd @@ -20,7 +20,7 @@ knitr::opts_chunk$set( [![R-CMD-check](https://github.com/tidyverse/elmer/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/elmer/actions/workflows/R-CMD-check.yaml) -The goal of elmer is to provide a user friendly wrapper over the most common llm providers. Major design goals include support for streaming and making it easy to register and call R functions. +elmer makes it easy to use large language models (LLM) from R. It supports a wider variety of LLM providers and implements a rich set of features including streaming outputs, tool/function calling, structured data extration, and more. (Looking for something similar to elmer for python? Check out [chatlas](https://github.com/cpsievert/chatlas)!) @@ -28,22 +28,41 @@ The goal of elmer is to provide a user friendly wrapper over the most common llm You can install the development version of elmer from [GitHub](https://github.com/) with: -``` r +```{r} +#| eval: false # install.packages("pak") pak::pak("tidyverse/elmer") ``` -## Prerequisites +## Providers -Depending on which backend you use, you'll need to set the appropriate environment variable in your `~/.Renviron` (an easy way to open that file is to call `usethis::edit_r_environ()`): +elmer supports a wide variety of model providers: -* For `chat_claude()`, set `ANTHROPIC_API_KEY` using the key from . -* For `chat_gemini()`, set `GOOGLE_API_KEY` using the key from . -* For `chat_openai()` set `OPENAI_API_KEY` using the key from . +* Anthropic's Claude: `chat_claude()`. +* AWS Bedrock: `chat_bedrock()`. +* Azure OpenAI: `chat_azure()`. +* Databricks: `chat_databricks()`. +* GitHub model marketplace: `chat_github()`. +* Google Gemini: `chat_gemini()`. +* Groq: `chat_groq()`. +* Ollama: `chat_ollama()`. +* OpenAI: `chat_openai()`. +* perplexity.ai: `chat_perplexity()`. +* Snowflake Cortex: `chat_cortex()`. + +## Model choice + +If you're using elmer inside your organisation, you'll be limited to what your org allows, which is likely to be one provided by a big cloud provider, e.g. `chat_azure()`, `chat_bedrock()`, `chat_databricks()`, or `chat_snowflake()`. If you're using elmer for your own personal exploration, you have a lot more freedom so we have a few recommendations to help you get started: + +- `chat_openai()` or `chat_claude()` are both good places to start. `chat_openai()` defaults to **GPT-4o**, but you can use `model = "gpt-4o-mini"` for a cheaper lower-quality model, or `model = "o1-mini"` for more complex reasoning. `chat_claude()` is similarly good; it defaults to **Claude 3.5 Sonnet** which we have found to be particularly code at writing code. + +- `chat_gemini()` is great for large prompt, because it has a much larger context window than other models. It allows up to 1 million tokens, compared to Claude 3.5 Sonnet's 200k and GPT-4o's 128k. + +- `chat_ollama()`, which uses [Ollama](https://ollama.com), allows you to run models on your own computer. The biggest models you can run locally aren't as good as the state of the art hosted models, but they also don't share your data and are effectively free. ## Using elmer -You chat with elmer in several different ways, depending on whether you are working interactively or programmatically. They all start with creating a new chat object: +You can work with elmer in several different ways, depending on whether you are working interactively or programmatically. They all start with creating a new chat object: ```r library(elmer) @@ -51,17 +70,18 @@ library(elmer) chat <- chat_openai( model = "gpt-4o-mini", system_prompt = "You are a friendly but terse assistant.", - echo = TRUE ) ``` -Chat objects are stateful: they retain the context of the conversation, so each new query can build on the previous ones. This is true regardless of which of the various ways of chatting you use. +Chat objects are stateful [R6 objects](https://r6.r-lib.org): they retain the context of the conversation, so each new query can build on the previous ones, and you call their methods with `$`. ### Interactive chat console -The most interactive, least programmatic way of using elmer is to chat with it directly in your R console with `live_console(chat)` or in your browser with `live_browser()`. +The most interactive and least programmatic way of using elmer is to chat directly in your R console or browser with `live_console(chat)` or `live_browser()`: + +```{r} +#| eval: false -```{r eval=FALSE} live_console(chat) #> ╔════════════════════════════════════════════════════════╗ #> ║ Entering chat console. Use """ for multi-line input. ║ @@ -76,59 +96,70 @@ live_console(chat) #> in the early 1990s. ``` -The chat console is useful for quickly exploring the capabilities of the model, especially when you've customized the chat object with tool integrations (see below). - -Again, keep in mind that the chat object retains state, so when you enter the chat console, any previous interactions with that chat object are still part of the conversation, and any interactions you have in the chat console will persist even after you exit back to the R prompt. +Keep in mind that the chat object retains state, so when you enter the chat console, any previous interactions with that chat object are still part of the conversation, and any interactions you have in the chat console will persist after you exit back to the R prompt. This is true regardless of which of the various chat functions you use. ### Interactive method call -The second most interactive way to chat using elmer is to call the `chat()` method. +The second most interactive way to chat is to call the `chat()` method: + +```{r} +#| eval: false -```{r eval=FALSE} chat$chat("What preceding languages most influenced R?") #> R was primarily influenced by the S programming language, particularly S-PLUS. #> Other languages that had an impact include Scheme and various data analysis #> languages. ``` -If you initialize the chat object with `echo = TRUE`, as we did above, the `chat` method streams the response to the console as it arrives. When the entire response is received, it is returned as a character vector (invisibly, so it's not printed twice). +If you initialize the chat object in the global environment, the `chat` method will stream the response to the console as it arrives. When the entire response is received, it is also returned as a character vector (but invisibly, so it's not printed twice). This mode is useful when you want to see the response as it arrives, but you don't want to enter the chat console. -This mode is useful when you want to see the response as it arrives, but you don't want to enter the chat console. +If you want to ask a question about an image, you can pass one or more additional input arguments using `content_image_file()` and/or `content_image_url()`: -#### Vision (image input) +```{r} +#| eval: false -If you want to ask a question about an image, you can pass one or more additional input arguments using `content_image_file()` and/or `content_image_url()`. - -```{r eval=FALSE} chat$chat( content_image_url("https://www.r-project.org/Rlogo.png"), "Can you explain this logo?" ) -#> The logo of R features a stylized letter "R" in blue, enclosed in an oval shape that resembles the letter "O," -#> signifying the programming language's name. The design conveys a modern and professional look, reflecting its use -#> in statistical computing and data analysis. The blue color often represents trust and reliability, which aligns -#> with R's role in data science. +#> The logo of R features a stylized letter "R" in blue, enclosed in an oval +#> shape that resembles the letter "O," signifying the programming language's +#> name. The design conveys a modern and professional look, reflecting its use +#> in statistical computing and data analysis. The blue color often represents +#> trust and reliability, which aligns with R's role in data science. ``` -The `content_image_url` function takes a URL to an image file and sends that URL directly to the API. The `content_image_file` function takes a path to a local image file and encodes it as a base64 string to send to the API. Note that by default, `content_image_file` automatically resizes the image to fit within 512x512 pixels; set the `resize` parameter to `"high"` if higher resolution is needed. - ### Programmatic chat -If you don't want to see the response as it arrives, you can turn off echoing by leaving off the `echo = TRUE` argument to `chat_openai()`. - -```{r eval=FALSE} -chat <- chat_openai( - model = "gpt-4o-mini", - system_prompt = "You are a friendly but terse assistant." -) -chat$chat("Is R a functional programming language?") -#> [1] "Yes, R supports functional programming concepts. It allows functions to be first-class objects, supports higher-order functions, and encourages the use of functions as core components of code. However, it also supports procedural and object-oriented programming styles." +The most programmataic way to chat is to create the chat object inside a function, where live streaming is automatically suppressed and `$chat()` returns the result as a string: + +```{r} +#| eval: false + +my_function <- function() { + chat <- chat_openai( + model = "gpt-4o-mini", + system_prompt = "You are a friendly but terse assistant.", + ) + chat$chat("Is R a functional programming language?") +} +my_function() +#> [1] "Yes, R supports functional programming concepts. It allows functions to +#> be first-class objects, supports higher-order functions, and encourages the +#> use of functions as core components of code. However, it also supports +#> procedural and object-oriented programming styles." ``` -This mode is useful for programming using elmer, when the result is either not intended for human consumption or when you want to process the response before displaying it. +If needed, you can manually control this behaviour with the `echo` argument. + +This mode is useful for programming with elmer, when the result is either not intended for human consumption or when you want to process the response before displaying it. ## Learning more -* Learn more about streaming and async APIs in `vignette("streaming-async")`. -* Learn more about tool calling (aka function calling) in `vignette("tool-calling")`. +elmer comes with a bunch of vignettes to help you learn more: + +* Learn key vocabulary and see example use cases in `vignette("elmer")`. +* Learn how to design your prompt in `vignette("prompt-design")`. +* Learn about tool/function calling in `vignette("tool-calling")`. * Learn how to extract structured data in `vignette("structured-data")`. +* Learn about streaming and async APIs in `vignette("streaming-async")`. diff --git a/README.md b/README.md index 7abc483..5eab89b 100644 --- a/README.md +++ b/README.md @@ -10,9 +10,10 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h [![R-CMD-check](https://github.com/tidyverse/elmer/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/elmer/actions/workflows/R-CMD-check.yaml) -The goal of elmer is to provide a user friendly wrapper over the most -common llm providers. Major design goals include support for streaming -and making it easy to register and call R functions. +elmer makes it easy to use large language models (LLM) from R. It +supports a wider variety of LLM providers and implements a rich set of +features including streaming outputs, tool/function calling, structured +data extration, and more. (Looking for something similar to elmer for python? Check out [chatlas](https://github.com/cpsievert/chatlas)!) @@ -27,23 +28,51 @@ You can install the development version of elmer from pak::pak("tidyverse/elmer") ``` -## Prerequisites - -Depending on which backend you use, you’ll need to set the appropriate -environment variable in your `~/.Renviron` (an easy way to open that -file is to call `usethis::edit_r_environ()`): - -- For `chat_claude()`, set `ANTHROPIC_API_KEY` using the key from - . -- For `chat_gemini()`, set `GOOGLE_API_KEY` using the key from - . -- For `chat_openai()` set `OPENAI_API_KEY` using the key from - . +## Providers + +elmer supports a wide variety of model providers: + +- Anthropic’s Claude: `chat_claude()`. +- AWS Bedrock: `chat_bedrock()`. +- Azure OpenAI: `chat_azure()`. +- Databricks: `chat_databricks()`. +- GitHub model marketplace: `chat_github()`. +- Google Gemini: `chat_gemini()`. +- Groq: `chat_groq()`. +- Ollama: `chat_ollama()`. +- OpenAI: `chat_openai()`. +- perplexity.ai: `chat_perplexity()`. +- Snowflake Cortex: `chat_cortex()`. + +## Model choice + +If you’re using elmer inside your organisation, you’ll be limited to +what your org allows, which is likely to be one provided by a big cloud +provider, e.g. `chat_azure()`, `chat_bedrock()`, `chat_databricks()`, or +`chat_snowflake()`. If you’re using elmer for your own personal +exploration, you have a lot more freedom so we have a few +recommendations to help you get started: + +- `chat_openai()` or `chat_claude()` are both good places to start. + `chat_openai()` defaults to **GPT-4o**, but you can use + `model = "gpt-4o-mini"` for a cheaper lower-quality model, or + `model = "o1-mini"` for more complex reasoning. `chat_claude()` is + similarly good; it defaults to **Claude 3.5 Sonnet** which we have + found to be particularly code at writing code. + +- `chat_gemini()` is great for large prompt, because it has a much + larger context window than other models. It allows up to 1 million + tokens, compared to Claude 3.5 Sonnet’s 200k and GPT-4o’s 128k. + +- `chat_ollama()`, which uses [Ollama](https://ollama.com), allows you + to run models on your own computer. The biggest models you can run + locally aren’t as good as the state of the art hosted models, but they + also don’t share your data and are effectively free. ## Using elmer -You chat with elmer in several different ways, depending on whether you -are working interactively or programmatically. They all start with +You can work with elmer in several different ways, depending on whether +you are working interactively or programmatically. They all start with creating a new chat object: ``` r @@ -52,19 +81,18 @@ library(elmer) chat <- chat_openai( model = "gpt-4o-mini", system_prompt = "You are a friendly but terse assistant.", - echo = TRUE ) ``` -Chat objects are stateful: they retain the context of the conversation, -so each new query can build on the previous ones. This is true -regardless of which of the various ways of chatting you use. +Chat objects are stateful [R6 objects](https://r6.r-lib.org): they +retain the context of the conversation, so each new query can build on +the previous ones, and you call their methods with `$`. ### Interactive chat console -The most interactive, least programmatic way of using elmer is to chat -with it directly in your R console with `live_console(chat)` or in your -browser with `live_browser()`. +The most interactive and least programmatic way of using elmer is to +chat directly in your R console or browser with `live_console(chat)` or +`live_browser()`: ``` r live_console(chat) @@ -81,19 +109,15 @@ live_console(chat) #> in the early 1990s. ``` -The chat console is useful for quickly exploring the capabilities of the -model, especially when you’ve customized the chat object with tool -integrations (see below). - -Again, keep in mind that the chat object retains state, so when you -enter the chat console, any previous interactions with that chat object -are still part of the conversation, and any interactions you have in the -chat console will persist even after you exit back to the R prompt. +Keep in mind that the chat object retains state, so when you enter the +chat console, any previous interactions with that chat object are still +part of the conversation, and any interactions you have in the chat +console will persist after you exit back to the R prompt. This is true +regardless of which of the various chat functions you use. ### Interactive method call -The second most interactive way to chat using elmer is to call the -`chat()` method. +The second most interactive way to chat is to call the `chat()` method: ``` r chat$chat("What preceding languages most influenced R?") @@ -102,60 +126,63 @@ chat$chat("What preceding languages most influenced R?") #> languages. ``` -If you initialize the chat object with `echo = TRUE`, as we did above, -the `chat` method streams the response to the console as it arrives. -When the entire response is received, it is returned as a character -vector (invisibly, so it’s not printed twice). - -This mode is useful when you want to see the response as it arrives, but -you don’t want to enter the chat console. - -#### Vision (image input) +If you initialize the chat object in the global environment, the `chat` +method will stream the response to the console as it arrives. When the +entire response is received, it is also returned as a character vector +(but invisibly, so it’s not printed twice). This mode is useful when you +want to see the response as it arrives, but you don’t want to enter the +chat console. If you want to ask a question about an image, you can pass one or more additional input arguments using `content_image_file()` and/or -`content_image_url()`. +`content_image_url()`: ``` r chat$chat( content_image_url("https://www.r-project.org/Rlogo.png"), "Can you explain this logo?" ) -#> The logo of R features a stylized letter "R" in blue, enclosed in an oval shape that resembles the letter "O," -#> signifying the programming language's name. The design conveys a modern and professional look, reflecting its use -#> in statistical computing and data analysis. The blue color often represents trust and reliability, which aligns -#> with R's role in data science. +#> The logo of R features a stylized letter "R" in blue, enclosed in an oval +#> shape that resembles the letter "O," signifying the programming language's +#> name. The design conveys a modern and professional look, reflecting its use +#> in statistical computing and data analysis. The blue color often represents +#> trust and reliability, which aligns with R's role in data science. ``` -The `content_image_url` function takes a URL to an image file and sends -that URL directly to the API. The `content_image_file` function takes a -path to a local image file and encodes it as a base64 string to send to -the API. Note that by default, `content_image_file` automatically -resizes the image to fit within 512x512 pixels; set the `resize` -parameter to `"high"` if higher resolution is needed. - ### Programmatic chat -If you don’t want to see the response as it arrives, you can turn off -echoing by leaving off the `echo = TRUE` argument to `chat_openai()`. +The most programmataic way to chat is to create the chat object inside a +function, where live streaming is automatically suppressed and `$chat()` +returns the result as a string: ``` r -chat <- chat_openai( - model = "gpt-4o-mini", - system_prompt = "You are a friendly but terse assistant." -) -chat$chat("Is R a functional programming language?") -#> [1] "Yes, R supports functional programming concepts. It allows functions to be first-class objects, supports higher-order functions, and encourages the use of functions as core components of code. However, it also supports procedural and object-oriented programming styles." +my_function <- function() { + chat <- chat_openai( + model = "gpt-4o-mini", + system_prompt = "You are a friendly but terse assistant.", + ) + chat$chat("Is R a functional programming language?") +} +my_function() +#> [1] "Yes, R supports functional programming concepts. It allows functions to +#> be first-class objects, supports higher-order functions, and encourages the +#> use of functions as core components of code. However, it also supports +#> procedural and object-oriented programming styles." ``` -This mode is useful for programming using elmer, when the result is +If needed, you can manually control this behaviour with the `echo` +argument. + +This mode is useful for programming with elmer, when the result is either not intended for human consumption or when you want to process the response before displaying it. ## Learning more -- Learn more about streaming and async APIs in - `vignette("streaming-async")`. -- Learn more about tool calling (aka function calling) in - `vignette("tool-calling")`. +elmer comes with a bunch of vignettes to help you learn more: + +- Learn key vocabulary and see example use cases in `vignette("elmer")`. +- Learn how to design your prompt in `vignette("prompt-design")`. +- Learn about tool/function calling in `vignette("tool-calling")`. - Learn how to extract structured data in `vignette("structured-data")`. +- Learn about streaming and async APIs in `vignette("streaming-async")`. diff --git a/man/chat_claude.Rd b/man/chat_claude.Rd index 8567658..567f895 100644 --- a/man/chat_claude.Rd +++ b/man/chat_claude.Rd @@ -53,12 +53,14 @@ A \link{Chat} object. \description{ \href{https://www.anthropic.com}{Anthropic} provides a number of chat based models under the \href{https://www.anthropic.com/claude}{Claude} moniker. +Note that a Claude Pro membership does not give you the ability to call +models via the API; instead, you will need to sign up (and pay for) a +\href{https://console.anthropic.com/}{developer account} -Note that a Claude Prop membership does not give you the ability to call -models via the API. You will need to go to the -\href{https://console.anthropic.com/account/keys}{developer console} to sign up -(and pay for) a developer account that will give you an API key that -you can use with this package. +To authenticate, we recommend saving your +\href{https://console.anthropic.com/account/keys}{API key} to +the \code{ANTHROPIC_API_KEY} env var in your \code{.Renviron} +(which you can easily edit by calling \code{usethis::edit_r_environ()}). } \seealso{ Other chatbots: diff --git a/man/chat_gemini.Rd b/man/chat_gemini.Rd index fc091b0..9ed5492 100644 --- a/man/chat_gemini.Rd +++ b/man/chat_gemini.Rd @@ -48,7 +48,10 @@ Note this only affects the \code{chat()} method.} A \link{Chat} object. } \description{ -Chat with a Google Gemini model +To authenticate, we recommend saving your +\href{https://aistudio.google.com/app/apikey}{API key} to +the \code{GOOGLE_API_KEY} env var in your \code{.Renviron} +(which you can easily edit by calling \code{usethis::edit_r_environ()}). } \seealso{ Other chatbots: diff --git a/man/chat_ollama.Rd b/man/chat_ollama.Rd index 9aa1fd5..a25fb5c 100644 --- a/man/chat_ollama.Rd +++ b/man/chat_ollama.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/provider-ollama.R \name{chat_ollama} \alias{chat_ollama} -\title{Chat with a local ollama model} +\title{Chat with a local Ollama model} \usage{ chat_ollama( system_prompt = NULL, @@ -45,7 +45,7 @@ Note this only affects the \code{chat()} method.} } \description{ To use \code{chat_ollama()} first download and install -\href{https://ollama.com}{ollama}. Then install some models from the command line, +\href{https://ollama.com}{Ollama}. Then install some models from the command line, e.g. with \verb{ollama pull llama3.1} or \verb{ollama pull gemma2}. This function is a lightweight wrapper around \code{\link[=chat_openai]{chat_openai()}} with diff --git a/man/chat_openai.Rd b/man/chat_openai.Rd index 6658cf4..3fdcb1f 100644 --- a/man/chat_openai.Rd +++ b/man/chat_openai.Rd @@ -52,14 +52,16 @@ Note this only affects the \code{chat()} method.} A \link{Chat} object. } \description{ -\href{https://openai.com/o1/}{OpenAI} provides a number of chat based models under -the \href{https://chatgpt.com}{ChatGPT} moniker. +\href{https://openai.com/}{OpenAI} provides a number of chat-based models, +mostly under the \href{https://chat.openai.com/}{ChatGPT} brand. +Note that a ChatGPT Plus membership does not grant access to the API. +You will need to sign up for a developer account (and pay for it) at the +\href{https://platform.openai.com}{developer platform}. -Note that a ChatGPT Plus membership does not give you the ability to call -models via the API. You will need to go to the -\href{https://platform.openai.com}{developer platform} to sign up -(and pay for) a developer account that will give you an API key that -you can use with this package. +For authentication, we recommend saving your +\href{https://platform.openai.com/account/api-keys}{API key} to +the \code{OPENAI_API_KEY} environment variable in your \code{.Renviron} file. +You can easily edit this file by calling \code{usethis::edit_r_environ()}. } \examples{ \dontshow{if (elmer:::openai_key_exists()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} diff --git a/man/create_tool_def.Rd b/man/create_tool_def.Rd index 7686776..336d5ac 100644 --- a/man/create_tool_def.Rd +++ b/man/create_tool_def.Rd @@ -11,7 +11,7 @@ create_tool_def(topic, model = "gpt-4o", echo = interactive(), verbose = FALSE) metadata for. Can also be an expression of the form \code{pkg::fun}.} \item{model}{The OpenAI model to use for generating the metadata. Defaults to -"gpt-4o", which is highly recommended over "gpt-4o-mini".} +"gpt-4o".} \item{echo}{Emit the registration code to the console. Defaults to \code{TRUE} in interactive sessions.} diff --git a/tests/testthat/_snaps/provider-openai.md b/tests/testthat/_snaps/provider-openai.md index 2b34479..1b18b88 100644 --- a/tests/testthat/_snaps/provider-openai.md +++ b/tests/testthat/_snaps/provider-openai.md @@ -3,7 +3,7 @@ Code . <- chat_openai() Message - Using model = "gpt-4o-mini". + Using model = "gpt-4o". # all tool variations work diff --git a/tests/testthat/_snaps/turns.md b/tests/testthat/_snaps/turns.md index fe3647c..a8b5ea5 100644 --- a/tests/testthat/_snaps/turns.md +++ b/tests/testthat/_snaps/turns.md @@ -14,7 +14,7 @@ normalize_turns(list(sys_msg, user_msg), 1) Condition Error: - ! `system_prompt` must be a single string or `NULL`, not the number 1. + ! `system_prompt` must be a character vector or `NULL`, not the number 1. Code normalize_turns(list(sys_msg, user_msg), "foo2") Condition diff --git a/tests/testthat/test-content.R b/tests/testthat/test-content.R index 7d2be6f..e9b4317 100644 --- a/tests/testthat/test-content.R +++ b/tests/testthat/test-content.R @@ -10,6 +10,6 @@ test_that("invalid inputs give useful errors", { test_that("can create content from a vector", { expect_equal( as_content(c("a", "b")), - ContentText("a\nb") + ContentText("a\n\nb") ) }) diff --git a/tests/testthat/test-provider-openai.R b/tests/testthat/test-provider-openai.R index d7081f4..9a9b2a0 100644 --- a/tests/testthat/test-provider-openai.R +++ b/tests/testthat/test-provider-openai.R @@ -1,14 +1,14 @@ # Getting started -------------------------------------------------------- test_that("can make simple request", { - chat <- chat_openai("Be as terse as possible; no punctuation") + chat <- chat_openai("Be as terse as possible; no punctuation", model = "gpt-4o-mini") resp <- chat$chat("What is 1 + 1?", echo = FALSE) expect_match(resp, "2") expect_equal(chat$last_turn()@tokens, c(27, 1)) }) test_that("can make simple streaming request", { - chat <- chat_openai("Be as terse as possible; no punctuation") + chat <- chat_openai("Be as terse as possible; no punctuation", model = "gpt-4o-mini") resp <- coro::collect(chat$stream("What is 1 + 1?")) expect_match(paste0(unlist(resp), collapse = ""), "2") }) @@ -20,14 +20,14 @@ test_that("defaults are reported", { }) test_that("respects turns interface", { - chat_fun <- chat_openai + chat_fun <- function(...) chat_openai(..., model = "gpt-4o-mini") test_turns_system(chat_fun) test_turns_existing(chat_fun) }) test_that("all tool variations work", { - chat_fun <- chat_openai + chat_fun <- function(...) chat_openai(..., model = "gpt-4o-mini") test_tools_simple(chat_fun) test_tools_async(chat_fun) @@ -36,13 +36,13 @@ test_that("all tool variations work", { }) test_that("can extract data", { - chat_fun <- chat_openai + chat_fun <- function(...) chat_openai(..., model = "gpt-4o-mini") test_data_extraction(chat_fun) }) test_that("can use images", { - chat_fun <- chat_openai + chat_fun <- function(...) chat_openai(..., model = "gpt-4o-mini") test_images_inline(chat_fun) test_images_remote(chat_fun) diff --git a/vignettes/elmer.Rmd b/vignettes/elmer.Rmd new file mode 100644 index 0000000..4412bcf --- /dev/null +++ b/vignettes/elmer.Rmd @@ -0,0 +1,138 @@ +--- +title: "Getting started with elmer" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Getting started with elmer} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + eval = elmer:::openai_key_exists(), + comment = "#>" +) +``` + +```{r setup} +library(elmer) +``` + +elmer makes it easy to access to the wealth of large language models (LLMs) from R. But what can you do with those models once you have access to them? This vignette will give you the basic vocabulary you need use an LLM effectively and show a bunch of examples to ignite your creativity. + +In this vignette we'll most ignore how LLMs work, using them as convenient black boxes. If you want to get a sense of how they actually work, we recommend watching Jeremy Howard's posit::conf(2023) keynote: [A hacker's guide to open source LLMs](https://www.youtube.com/watch?v=sYliwvml9Es). + +## Vocabulary + +We'll start by laying out the key vocab that you'll need to understand LLMs. Unfortunately the vocab is all a little entangled: to understand one term you often have to know a little about some of the others. So we'll start with some simple definitions of the most important terms then iteratively go a little deeper. + +It all starts with a **prompt**, which is the text (typically a question or a request) that you send to the LLM. This starts a **conversation**, a sequence of turns that alternate between user prompts and model responses. Inside the model, both the prompt and response are represented by a sequence of **tokens**, which represent either individual words or subcomponents of a word. The tokens are used to compute the cost of using a model and are used to measure the size of the **context**, the combination of the current prompt and any previous prompts and responses used to generate the next response. + +It's useful to make the distinction between providers and models. A **provider** is a web API that gives access to one or more **model**. The distinction is a bit subtle because providers are often synonymous with a model, like OpenAI and GPT, Anthropic and Claude, and Google and Gemini. But other providers, like Ollama, can host many different models, typically open source models like LLaMa and Mistral. Still other providers support both closed and open models, typically by partnering with a company that provides a popular closed model. For example, Azure OpenAI offers both open source models and OpenAI's GPT, while AWS Bedrock offers both open source models and Anthropic's Claude. + +### What is a token? + +An LLM is a _model_, and like all models needs some way to represent its inputs numerically. For LLMs, that means we need some way to convert words to numbers, which is the goal of the **tokenizer**. For example, using the GPT 4o tokenizer, the string "When was R created?" is converted to 5 tokens: 5958 ("When"), 673 (" was"), 460 (" R"), 5371 (" created"), 30 ("?"). As you can see, many simple strings can be represented by a single token. But more complex strings require multiple tokens. For example, the string "counterrevolutionary" is requires 4 tokens: 32128 ("counter"), 264 ("re"), 9477 ("volution"), 815 ("ary"). (You can see how various strings are tokenized at ). + +It's important to have a rough sense of how text is converted to tokens because tokens are used to determine the cost of a model and how much context can be used to predict the next response. On average an English word needs ~1.5 tokens so a page might require 375-400 tokens and a complete book might require 75,000 to 150,000 tokens. Other languages will typically require more tokens, because (in brief) LLMs are trained on data from the internet, which is primarily in English. + +LLMs are priced per million tokens. State of the art models (like GPT-4o or Claude 3.5 sonnet) cost $2-3 per million input tokens, and $10-15 per million output tokens. Cheaper models can cost much less, e.g. GPT-4o mini costs $0.15 per million input tokens and $0.60 per million output tokens. Even $10 of API credit will give you a lot of room for experimentation, particularly with cheaper models, and prices are likely to decline as model performance improves. + +Tokens also used to measure the context window, which is how much text the LLM can use to generate the next response. As we'll discuss shortly, the context length includes the full state of your conversation so far (both your prompts and the model's responses), which means that cost grow rapidly with the number of conversational turns. + +In elmer, you can see how many tokens a conversations has used by printing it, and you can see total usage for a session with `token_usage()`. + +```{r} +chat <- chat_openai(model = "gpt-4o") +. <- chat$chat("Who created R?", echo = FALSE) +chat + +token_usage() +``` + +If you want to learn more about tokens and tokenizers, I'd recommend watching the first 20-30 minutes of [Let's build the GPT Tokenizer](https://www.youtube.com/watch?v=zduSFxRajkE) by Andrej Karpathy. You certainly don't need to learn how to build your own tokenizer, but the intro will give you a bunch of useful background knowledge that will help improve your undersstanding of how LLM's work. + +### What is a conversation? + +A conversation with an LLM takes place through a series of HTTP requests and responses: you send your question to the LLM in a HTTP request, and it sends its reply back in a HTTP response. In other words, a conversation consists of a sequence of a paired turns: you send a prompt then the model returns a response. + +It's important to note that a request includes not only the current user prompt, but every previous user prompt and model response. This means that: + +* The cost of a conversation will grow quadratically with the number of turns: if you want to save money, keep your conversations short. + +* Each response is affected by all previous prompts and responses. This can make a converstion get stuck in a local optimum, so it's generally better to iterate by starting a new conversation with a better prompt rather than having a long back-and-forth. + +* elmer has full control over the conversational history, because it's elmer's responsibility to send the previous turns of the conversation. That makes it possible to start a conversation with one model and finish it with another. + +### What is a prompt? + +The user prompt is the question that you send to the model. There are two other important prompts that underlie the user prompt: + +* The **core system prompt** is unchangeable, set by the model provider, and affects every conversation. You can see what these look like from Anthropic, who [publishes their core system prompts](https://docs.anthropic.com/en/release-notes/system-prompts). + +* The **system prompt** is set when you create a new conversation, and affects every response. It's used to provide additional instructions for the model, shaping its responses to your needs. For example, you might use the system prompt to ask the model to always respond in Spanish or to write dependency-free base R code. You can also use the system prompt to provide the model with information it wouldn't otherwise know about, like the details of your database schema, or the ggplot2 theme and color palette that you most prefer. + +When you use a chat app like ChatGPT or claude.ai you can only iterate on the user prompt. But when you're programming with LLMs, you'll primarily iterate on the system prompt. For example, if you're developing an app that helps a user write tidyverse code, you'd work with the system prompt to ensure that user gets the style of code that you want. + +Writing good prompts is called __prompt design__, is key to effective use of LLMs, and is discussed in more detail in `vignette("prompt-design")`. + +## Example uses + +Now that you've got the basic vocab under your belt, I'm going to fire a bunch of interesting potential use cases at you. Many of these use cases have existing special purpose tools that might be faster, or cheaper, or both. But using an LLM allows you to rapidly prototype an idea, and this is extremely valuable, even if you end up using a more specialised tool for the final product. + +In general, we recommend avoiding LLMs where accuracy is critically important. But that still allows for a wide variety of uses. For example, there are plenty of cases where an 80% correct solution which always requires some manual fiddling might still save you a bunch of time. In other cases, a not-so-good solution can still make it much easier to get started: it's easier to react to something existing rather than starting from scratch with a blank page. + +### Chatbots + +A great place to start with elmer and LLMs to build a chatbot with a custom prompt. Chatbots are a familiar interface to LLMs and easy to create in R with [shinychat](https://github.com/jcheng5/shinychat). And there's surprising amount of value to creating a custom chatbot that has a prompt stuffed with useful knowledge. For example: + +* Maybe you want to help people use your new package. You need a custom prompt because LLMs were trained on data before your package exists. You can create a surprisingly useful tool just by preloading the prompt with your README and other vignettes. This is how the [elmer assistant](https://github.com/jcheng5/elmer-assistant) works. + +* [Shiny assistant](https://shiny.posit.co/blog/posts/shiny-assistant/) helps you build shiny apps (either in R or python) by combining a [prompt](https://github.com/posit-dev/shiny-assistant/blob/main/shinyapp/app_prompt.md) that gives general advice on building apps with a language specific prompt for [R](https://github.com/posit-dev/shiny-assistant/blob/main/shinyapp/app_prompt_r.md) or [python](https://github.com/posit-dev/shiny-assistant/blob/main/shinyapp/app_prompt_python.md). The python prompt is very detailed because there's much less information about Shiny for Python in the existing LLM knowledgebases. + +* If you've written a bunch of documentation for something, you might find that you still get a bunch of questions about it because folks can't easily find exactly what they're looking for. You can reduce the need to answer these questions by creating a chatbot with a prompt that contains your documentation. For example, if you're a teacher, you could create a chatbot that includes your syllabus in the prompt. This eliminates a common class of question where the data necessary to answer the question is already available, but hard to find. + +Another direction is to give the chatbot additional context about your current environment. For example, [aidea](https://github.com/cpsievert/aidea) allows the user to interactively explore a dataset with the help of the LLM. It adds summary statistics about the dataset to the [prompt](https://github.com/cpsievert/aidea/blob/main/inst/app/prompt.md) so that the LLM knows something about your data. Along the same lines, you could imagine writing a chatbot to help with data import that has a prompt which include all the files in the current directory along with their first few lines. + +### Structured data extraction + +LLMs are often very good at extracting structured data from unstructured text, which can give you traction to analyse data that was previously unaccessible. For example: + +* Customer tickets and GitHub issues: you can use LLMs for quick and dirty sentiment analysis, extract any specific products mentioned, and summarise the discussion into a few bullet points. + +* Geocoding: LLMs do a surprisingly good job at geocoding, especially to extract addresses or find the latitute/longitude of cities. There are specialised tools that do this better, but using an LLM makes it easy to get started. + +* Recipes: I've extracted structured data from baking and cocktail recipes. Once you have the data in a structured form you can use your R skills to better understand how recipes vary within a cookbook or you could look for recipes that use the ingredients that you currently have in your kitchen. Or you could use shiny assistant to help make those techniques available to anyone, not just R users. + +Structured data extraction also works well with images. It's not the fastest or cheapest way to extract data but it makes it really easy to prototype ideas. For example, maybe you have a bunch of scanned documents that you want to index. You can convert PDFs to images (e.g. using {imagemagick}) then use structured data extraction to pull out key details. + +Learn more about structured data extractio in `vignette("structure-data")`. + +### Programming + +LLMs can also be useful to solve general programming problems. For example: + +* Write a detailed prompt that explains how update code to use a new version of a package. You could combine this with the rstudioapi package to allow the user to select code, transform it, and then replace the existing text. A comprehensive example of this sort of app is [pal](https://simonpcouch.github.io/pal/), which includes prompts for automatically generating roxygen documentation blocks, updating testthat code to the 3rd edition, and converting `stop()` and `abort()` to use `cli::cli_abort()`. + +* You could automatically look up the documentation for an R function, and include it in the prompt to make it easier to figure out how to use a specific function. + +* You can use LLMs to explain code, or even ask them to [generate a diagram](https://bsky.app/profile/daviddiviny.bsky.social/post/3lb6kjaen4c2u). + +* You can ask an LLM to analyse your code for potential code smells or security issues. You can do this a function at a time, or explore including the entire source code for your package or script in the prompt. + +* You could use [gh](https://gh.r-lib.org) to find unlabelled issues, extract the text, and ask the LLM to figure out what labels might be most appropriate. Or maybe an LLM might be able to help people create better reprexes, or simplify reprexes that are too complicated? + +* I find it useful to have an LLM document a function for me, even knowing that it's likely to be mostly incorrect. Having something to react to make it much easier for me to get started. + +* If you're working with code or data from another programming language, you ask an LLM to convert it to R code for you. Even if it's not perfect, it's still typically much faster than doing everything yourself. + +## Miscellaneous + +To finsish up here are a few other ideas that seem cool but didn't seem to fit in the other categories: + +* Automatically generate alt text for plots, using `content_image_plot()`. + +* Try analysing the text of your statistical report and look for flaws in your statistical reasoning. (e.g. misinterpreting p-values or assuming causation where only correlation exists.) + +* Use your existing company style guide to generate a [brand.yaml](https://posit-dev.github.io/brand-yml/articles/llm-brand-yml-prompt/) specification to automatically style your reports, apps, dashboards, plots to match your corporate style guide. diff --git a/vignettes/prompt-design.Rmd b/vignettes/prompt-design.Rmd new file mode 100644 index 0000000..67afbc1 --- /dev/null +++ b/vignettes/prompt-design.Rmd @@ -0,0 +1,390 @@ +--- +title: "Prompt design" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Prompt design} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + eval = elmer:::openai_key_exists() && elmer:::anthropic_key_exists(), + cache = TRUE +) +options(elmer_seed = 1337) + +# Manually ratchet claude variability way down to hopefully make generated +# code better match my prose. +chat_claude <- function(...) { + elmer::chat_claude(..., api_args = list(temperature = 0)) +} +``` + +This vignette will give you some advice about the logistics of writing prompts with elmer, and then work through two hopefully relevant examples showing how you might write a prompt when generating code and when extracting structured data. If you've never written a prompt before, I'd highly recommend reading [Getting started with AI: Good enough prompting](https://www.oneusefulthing.org/p/getting-started-with-ai-good-enough) by Ethan Mollick. I think his motivating analogy does a really good job of getting you started: + +> Treat AI like an infinitely patient new coworker who forgets everything you tell them each new conversation, one that comes highly recommended but whose actual abilities are not that clear. ... Two parts of this are analogous to working with humans (being new on the job and being a coworker) and two of them are very alien (forgetting everything and being infinitely patient). We should start with where AIs are closest to humans, because that is the key to good-enough prompting + +As well as learning general prompt design skills, it's also a good idea to read any specific advice for the model that you're using. Here are some pointers to the prompt design guides some of the most popular models: + +* [Claude](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) +* [OpenAI](https://platform.openai.com/docs/guides/prompt-engineering) +* [Gemini](https://ai.google.dev/gemini-api/docs/prompting-intro) + +If you have a claude account, you can use its . This prompt generator has been specifically tailored for Claude, but I suspect it will help many other LLMs, or at least give you some ideas as to what else you might want to include in your prompt. + +```{r setup} +#| cache: false +library(elmer) +``` + +## Logistics + +There are a few logistics to discuss first. It's highly likely that you will end up writing long, possibly multi-page prompts, so we want to ensure that you're set up for success. We recommend that you put each prompt in its own file, and write them using markdown. LLMs, like humans, appear to find markdown to be quite readable and markdown allows you to (e.g.) use headers to divide up the prompt into esection, and other tools like itemised lists to enumerate multiple options. You can see some a few examples of this style of prompt here: + +* +* +* +* + +In terms of file names, if you only have one prompt in your project, call it `prompt.md`. If you have multiple prompts, give them informative names like `prompt-extract-metadata.md` or `prompt-summarize-text.md`. If you're writing a package, put your prompt(s) in `inst/prompts`, otherwise it's fine to put them in the root directory of your project. + +Your prompts are going to change over time, so we highly recommend commiting them to a git repo. That will ensure that you can easily see what has changed, and if you accidentally make a mistake you can easily roll back to a known good verison. + +If your prompt includes dynamic data, use `elmer::interpolate_file()` to interpolate it into your prompt. `interpolate_file()` works like [glue](https://glue.tidyverse.org), but uses `{{ }}` instead of `{ }` to make it easier to work with JSON. + +As you iterate on the prompt, it's a good idea to build up a small set of challenging examples that you can regularly re-check with your latest version of the prompt. Currently you'll need to do this by hand, but we hope to eventually also provide tools that help you do this a little more formally. + +Unfortunately, however, you won't see these logistics in action in this vignette, since we're keeping the prompts short and inline to make it easier for you to grok what's going on. + +## Code generation + +Let's explore prompt design for a simple code generation task: + +```{r} +#| cache: false +question <- " + How can I compute the mean and median of variables a, b, c, and so on, + all the way up to z, grouped by age and sex. +" +``` + +I'll use `chat_claude()` for this problem because in our experience it does the best job of generating code. + +### Basic flavour + +When I don't provide a system prompt, I sometimes get answers in a different languages or different styles of R code: + +```{r} +#| label: code-basic +chat <- chat_claude() +chat$chat(question) +``` + +I can ensure that I always get R code in a given style by providing a system prompt: + +```{r} +#| label: code-r +chat <- chat_claude(system_prompt = " + You are an expert R programmer who prefers the tidyverse. +") +chat$chat(question) +``` + +Note that I'm using both a system prompt (which defines the general behaviour) and a user prompt (which askes the specific question). You could put all of the content in the user prompt and get similar results, but I think it's helpful to use both to cleanly divide the general framing of the response from the specific questions that you want to ask. + +Since I'm mostly interested in the code, I ask it to drop the explanation and the sample data: + +```{r} +#| label: code-r-minimal +chat <- chat_claude(system_prompt = " + You are an expert R programmer who prefers the tidyverse. + Just give me the code. I don't want any explanation or sample data. +") +chat$chat(question) +``` + +And of course, if you want a different style of R code, you can of course ask for it: + +```{r} +#| label: code-styles +chat <- chat_claude(system_prompt = " + You are an expert R programmer who prefers data.table. + Just give me the code. I don't want any explanation or sample data. +") +chat$chat(question) + +chat <- chat_claude(system_prompt = " + You are an expert R programmer who prefers base R. + Just give me the code. I don't want any explanation or sample data. +") +chat$chat(question) +``` + +### Be explicit + +If there's something about the output that you don't like, you can try being more explicit about it. For example, the code isn't styled quite how I like, so I provide more details about what I do want: + +```{r} +#| label: code-explicit +chat <- chat_claude(system_prompt = " + You are an expert R programmer who prefers the tidyverse. + Just give me the code. I don't want any explanation or sample data. + + Follow the tidyverse style guide: + * Spread long function calls across multiple lines. + * Where needed, always indent function calls with two spaces. + * Only name arguments that are less commonly used. + * Always use double quotes for strings. + * Use the base pipe, `|>`, not the magrittr pipe `%>%`. +") +chat$chat(question) +``` + +This still doesn't yield exactly the code that I'd write, but it's prety close. + +You could provide a different prompt if you were looking for more explanation of the code: + +```{r} +#| label: code-teacher +chat <- chat_claude(system_prompt = " + You are an expert R teacher. + I am a new R user who wants to improve my programming skills. + Help me understand the code you produce by explaining each function call with + a brief comment. For more complicated calls, add documentation to each + argument. Just give me the code. I don't want any explanation or sample data. +") +chat$chat(question) +``` + +### Teach it about new features + +You can imagine LLMs as being a sort of an average of the internet at a given point in time. That means they will provide popular answers, which will tend to reflect older coding styles (either because the new features aren't in their index, or the older features are so much more popular). So if you want your code to use specific features that are relatively recent, you might need to provide the examples yourself: + +```{r} +#| label: code-new-feature +chat <- chat_claude(system_prompt = " + You are an expert R programmer. + Just give me the code; no explanation in text. + Use the `.by` argument rather than `group_by()`. + dplyr 1.1.0 introduced per-operation grouping with the `.by` argument. + e.g., instead of: + + transactions |> + group_by(company, year) |> + mutate(total = sum(revenue)) + + write this: + transactions |> + mutate( + total = sum(revenue), + .by = c(company, year) + ) +") +chat$chat(question) +``` + +## Structured data + +Providing a rich set of examples is a great way to encourage the output to produce exactly what you want. This is also known as **multi-shot prompting**. Here we'll work through a prompt that I designed to extract structured data from recipes, but the same ideas apply in many other situations. + +### Getting started + +My overall goal is to turn a list of ingredients, like the following, into a nicely structured JSON that I can then analyse in R (e.g. to compute the total weight, scale the recipe up or down, or to convert the units from volumes to weights). + +```{r} +#| cache: false +ingredients <- " + ¾ cup (150g) dark brown sugar + 2 large eggs + ¾ cup (165g) sour cream + ½ cup (113g) unsalted butter, melted + 1 teaspoon vanilla extract + ¾ teaspoon kosher salt + ⅓ cup (80ml) neutral oil + 1½ cups (190g) all-purpose flour + 150g plus 1½ teaspoons sugar +" +``` + +(This isn't the ingredient list for a real recipe but it includes a sampling of styles that I encountered in my project.) + +If you don't have strong feelings about what the data structure should look like, you can start with a very loose prompt and see what you get back. I find this a useful pattern for underspecified problems where a big part of the problem is just defining precisely what problem you want to solve. Seeing the LLM's attempt at a data structure gives me something to immediately react to, rather than having to start from a blank page. + +```{r} +#| label: data-loose +instruct_json <- " + You're an expert baker who also loves JSON. I am going to give you a list of + ingredients and your job is to return nicely structured JSON. Just return the + JSON and no other commentary. +" + +chat <- chat_openai(instruct_json) +chat$chat(ingredients) +``` + +(I don't know if the colour text, "You're an expert baker who also loves JSON", does anything, but I like to think this helps the LLM get into the right mindset of a very nerdy baker.) + +### Provide examples + +This isn't a bad start, but I prefer to cook with weight and I only want to see volumes if weight isn't available so I provide a couple of examples of what I'm looking for. I was pleasantly suprised that I can provide the input and output examples in such a loose format. + +```{r} +#| label: data-examples +instruct_weight <- r"( + Here are some examples of the sort of output I'm looking for: + + ¾ cup (150g) dark brown sugar + {"name": "dark brown sugar", "quantity": 150, "unit": "g"} + + ⅓ cup (80ml) neutral oil + {"name": "neutral oil", "quantity": 80, "unit": "ml"} + + 2 t ground cinnamon + {"name": "ground cinnamon", "quantity": 2, "unit": "teaspoon"} +)" + +chat <- chat_openai(c(instruct_json, instruct_weight)) +chat$chat(ingredients) +``` + +Just providing the examples seems to work remarkably well. But I found it useful to also include description of what the examples are trying to accomplish. I'm not sure if this helps the LLM or not, but it certainly makes it easier for me to understand the organisation of the whole prompt and check that I've covered the key pieces that I'm interested in. + +```{r} +#| cacahe: false +instruct_weight <- r"( + * If an ingredient has both weight and volume, extract only the weight: + + ¾ cup (150g) dark brown sugar + [ + {"name": "dark brown sugar", "quantity": 150, "unit": "g"} + ] + +* If an ingredient only lists a volume, extract that. + + 2 t ground cinnamon + ⅓ cup (80ml) neutral oil + [ + {"name": "ground cinnamon", "quantity": 2, "unit": "teaspoon"}, + {"name": "neutral oil", "quantity": 80, "unit": "ml"} + ] +)" +``` + +This structure also allows me to give the LLMs a hint about how I want multiple ingredients to be stored, i.e. as an JSON array. + +I then iterated on the prompt, looking at the results from different recipes to get a sense of what the LLM was getting wrong. Much of this felt like I waws iterating on my understanding of the problem as I didn't start by knowing exactly how I wanted the data. For example, when I started out I didn't really think about all the various ways that ingredients are specified. For later analysis, I always want quantities to be number, even if they were originally fractions, or the if the units aren't precise (like a pinch). It made me realise that some ingredients are unitless. + +```{r} +#| cache: false +instruct_unit <- r"( +* If the unit uses a fraction, convert it to a decimal. + + ⅓ cup sugar + ½ teaspoon salt + [ + {"name": "dark brown sugar", "quantity": 0.33, "unit": "cup"}, + {"name": "salt", "quantity": 0.5, "unit": "teaspoon"} + ] + +* Quantities are always numbers + + pinch of kosher salt + [ + {"name": "kosher salt", "quantity": 1, "unit": "pinch"} + ] + +* Some ingredients don't have a unit. + 2 eggs + 1 lime + 1 apple + [ + {"name": "egg", "quantity": 2}, + {"name": "lime", "quantity": 1}, + {"name", "apple", "quantity": 1} + ] +)" +``` + +You might want to take a look at the [full prompt](https://gist.github.com/hadley/7688b4dd1e5e97b800c6d7d79e437b48) to see what I ended up with. + +### Structured data + +Now that I've iterated to get a data structure that I like, it seems useful to formalise it and tell the LLM exactly what I'm looking for using structured data. This guarantees that the LLM will only return JSON, the JSON will have the fields that you expect, and that elmer will convert it into an R data structure for you. + +```{r} +#| label: data-structured +type_ingredient <- type_object( + name = type_string("Ingredient name"), + quantity = type_number(), + unit = type_string("Unit of measurement") +) + +type_ingredients <- type_array(items = type_ingredient) + +chat <- chat_openai(c(instruct_json, instruct_weight)) +data <- chat$extract_data(ingredients, type = type_object(ingredients = type_ingredients)) +do.call(rbind, lapply(data$ingredients, as.data.frame)) +``` + +### Capturing raw input + +One thing that I'd do next time would also be to include the raw ingredient name in the output. This doesn't make much difference here, in this simple example, but it makes it much easier to align the input and the output and start to develop automated measures of how well my prompt is doing. + +```{r} +#| cache: false +instruct_weight_input <- r"( + * If an ingredient has both weight and volume, extract only the weight: + + ¾ cup (150g) dark brown sugar + [ + {"name": "dark brown sugar", "quantity": 150, "unit": "g", "input": "¾ cup (150g) dark brown sugar"} + ] + + * If an ingredient only lists a volume, extract that. + + 2 t ground cinnamon + ⅓ cup (80ml) neutral oil + [ + {"name": "ground cinnamon", "quantity": 2, "unit": "teaspoon", "input": "2 t ground cinnamon"}, + {"name": "neutral oil", "quantity": 80, "unit": "ml", "input": "⅓ cup (80ml) neutral oil"} + ] +)" +``` + +I think this is particularly important if you're working with even less structured text. For example, imagine you had this text: + +```{r} +#| cache: false +recipe <- r"( + In a large bowl, cream together one cup of softened unsalted butter and a + quarter cup of white sugar until smooth. Beat in an egg and 1 teaspoon of + vanilla extract. Gradually stir in 2 cups of all-purpose flour until the + dough forms. Finally, fold in 1 cup of semisweet chocolate chips. Drop + spoonfuls of dough onto an ungreased baking sheet and bake at 350°F (175°C) + for 10-12 minutes, or until the edges are lightly browned. Let the cookies + cool on the baking sheet for a few minutes before transferring to a wire + rack to cool completely. Enjoy! +)" +``` + +Including the input text in the output makes it easier to see if it's doing a good job: + +```{r} +#| label: data-unstructured-input +chat <- chat_openai(c(instruct_json, instruct_weight_input)) +chat$chat(recipe) +``` + +When I ran it while writing this vignette, it seems to be working out the weight of the ingredients specified in volume, even though the prompt specifically asks it not to do that. This may suggest I need to broaden my examples. + +## Token usage + +```{r} +#| label: usage +#| type: asis +#| echo: false +knitr::kable(token_usage()) +``` diff --git a/vignettes/prompt-design_cache/html/__packages b/vignettes/prompt-design_cache/html/__packages new file mode 100644 index 0000000..0a58af8 --- /dev/null +++ b/vignettes/prompt-design_cache/html/__packages @@ -0,0 +1 @@ +elmer diff --git a/vignettes/prompt-design_cache/html/code-basic_23c2af49b209e7f837cf1d5796fe658a.RData b/vignettes/prompt-design_cache/html/code-basic_23c2af49b209e7f837cf1d5796fe658a.RData new file mode 100644 index 0000000000000000000000000000000000000000..4ec1c98d1a33a336de5a83411341e2736762aac0 GIT binary patch literal 629 zcmV-*0*d_~iwFP!0000018q~=PTN2bbxU|?p85$RD5|YGrX*aW7O85XJoOE-$0 z*zS8|QCERo>=5St?NhiQMejA_^&OZR1oLhTK}lEvRtYCb!D(76xJ6Z9 zEJ*``$yMECIs6JC<7`0*#7n@8ETmnh>3(c;`|?i8#%-ADqRJpy$fX;QMXXRQeK!!S z%99j!PIqhuE%+>6`V1Xf&|p2Mo3dm|ntT+h*4oE0(~*TXep>*ojL-07qaTG(VWdhP 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Data} + %\VignetteIndexEntry{Structured data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -11,14 +11,16 @@ vignette: > knitr::opts_chunk$set( collapse = TRUE, comment = "#>", - eval = elmer:::openai_key_exists() + eval = elmer:::openai_key_exists(), + cache = TRUE ) ``` When using an LLM to extract data from text or images, you can ask the chatbot to nicely format it, in JSON or any other format that you like. This will generally work well most of the time, but there's no gaurantee that you'll actually get the exact format that you want. In particular, if you're trying to get JSON, find that it's typically surrounded in ```` ```json ````, and you'll occassionally get text that isn't actually valid JSON. To avoid these challenges you can use a recent LLM feature: **structured data** (aka structured output). With structured data, you supply a type specification that exactly defines the object structure that you want and the LLM will guarantee that's what you get back. ```{r setup} -library(elmer) +#| cache: false +library(elmer)#| ``` ## Structured data basics @@ -26,6 +28,7 @@ library(elmer) To extract structured data you call the `$extract_data()` method instead of the `$chat()` method. You'll also need to define a type specification that describes the structure of the data that you want (more on that shortly). Here's a simple example that extracts two specific values from a string: ```{r} +#| label: basics-text chat <- chat_openai() chat$extract_data( "My name is Susan and I'm 13 years old", @@ -39,6 +42,7 @@ chat$extract_data( The same basic idea works with images too: ```{r} +#| label: basics-image chat$extract_data( content_image_url("https://www.r-project.org/Rlogo.png"), type = type_object( @@ -57,22 +61,25 @@ To define your desired type specification (also known as a **schema**), you use * **Arrays** represent any number of values of the same type and are created with `type_array()`. You must always supply the `item` argument which specifies the type of each individual element. Arrays of scalars are very similar to R's atomic vectors: ```{r} - logical <- type_array(items = type_boolean()) - integer <- type_array(items = type_integer()) - double <- type_array(items = type_number()) - character <- type_array(items = type_string()) + #| cache: false + type_logical_vector <- type_array(items = type_boolean()) + type_integer_vector <- type_array(items = type_integer()) + type_double_vector <- type_array(items = type_number()) + type_character_vector <- type_array(items = type_string()) ``` You can also have arrays of arrays and arrays of objects, which more closely resemble lists with well defined structures: ```{r} - list_of_integers <- type_array(items = type_array(items = type_integer())) + #| cache: false + list_of_integers <- type_array(items = type_integer_vector) ``` * **Objects** represent a collection of named values and are created with `type_object()`. Objects can contain any number of scalars, arrays, and other objects. They are similar to named lists in R. ```{r} - person <- type_object( + #| cache: false + type_person <- type_object( name = type_string(), age = type_integer(), hobbies = type_array(items = type_string()) @@ -82,7 +89,8 @@ To define your desired type specification (also known as a **schema**), you use As well as the definition of the types, you need to provide the LLM with some information about what you actually want. This is the purpose of the first argument, `description`, which is a string that describes the data that you want. This is a good place to ask nicely for other attributes you'll like the value to possess (e.g. minimum or maximum values, date formats, ...). You aren't guaranteed that these requests will be honoured, but the LLM will usually make a best effort to do so. ```{r} -type_person <- type_object( +#| cache: false +type_type_person <- type_object( "A person", name = type_string("Name"), age = type_integer("Age, in years."), @@ -102,6 +110,7 @@ The following examples are [closely inspired by the Claude documentation](https: ### Example 1: Article summarisation ```{r} +#| label: examples-summarisation text <- readLines(system.file("examples/third-party-testing.txt", package = "elmer")) # url <- "https://www.anthropic.com/news/third-party-testing" # html <- rvest::read_html(url) @@ -129,7 +138,11 @@ str(data) ### Example 2: Named entity recognition ```{r} -text <- "John works at Google in New York. He met with Sarah, the CEO of Acme Inc., last week in San Francisco." +#| label: examples-named-entity +text <- " + John works at Google in New York. He met with Sarah, the CEO of + Acme Inc., last week in San Francisco. +" type_named_entity <- type_object( name = type_string("The extracted entity name."), @@ -148,7 +161,11 @@ str(chat$extract_data(text, type = type_named_entities)) ### Example 3: Sentiment analysis ```{r} -text <- "The product was okay, but the customer service was terrible. I probably won't buy from them again." +#| label: examples-sentiment +text <- " + The product was okay, but the customer service was terrible. I probably + won't buy from them again. +" type_sentiment <- type_object( "Extract the sentiment scores of a given text. Sentiment scores should sum to 1.", @@ -166,6 +183,7 @@ Note that we've asked nicely for the scores to sum 1, and they do in this exampl ### Example 4: Text classification ```{r} +#| label: examples-classification text <- "The new quantum computing breakthrough could revolutionize the tech industry." type_classification <- type_array( @@ -207,6 +225,7 @@ do.call(rbind, lapply(data, as.data.frame)) ### Example 5: Working with unknown keys ```{r, eval = elmer:::anthropic_key_exists()} +#| label: examples-unknown-keys type_characteristics <- type_object( "All characteristics", .additional_properties = TRUE @@ -229,15 +248,14 @@ supports adding arbitrary additional properties. ### Example 6: Extracting data from an image -This example comes from [Dan Nguyen](https://gist.github.com/dannguyen/faaa56cebf30ad51108a9fe4f8db36d8) and you can see other interesting applications at that link. - -The goal is to extract structured data from this screenshot: +This example comes from [Dan Nguyen](https://gist.github.com/dannguyen/faaa56cebf30ad51108a9fe4f8db36d8) and you can see other interesting applications at that link. The goal is to extract structured data from this screenshot: ![A screenshot of schedule A: a table showing assets and "unearned" income](congressional-assets.png) Even without any descriptions, ChatGPT does pretty well: ```{r} +#| label: examples-image type_asset <- type_object( assert_name = type_string(), owner = type_string(), @@ -270,6 +288,7 @@ By default, all components of an object are required. If you want to make some o For example, here the LLM hallucinates a date even though there isn't one in the text: ```{r} +#| label: type-required type_article <- type_object( "Information about an article written in markdown", title = type_string("Article title"), @@ -298,6 +317,7 @@ Note that I've used more of an explict prompt here. For this example, I found th If let the LLM know that the fields are all optional, it'll instead return `NULL` for the missing fields: ```{r} +#| label: type-optional type_article <- type_object( "Information about an article written in markdown", title = type_string("Article title", required = FALSE), @@ -312,6 +332,7 @@ chat$extract_data(prompt, type = type_article) If you want to define a data frame like object, you might be tempted to create a definition similar to what R uses: an object (i.e. a named list) containing multiple vectors (i.e. arrays): ```{r} +#| cache: false type_my_df <- type_object( name = type_array(items = type_string()), age = type_array(items = type_integer()), @@ -323,6 +344,7 @@ type_my_df <- type_object( This however, is not quite right becuase there's no way to specify that each array should have the same length. 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zcmb2|=3oE==I#ec2?+^l35kr8);Op!XJ>TGUdPJJJlPtitle%20How%20tool%20calling%20DOES%20work%0A%0Aparticipant%20openweather.org%0Aactor%20User%0Amaterialdesignicons%20F167A%20Assistant%0A%0AUser-%3EAssistant%3A%20%7B%5Cn%20%20role%3A%20%22user%22%2C%5Cn%20%20content%3A%20%22What's%20the%20weather%20right%20now%20at%20Fenway%20Park%3F%22%2C%5Cn%20%20tools%3A%20%5Bget_current_weather%5D%5Cn%7D%0AAssistant-%3EUser%3A%20CALL%20get_current_weather(%2202215%22)%0AUser-%3Eopenweather.org%3A%20GET%20%2Fweather%2Fcurrent%2Fus%2F02215.json%0Aopenweather.org-%3EUser%3A%20%7B%22temp%22%3A%2065%2C%20%22conditions%22%3A%20%22sunny%22%7D%0AUser-%3EAssistant%3A%20RETURN%20%7B%22temp%22%3A%2065%2C%20%22conditions%22%3A%20%22sunny%22%7D%0AAssistant-%3EUser%3A%20%7B%5Cn%20%20role%3A%20%22assistant%22%2C%5Cn%20%20content%3A%20%22It's%2065%20degrees%20and%20sunny%E2%80%94a%20beautiful%20day!%22%5Cn%7Dopenweather.orgUserAssistantHow tool calling DOES work{  role: "user",  content: "What's the weather right now at Fenway Park?",  tools: [get_current_weather]}CALL get_current_weather("02215")GET /weather/current/us/02215.json{"temp": 65, "conditions": "sunny"}RETURN {"temp": 65, "conditions": "sunny"}{  role: "assistant",  content: "It's 65 degrees and sunny—a beautiful day!"} \ No newline at end of file diff --git a/vignettes/tool-calling-wrong.svg b/vignettes/tool-calling-wrong.svg new file mode 100644 index 0000000..5f05d8c --- /dev/null +++ b/vignettes/tool-calling-wrong.svg @@ -0,0 +1 @@ +title%20How%20tool%20calling%20DOESN%E2%80%99T%20work%0A%0Aactor%20User%0Amaterialdesignicons%20F167A%20Assistant%0Aparticipant%20openweather.org%0A%0AUser-%3EAssistant%3A%20%7B%5Cn%20%20role%3A%20%22user%22%2C%5Cn%20%20content%3A%20%22What's%20the%20weather%20right%20now%20at%20Fenway%20Park%3F%22%2C%5Cn%7D%0AAssistant-%3Eopenweather.org%3A%20GET%20%2Fweather%2Fcurrent%2Fus%2F02215.json%0Aopenweather.org-%3EAssistant%3A%20%7B%22temp%22%3A%2065%2C%20%22conditions%22%3A%20%22sunny%22%7D%0AAssistant-%3EUser%3A%20%7B%5Cn%20%20role%3A%20%22assistant%22%2C%5Cn%20%20content%3A%20%22It's%2065%20degrees%20and%20sunny%E2%80%94a%20beautiful%20day!%22%5Cn%7DUserAssistantopenweather.orgHow tool calling DOESN’T work{  role: "user",  content: "What's the weather right now at Fenway Park?",}GET /weather/current/us/02215.json{"temp": 65, "conditions": "sunny"}{  role: "assistant",  content: "It's 65 degrees and sunny—a beautiful day!"} \ No newline at end of file diff --git a/vignettes/tool-calling.Rmd b/vignettes/tool-calling.Rmd index 4c2e988..d75daec 100644 --- a/vignettes/tool-calling.Rmd +++ b/vignettes/tool-calling.Rmd @@ -1,11 +1,12 @@ --- -title: "Tool calling (a.k.a. function calling)" +title: "Tool/function calling" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{Tool calling (a.k.a. function calling)} + %\VignetteIndexEntry{Tool/function calling} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- + ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, @@ -18,9 +19,17 @@ knitr::opts_chunk$set( 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. +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 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 request that the user's runs that tool, returns the result to the assistant, which uses it to generate the final answer.](tool-calling-right.svg) -*Note that the chat model does not directly execute any external tools!* It only makes requests for the caller to execute them. 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. +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(elmer)