## ----eval=FALSE---------------------------------------------------------------
# install.packages("ollamar")
## ----eval=FALSE---------------------------------------------------------------
# # install.packages("remotes") # run this line if you don't have the remotes library
# remotes::install_github("hauselin/ollamar")
## ----eval=FALSE---------------------------------------------------------------
# library(ollamar)
#
# test_connection() # test connection to Ollama server
# # if you see "Ollama local server not running or wrong server," Ollama app/server isn't running
#
# # generate a response/text based on a prompt; returns an httr2 response by default
# resp <- generate("llama3.1", "tell me a 5-word story")
# resp
#
# #' interpret httr2 response object
# #' <httr2_response>
# #' Status: 200 OK # if successful, status code should be 200 OK
# #' Content-Type: application/json
# #' Body: In memory (414 bytes)
#
# # get just the text from the response object
# resp_process(resp, "text")
# # get the text as a tibble dataframe
# resp_process(resp, "df")
#
# # alternatively, specify the output type when calling the function initially
# txt <- generate("llama3.1", "tell me a 5-word story", output = "text")
#
# # list available models (models you've pulled/downloaded)
# list_models()
# name size parameter_size quantization_level modified
# 1 codegemma:7b 5 GB 9B Q4_0 2024-07-27T23:44:10
# 2 llama3.1:latest 4.7 GB 8.0B Q4_0 2024-07-31T07:44:33
## ----eval=FALSE---------------------------------------------------------------
# pull("llama3.1") # download a model (equivalent bash code: ollama run llama3.1)
# list_models() # verify you've pulled/downloaded the model
## ----eval=FALSE---------------------------------------------------------------
# list_models() # see the models you've pulled/downloaded
# delete("all-minilm:latest") # returns a httr2 response object
## ----eval=FALSE---------------------------------------------------------------
# resp <- generate("llama3.1", "Tomorrow is a...") # return httr2 response object by default
# resp
#
# resp_process(resp, "text") # process the response to return text/vector output
#
# generate("llama3.1", "Tomorrow is a...", output = "text") # directly return text/vector output
# generate("llama3.1", "Tomorrow is a...", stream = TRUE) # return httr2 response object and stream output
# generate("llama3.1", "Tomorrow is a...", output = "df", stream = TRUE)
#
# # image prompt
# # use a vision/multi-modal model
# generate("benzie/llava-phi-3", "What is in the image?", images = "image.png", output = 'text')
## ----eval=FALSE---------------------------------------------------------------
# messages <- create_message("what is the capital of australia") # default role is user
# resp <- chat("llama3.1", messages) # default returns httr2 response object
# resp # <httr2_response>
# resp_process(resp, "text") # process the response to return text/vector output
#
# # specify output type when calling the function
# chat("llama3.1", messages, output = "text") # text vector
# chat("llama3.1", messages, output = "df") # data frame/tibble
# chat("llama3.1", messages, output = "jsonlist") # list
# chat("llama3.1", messages, output = "raw") # raw string
# chat("llama3.1", messages, stream = TRUE) # stream output and return httr2 response object
#
# # create chat history
# messages <- create_messages(
# create_message("end all your sentences with !!!", role = "system"),
# create_message("Hello!"), # default role is user
# create_message("Hi, how can I help you?!!!", role = "assistant"),
# create_message("What is the capital of Australia?"),
# create_message("Canberra!!!", role = "assistant"),
# create_message("what is your name?")
# )
# cat(chat("llama3.1", messages, output = "text")) # print the formatted output
#
# # image prompt
# messages <- create_message("What is in the image?", images = "image.png")
# # use a vision/multi-modal model
# chat("benzie/llava-phi-3", messages, output = "text")
## ----eval=FALSE---------------------------------------------------------------
# messages <- create_message("Tell me a 1-paragraph story.")
#
# # use "llama3.1" model, provide list of messages, return text/vector output, and stream the output
# chat("llama3.1", messages, output = "text", stream = TRUE)
# # chat(model = "llama3.1", messages = messages, output = "text", stream = TRUE) # same as above
## ----eval=FALSE---------------------------------------------------------------
# list( # main list containing all the messages
# list(role = "user", content = "Hello!"), # first message as a list
# list(role = "assistant", content = "Hi! How are you?") # second message as a list
# )
## ----eval=FALSE---------------------------------------------------------------
# # create a chat history with one message
# messages <- create_message(content = "Hi! How are you? (1ST MESSAGE)", role = "assistant")
# # or simply, messages <- create_message("Hi! How are you?", "assistant")
# messages[[1]] # get 1st message
#
# # append (add to the end) a new message to the existing messages
# messages <- append_message("I'm good. How are you? (2ND MESSAGE)", "user", messages)
# messages[[1]] # get 1st message
# messages[[2]] # get 2nd message (newly added message)
#
# # prepend (add to the beginning) a new message to the existing messages
# messages <- prepend_message("I'm good. How are you? (0TH MESSAGE)", "user", messages)
# messages[[1]] # get 0th message (newly added message)
# messages[[2]] # get 1st message
# messages[[3]] # get 2nd message
#
# # insert a new message at a specific index/position (2nd position in the example below)
# # by default, the message is inserted at the end of the existing messages (position -1 is the end/default)
# messages <- insert_message("I'm good. How are you? (BETWEEN 0 and 1 MESSAGE)", "user", messages, 2)
# messages[[1]] # get 0th message
# messages[[2]] # get between 0 and 1 message (newly added message)
# messages[[3]] # get 1st message
# messages[[4]] # get 2nd message
#
# # delete a message at a specific index/position (2nd position in the example below)
# messages <- delete_message(messages, 2)
#
# # create a chat history with multiple messages
# messages <- create_messages(
# create_message("You're a knowledgeable tour guide.", role = "system"),
# create_message("What is the capital of Australia?") # default role is user
# )
## ----eval=FALSE---------------------------------------------------------------
# # create a list of messages
# messages <- create_messages(
# create_message("You're a knowledgeable tour guide.", role = "system"),
# create_message("What is the capital of Australia?")
# )
#
# # convert to dataframe
# df <- dplyr::bind_rows(messages) # with dplyr library
# df <- data.table::rbindlist(messages) # with data.table library
#
# # convert dataframe to list with apply, purrr functions
# apply(df, 1, as.list) # convert each row to a list with base R apply
# purrr::transpose(df) # with purrr library
## ----eval=FALSE---------------------------------------------------------------
# embed("llama3.1", "Hello, how are you?")
#
# # don't normalize embeddings
# embed("llama3.1", "Hello, how are you?", normalize = FALSE)
## ----eval=FALSE---------------------------------------------------------------
# # get embeddings for similar prompts
# e1 <- embed("llama3.1", "Hello, how are you?")
# e2 <- embed("llama3.1", "Hi, how are you?")
#
# # compute cosine similarity
# sum(e1 * e2) # not equals to 1
# sum(e1 * e1) # 1 (identical vectors/embeddings)
#
# # non-normalized embeddings
# e3 <- embed("llama3.1", "Hello, how are you?", normalize = FALSE)
# e4 <- embed("llama3.1", "Hi, how are you?", normalize = FALSE)
## ----eval=FALSE---------------------------------------------------------------
# resp <- list_models(output = "resp") # returns a httr2 response object
# # <httr2_response>
# # Status: 200 OK
# # Content-Type: application/json
#
# # process the httr2 response object with the resp_process() function
# resp_process(resp, "df")
# # or list_models(output = "df")
# resp_process(resp, "jsonlist") # list
# # or list_models(output = "jsonlist")
# resp_process(resp, "raw") # raw string
# # or list_models(output = "raw")
# resp_process(resp, "resp") # returns the input httr2 response object
# # or list_models() or list_models("resp")
# resp_process(resp, "text") # text vector
# # or list_models("text")
## ----eval=FALSE---------------------------------------------------------------
# add_two_numbers <- function(x, y) {
# return(x + y)
# }
#
# multiply_two_numbers <- function(a, b) {
# return(a * b)
# }
#
# # each tool needs to be in a list
# tool1 <- list(type = "function",
# "function" = list(
# name = "add_two_numbers", # function name
# description = "add two numbers",
# parameters = list(
# type = "object",
# required = list("x", "y"), # function parameters
# properties = list(
# x = list(class = "numeric", description = "first number"),
# y = list(class = "numeric", description = "second number")))
# )
# )
#
# tool2 <- list(type = "function",
# "function" = list(
# name = "multiply_two_numbers", # function name
# description = "multiply two numbers",
# parameters = list(
# type = "object",
# required = list("a", "b"), # function parameters
# properties = list(
# x = list(class = "numeric", description = "first number"),
# y = list(class = "numeric", description = "second number")))
# )
# )
## ----eval=FALSE---------------------------------------------------------------
# msg <- create_message("what is three plus one?")
# resp <- chat("llama3.1", msg, tools = list(tool1), output = "tools")
# tool <- resp[[1]] # get the first tool/function
#
# # call the tool function with arguments: add_two_numbers(3, 1)
# do.call(tool$name, tool$arguments)
## ----eval=FALSE---------------------------------------------------------------
# msg <- create_message("what is three multiplied by four?")
# resp <- chat("llama3.1", msg, tools = list(tool1, tool2), output = "tools")
# tool <- resp[[1]] # get the first tool/function
#
# # call the tool function with arguments: multiply_two_numbers(3, 4)
# do.call(tool$name, tool$arguments)
## ----eval=FALSE---------------------------------------------------------------
# msg <- create_message("add three plus four. then multiply by ten")
# resp <- chat("llama3.1", msg, tools = list(tool1, tool2), output = "tools")
#
# # first tool/function: add_two_numbers(3, 4)
# do.call(resp[[1]]$name, resp[[1]]$arguments) # 7
# # second tool/function: multiply_two_numbers(7, 10)
# do.call(resp[[2]]$name, resp[[2]]$arguments) # 70
## ----eval=FALSE---------------------------------------------------------------
# # define a JSON schema as a list to constrain a model's output
# format <- list(
# type = "object",
# properties = list(
# name = list(type = "string"),
# capital = list(type = "string"),
# languages = list(type = "array",
# items = list(type = "string")
# )
# ),
# required = list("name", "capital", "languages")
# )
#
# generate("llama3.1", "tell me about Canada", output = "structured", format = format)
#
# msg <- create_message("tell me about Canada")
# chat("llama3.1", msg, format = format, output = "structured")
## ----eval=FALSE---------------------------------------------------------------
# prompt <- "Tell me a 10-word story"
# req <- generate("llama3.1", prompt, output = "req") # returns a httr2_request object
## ----eval=FALSE---------------------------------------------------------------
# library(httr2)
#
# prompt <- "Tell me a 5-word story"
#
# # create 5 httr2_request objects that generate a response to the same prompt
# reqs <- lapply(1:5, function(r) generate("llama3.1", prompt, output = "req"))
#
# # make parallel requests and get response
# resps <- req_perform_parallel(reqs) # list of httr2_request objects
#
# # process the responses
# sapply(resps, resp_process, "text") # get responses as text
# # [1] "She found him in Paris." "She found the key upstairs."
# # [3] "She found her long-lost sister." "She found love on Mars."
# # [5] "She found the diamond ring."
#
## ----eval=FALSE---------------------------------------------------------------
# library(httr2)
# library(glue)
# library(dplyr)
#
# # text to classify
# texts <- c('I love this product', 'I hate this product', 'I am neutral about this product')
#
# # create httr2_request objects for each text, using the same system prompt
# reqs <- lapply(texts, function(text) {
# prompt <- glue("Your only task/role is to evaluate the sentiment of product reviews, and your response should be one of the following:'positive', 'negative', or 'other'. Product review: {text}")
# generate("llama3.1", prompt, output = "req")
# })
#
# # make parallel requests and get response
# resps <- req_perform_parallel(reqs) # list of httr2_request objects
#
# # process the responses
# sapply(resps, resp_process, "text") # get responses as text
# # [1] "Positive" "Negative."
# # [3] "'neutral' translates to... 'other'."
#
#
## ----eval=FALSE---------------------------------------------------------------
# library(httr2)
# library(dplyr)
#
# # text to classify
# texts <- c('I love this product', 'I hate this product', 'I am neutral about this product')
#
# # create system prompt
# chat_history <- create_message("Your only task/role is to evaluate the sentiment of product reviews provided by the user. Your response should simply be 'positive', 'negative', or 'other'.", "system")
#
# # create httr2_request objects for each text, using the same system prompt
# reqs <- lapply(texts, function(text) {
# messages <- append_message(text, "user", chat_history)
# chat("llama3.1", messages, output = "req")
# })
#
# # make parallel requests and get response
# resps <- req_perform_parallel(reqs) # list of httr2_request objects
#
# # process the responses
# bind_rows(lapply(resps, resp_process, "df")) # get responses as dataframes
# # # A tibble: 3 × 4
# # model role content created_at
# # <chr> <chr> <chr> <chr>
# # 1 llama3.1 assistant Positive 2024-08-05T17:54:27.758618Z
# # 2 llama3.1 assistant negative 2024-08-05T17:54:27.657525Z
# # 3 llama3.1 assistant other 2024-08-05T17:54:27.657067Z
#