Ollama R Library

@article{Lin2024Aug,
    author = {Lin, Hause and Safi, Tawab},
    title = {{ollamar: An R package for running large language models}},
    journal = {PsyArXiv},
    year = {2024},
    month = aug,
    publisher = {OSF},
    doi = {10.31234/osf.io/zsrg5},
    url = {https://doi.org/10.31234/osf.io/zsrg5}
}

install.packages("ollamar")

# install.packages("remotes")  # run this line if you don't have the remotes library
remotes::install_github("hauselin/ollamar")

library(ollamar)

test_connection()  # test connection to Ollama server
# if you see Ollama local server running, it's working

# 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>
#' POST http://127.0.0.1:11434/api/generate  # endpoint
#' 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

pull("llama3.1")  # download a model (the equivalent bash code: ollama run llama3.1)
list_models()  # verify you've pulled/downloaded the model

list_models()  # see the models you've pulled/downloaded
delete("all-minilm:latest")  # returns a httr2 response object

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')

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")

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

embed("llama3.1", "Hello, how are you?")

# don't normalize embeddings
embed("llama3.1", "Hello, how are you?", normalize = 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)

test_connection()
# Ollama local server not running or wrong server.
# Error in `httr2::req_perform()` at ollamar/R/test_connection.R:18:9:

resp <- list_models(output = "resp")  # returns a httr2 response object
# <httr2_response>
# GET http://127.0.0.1:11434/api/tags
# Status: 200 OK
# Content-Type: application/json
# Body: In memory (5401 bytes)

# 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")

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
)

# 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
)

# 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

prompt <- "Tell me a 10-word story"
req <- generate("llama3.1", prompt, output = "req")  # returns a httr2_request object
# <httr2_request>
# POST http://127.0.0.1:11434/api/generate
# Headers:
# • content_type: 'application/json'
# • accept: 'application/json'
# • user_agent: 'ollama-r/1.1.1 (aarch64-apple-darwin20) R/4.4.0'
# Body: json encoded data

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."    

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'."

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