Nothing
tests/testthat/test-tabscreen_ollama.r
In AIscreenR: AI Screening Tools in R for Systematic Reviewing
skip <- TRUE
skip_github_action <- FALSE
test_that(".ollama_engine return errors correctly", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
tools_choice_name <- list(
type = "function",
"function" = list(
name = "inclusion_decision_simple"
)
)
# Specifying unknown model
body <- list(
model = "llama-7",
messages = list(list(
role = "user",
content = question
)),
tools = tools_simple,
tool_choice = tools_choice_name,
top_p = 1
)
expect_error(
.ollama_engine(
body = body,
RPM = 10000,
time_inf = TRUE,
max_t = 4,
max_s = 10,
back = NULL,
aft = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
),
regexp = "404|Not Found|Error"
)
# Specifying ineligible role
body <- list(
model = "llama3-8b-8192",
messages = list(list(
role = "use",
content = question
)),
tools = tools_simple,
tool_choice = tools_choice_name,
top_p = 1
)
expect_error(
.ollama_engine(
body = body,
RPM = 10000,
time_inf = TRUE,
max_t = 4,
max_s = 10,
back = NULL,
aft = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
),
regexp = "HTTP 404 Not Found"
)
})
test_that(".rep_ollama_engine controls errors correctly", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
iterations <- 3
# Ineligible role
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama3-8b-8192",
topp = 1,
role_gpt = "use",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(all(stringr::str_detect(res$decision_gpt, "400|Error")))
# Ineligible api key
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama3-8b-8192",
topp = 1,
role_gpt = "user",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(all(stringr::str_detect(res$decision_gpt, "401|Error")))
# Ineligible model
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama-min",
topp = 1,
role_gpt = "user",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(any(stringr::str_detect(res$decision_gpt, "404|400|Error")))
})
paths <- system.file("extdata", "test_prompts.rds", package = "AIscreenR")
prompts <- readRDS(file = paths)
prompt <- prompts[1]
prompt2 <- prompts[2]
models <- c("llama-3.1-8b-instant", "openai/gpt-oss-20b")
reps <- c(2, 1)
rpm <- c(30, 30)
test_that("tabscreen_ollama() overinclusive chooses default tools", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
# Use closed local port to avoid dependency on a running Ollama server.
test_api <- "http://127.0.0.1:1/api/chat"
# Binary default tools when overinclusive = FALSE
res_bin <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = FALSE,
decision_description = FALSE,
messages = FALSE,
progress = FALSE
)
args_bin <- attr(res_bin, "arg_list")
expect_identical(args_bin$tool_choice, "inclusion_decision_simple_binary")
expect_identical(args_bin$tools[[1]]$`function`$name, "inclusion_decision_simple_binary")
res_bin_det <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = FALSE,
decision_description = TRUE,
messages = FALSE,
progress = FALSE
)
args_bin_det <- attr(res_bin_det, "arg_list")
expect_identical(args_bin_det$tool_choice, "inclusion_decision_binary")
expect_identical(args_bin_det$tools[[1]]$`function`$name, "inclusion_decision_binary")
# Over-inclusive tools (supports 1.1) when overinclusive = TRUE
res_over <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = TRUE,
decision_description = FALSE,
messages = FALSE,
progress = FALSE
)
args_over <- attr(res_over, "arg_list")
expect_identical(args_over$tool_choice, "inclusion_decision_simple")
expect_identical(args_over$tools[[1]]$`function`$name, "inclusion_decision_simple")
res_over_det <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = TRUE,
decision_description = TRUE,
messages = FALSE,
progress = FALSE
)
args_over_det <- attr(res_over_det, "arg_list")
expect_identical(args_over_det$tool_choice, "inclusion_decision")
expect_identical(args_over_det$tools[[1]]$`function`$name, "inclusion_decision")
})
test_that("tabscreen_ollama() validates missing or empty model", {
test_api <- "http://127.0.0.1:1/api/chat"
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = "",
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = NA_character_,
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = character(0),
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
})
test_that("tabscreen_ollama() normalizes common Ollama base URLs", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
# Use closed local port to avoid dependency on a running Ollama server.
test_api_base <- "http://127.0.0.1:1"
test_api_root <- "http://127.0.0.1:1/api"
res_base <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api_base,
model = "llama3-8b-8192",
messages = FALSE,
progress = FALSE
)
res_root <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api_root,
model = "llama3-8b-8192",
messages = FALSE,
progress = FALSE
)
expect_identical(attr(res_base, "arg_list")$api_url, "http://127.0.0.1:1/api/chat")
expect_identical(attr(res_root, "arg_list")$api_url, "http://127.0.0.1:1/api/chat")
})
future::plan(future::multisession)
test_that("tabscreen_ollama() works with single parameter values.",{
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
max_tries = 2
)
)
expect_true(is.numeric(test_obj$answer_data$decision_binary))
expect_equal(nrow(test_obj$answer_data), 1L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = 1
)
)
expect_equal(nrow(test_obj$answer_data), 2L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = c(prompt, prompt2),
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192"),
reps = 1
)
)
expect_equal(nrow(test_obj$answer_data), 2L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
})
test_that("tabscreen_ollama() works with multiple parameter values.",{
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 3L)
expect_equal(nrow(test_obj$answer_data_aggregated), 2L)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_gt(nrow(test_obj$answer_data), nrow(test_obj$answer_data_aggregated))
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 6L)
expect_equal(nrow(test_obj$answer_data_aggregated), 4L)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1),
top_p = c(0.2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 12L)
expect_equal(nrow(test_obj$answer_data_aggregated), 2*2*2)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
})
test_that("tabscreen_ollama() works with detailed functions and ... .", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
decision_description = TRUE
)
) |>
suppressMessages()
expect_true(is.character(test_obj$answer_data$detailed_description))
expect_null(test_obj$answer_data_aggregated)
# Working with temperature, i.e. ... calls
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 3,
decision_description = TRUE,
progress = FALSE,
temperature = 0.001
)
) |>
suppressMessages()
n_distinct_detailed_answers <- test_obj$answer_data$detailed_description |> dplyr::n_distinct()
expect_lt(n_distinct_detailed_answers, 3)
})
test_that("Message behavior.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
messages = FALSE,
progress = FALSE
)
)
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
messages = FALSE,
progress = FALSE
)
)
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "gemma-7b-it",
reps = 2
)
)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
decision_description = TRUE
)
) |>
suppressMessages()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "gemma-7b-it",
reps = 1,
decision_description = TRUE
),
paste0(
"* Be aware that getting detailed reponses ",
"will substantially increase the time of the screening."
)
) |>
suppressMessages()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[148,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3.2:latest",
),
paste0(
"* Consider removing references without abstracts ",
"since these can distort the accuracy of the screening."
)
) |>
suppressMessages()
})
test_that("tabscreen_ollama() expected errors.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1, 1)
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1),
rpm = c(30, 30, 30)
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192-x", "gemma-7b-it"),
reps = reps,
rpm = rpm
)
)
# With detailed description functions
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1, 1),
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1),
rpm = c(30, 30, 30),
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192-x", "gemma-7b-it"),
reps = reps,
rpm = rpm,
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
incl_cutoff_upper = 0.4,
incl_cutoff_lower = 0.5
)
)
})
future::plan(future::sequential)
# Test parallel
test_that("That parallel processing works.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
future::plan(future::multisession, workers = future::availableCores())
expect_message(
tm_par <- system.time(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 10
)
)
)
future::plan(future::sequential)
expect_lt(tm_par[["elapsed"]], sum(test_obj$answer_data$run_time))
ollama_answers <-
test_obj$answer_data |>
filter(!is.na(decision_gpt)) |>
pull(decision_gpt)
# Testing function call work
ollama_answers |>
stringr::str_detect("1|0|rror") |>
unique() |>
expect_true()
})
test_that("Print and class expectation for ollama object.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
max_tries = 2
)
)
# class check
expect_s3_class(random_name, "gpt")
expect_s3_class(random_name, "list")
expect_output(print(random_name), "result_object\\$answer_data")
expect_output(print.gpt(random_name), "result_object\\$answer_data")
expect_false("answer_data_aggregated" %in% names(random_name))
expect_false("price_dollar" %in% names(random_name))
print_out1 <- paste0(
"Find the final result dataset via result_object$answer_data"
)
expect_output(print(random_name), print_out1)
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = c(prompt),
studyid = studyid, # indicate the variable with the studyid in the data
title = title, # indicate the variable with the titles in the data
abstract = abstract,
)
) |>
suppressMessages()
expect_identical(names(random_name$answer_data), names(random_name$error_data))
expect_output(print(random_name), "result_object\\$answer_data")
print_out2 <- paste0(
"Find the final result dataset via result_object$answer_data"
)
expect_output(print(random_name), print_out2)
#############
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
)
)
# class check
expect_s3_class(random_name, "gpt")
expect_s3_class(random_name, "list")
expect_output(print(random_name), "result_object\\$answer_data")
expect_output(print.gpt(random_name), "result_object\\$answer_data")
expect_false("price_dollar" %in% names(random_name))
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = c(prompt),
studyid = studyid, # indicate the variable with the studyid in the data
title = title, # indicate the variable with the titles in the data
abstract = abstract
)
) |>
suppressMessages()
expect_output(print(random_name), "result_object\\$answer_data")
expect_false("price_dollar" %in% names(random_name))
})
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skip <- TRUE
skip_github_action <- FALSE
test_that(".ollama_engine return errors correctly", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
tools_choice_name <- list(
type = "function",
"function" = list(
name = "inclusion_decision_simple"
)
)
# Specifying unknown model
body <- list(
model = "llama-7",
messages = list(list(
role = "user",
content = question
)),
tools = tools_simple,
tool_choice = tools_choice_name,
top_p = 1
)
expect_error(
.ollama_engine(
body = body,
RPM = 10000,
time_inf = TRUE,
max_t = 4,
max_s = 10,
back = NULL,
aft = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
),
regexp = "404|Not Found|Error"
)
# Specifying ineligible role
body <- list(
model = "llama3-8b-8192",
messages = list(list(
role = "use",
content = question
)),
tools = tools_simple,
tool_choice = tools_choice_name,
top_p = 1
)
expect_error(
.ollama_engine(
body = body,
RPM = 10000,
time_inf = TRUE,
max_t = 4,
max_s = 10,
back = NULL,
aft = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
),
regexp = "HTTP 404 Not Found"
)
})
test_that(".rep_ollama_engine controls errors correctly", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
iterations <- 3
# Ineligible role
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama3-8b-8192",
topp = 1,
role_gpt = "use",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(all(stringr::str_detect(res$decision_gpt, "400|Error")))
# Ineligible api key
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama3-8b-8192",
topp = 1,
role_gpt = "user",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(all(stringr::str_detect(res$decision_gpt, "401|Error")))
# Ineligible model
res <- .rep_ollama_engine(
question = question,
model_gpt = "llama-min",
topp = 1,
role_gpt = "user",
tool = tools_simple,
t_choice = "inclusion_decision_simple",
iterations = iterations,
req_per_min = 10000,
seeds = NULL,
time_inf = T,
max_t = 4,
max_s = 10,
ba = NULL,
af = NULL,
endpoint_url = "http://127.0.0.1:11434/api/chat"
)
expect_equal(ncol(res), 4)
expect_equal(nrow(res), iterations)
expect_equal(max(res$n), iterations)
expect_true(any(stringr::str_detect(res$decision_gpt, "404|400|Error")))
})
paths <- system.file("extdata", "test_prompts.rds", package = "AIscreenR")
prompts <- readRDS(file = paths)
prompt <- prompts[1]
prompt2 <- prompts[2]
models <- c("llama-3.1-8b-instant", "openai/gpt-oss-20b")
reps <- c(2, 1)
rpm <- c(30, 30)
test_that("tabscreen_ollama() overinclusive chooses default tools", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
# Use closed local port to avoid dependency on a running Ollama server.
test_api <- "http://127.0.0.1:1/api/chat"
# Binary default tools when overinclusive = FALSE
res_bin <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = FALSE,
decision_description = FALSE,
messages = FALSE,
progress = FALSE
)
args_bin <- attr(res_bin, "arg_list")
expect_identical(args_bin$tool_choice, "inclusion_decision_simple_binary")
expect_identical(args_bin$tools[[1]]$`function`$name, "inclusion_decision_simple_binary")
res_bin_det <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = FALSE,
decision_description = TRUE,
messages = FALSE,
progress = FALSE
)
args_bin_det <- attr(res_bin_det, "arg_list")
expect_identical(args_bin_det$tool_choice, "inclusion_decision_binary")
expect_identical(args_bin_det$tools[[1]]$`function`$name, "inclusion_decision_binary")
# Over-inclusive tools (supports 1.1) when overinclusive = TRUE
res_over <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = TRUE,
decision_description = FALSE,
messages = FALSE,
progress = FALSE
)
args_over <- attr(res_over, "arg_list")
expect_identical(args_over$tool_choice, "inclusion_decision_simple")
expect_identical(args_over$tools[[1]]$`function`$name, "inclusion_decision_simple")
res_over_det <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api,
model = "llama3-8b-8192",
overinclusive = TRUE,
decision_description = TRUE,
messages = FALSE,
progress = FALSE
)
args_over_det <- attr(res_over_det, "arg_list")
expect_identical(args_over_det$tool_choice, "inclusion_decision")
expect_identical(args_over_det$tools[[1]]$`function`$name, "inclusion_decision")
})
test_that("tabscreen_ollama() validates missing or empty model", {
test_api <- "http://127.0.0.1:1/api/chat"
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = "",
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = NA_character_,
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
expect_error(
tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
api_url = test_api,
model = character(0),
messages = FALSE,
progress = FALSE
),
"You must provide a model\\."
)
})
test_that("tabscreen_ollama() normalizes common Ollama base URLs", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
# Use closed local port to avoid dependency on a running Ollama server.
test_api_base <- "http://127.0.0.1:1"
test_api_root <- "http://127.0.0.1:1/api"
res_base <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api_base,
model = "llama3-8b-8192",
messages = FALSE,
progress = FALSE
)
res_root <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
api_url = test_api_root,
model = "llama3-8b-8192",
messages = FALSE,
progress = FALSE
)
expect_identical(attr(res_base, "arg_list")$api_url, "http://127.0.0.1:1/api/chat")
expect_identical(attr(res_root, "arg_list")$api_url, "http://127.0.0.1:1/api/chat")
})
future::plan(future::multisession)
test_that("tabscreen_ollama() works with single parameter values.",{
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
max_tries = 2
)
)
expect_true(is.numeric(test_obj$answer_data$decision_binary))
expect_equal(nrow(test_obj$answer_data), 1L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = 1
)
)
expect_equal(nrow(test_obj$answer_data), 2L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = c(prompt, prompt2),
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192"),
reps = 1
)
)
expect_equal(nrow(test_obj$answer_data), 2L)
expect_null(test_obj$answer_data_aggregated)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
})
test_that("tabscreen_ollama() works with multiple parameter values.",{
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 3L)
expect_equal(nrow(test_obj$answer_data_aggregated), 2L)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_gt(nrow(test_obj$answer_data), nrow(test_obj$answer_data_aggregated))
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 6L)
expect_equal(nrow(test_obj$answer_data_aggregated), 4L)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192", "gemma-7b-it"),
reps = c(2, 1),
top_p = c(0.2, 1)
)
)
expect_equal(nrow(test_obj$answer_data), 12L)
expect_equal(nrow(test_obj$answer_data_aggregated), 2*2*2)
expect_null(test_obj$price_data)
expect_null(test_obj$price_dollar)
})
test_that("tabscreen_ollama() works with detailed functions and ... .", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
decision_description = TRUE
)
) |>
suppressMessages()
expect_true(is.character(test_obj$answer_data$detailed_description))
expect_null(test_obj$answer_data_aggregated)
# Working with temperature, i.e. ... calls
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 3,
decision_description = TRUE,
progress = FALSE,
temperature = 0.001
)
) |>
suppressMessages()
n_distinct_detailed_answers <- test_obj$answer_data$detailed_description |> dplyr::n_distinct()
expect_lt(n_distinct_detailed_answers, 3)
})
test_that("Message behavior.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
messages = FALSE,
progress = FALSE
)
)
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
messages = FALSE,
progress = FALSE
)
)
expect_no_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "gemma-7b-it",
reps = 2
)
)
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
decision_description = TRUE
)
) |>
suppressMessages()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "gemma-7b-it",
reps = 1,
decision_description = TRUE
),
paste0(
"* Be aware that getting detailed reponses ",
"will substantially increase the time of the screening."
)
) |>
suppressMessages()
expect_message(
test_obj <- tabscreen_ollama(
data = filges2015_dat[148,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3.2:latest",
),
paste0(
"* Consider removing references without abstracts ",
"since these can distort the accuracy of the screening."
)
) |>
suppressMessages()
})
test_that("tabscreen_ollama() expected errors.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1, 1)
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1),
rpm = c(30, 30, 30)
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192-x", "gemma-7b-it"),
reps = reps,
rpm = rpm
)
)
# With detailed description functions
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1, 1),
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = models,
reps = c(10, 1),
rpm = c(30, 30, 30),
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = c("llama3-8b-8192-x", "gemma-7b-it"),
reps = reps,
rpm = rpm,
decision_description = TRUE
)
)
expect_error(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
incl_cutoff_upper = 0.4,
incl_cutoff_lower = 0.5
)
)
})
future::plan(future::sequential)
# Test parallel
test_that("That parallel processing works.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
future::plan(future::multisession, workers = future::availableCores())
expect_message(
tm_par <- system.time(
test_obj <- tabscreen_ollama(
data = filges2015_dat[1:2,],
prompt = prompt,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 10
)
)
)
future::plan(future::sequential)
expect_lt(tm_par[["elapsed"]], sum(test_obj$answer_data$run_time))
ollama_answers <-
test_obj$answer_data |>
filter(!is.na(decision_gpt)) |>
pull(decision_gpt)
# Testing function call work
ollama_answers |>
stringr::str_detect("1|0|rror") |>
unique() |>
expect_true()
})
test_that("Print and class expectation for ollama object.", {
if(skip) skip()
if(skip_github_action) skip_on_ci()
skip_on_cran()
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
max_tries = 2
)
)
# class check
expect_s3_class(random_name, "gpt")
expect_s3_class(random_name, "list")
expect_output(print(random_name), "result_object\\$answer_data")
expect_output(print.gpt(random_name), "result_object\\$answer_data")
expect_false("answer_data_aggregated" %in% names(random_name))
expect_false("price_dollar" %in% names(random_name))
print_out1 <- paste0(
"Find the final result dataset via result_object$answer_data"
)
expect_output(print(random_name), print_out1)
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = c(prompt),
studyid = studyid, # indicate the variable with the studyid in the data
title = title, # indicate the variable with the titles in the data
abstract = abstract,
)
) |>
suppressMessages()
expect_identical(names(random_name$answer_data), names(random_name$error_data))
expect_output(print(random_name), "result_object\\$answer_data")
print_out2 <- paste0(
"Find the final result dataset via result_object$answer_data"
)
expect_output(print(random_name), print_out2)
#############
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[1,],
prompt = prompt,
studyid = studyid,
title = title,
abstract = abstract,
model = "llama3-8b-8192",
reps = 1,
)
)
# class check
expect_s3_class(random_name, "gpt")
expect_s3_class(random_name, "list")
expect_output(print(random_name), "result_object\\$answer_data")
expect_output(print.gpt(random_name), "result_object\\$answer_data")
expect_false("price_dollar" %in% names(random_name))
expect_message(
random_name <- tabscreen_ollama(
data = filges2015_dat[c(1:2),],
prompt = c(prompt),
studyid = studyid, # indicate the variable with the studyid in the data
title = title, # indicate the variable with the titles in the data
abstract = abstract
)
) |>
suppressMessages()
expect_output(print(random_name), "result_object\\$answer_data")
expect_false("price_dollar" %in% names(random_name))
})
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