tests/testthat/test-readme-against-llm.R

# Models the documentation is prompted with. Cheap, fast models are used on
# purpose: the tasks are simple, and if such a model cannot follow the Readme,
# that says something about the Readme, which is what is being tested here.
# Override with a comma separated list in RKORAP_LLM_MODELS, e.g. to add
# OpenAI's cheapest models, "gpt-5-nano" or "gpt-5.6-luna".
defaultLlmModels <- c(
  "gemini-3.5-flash-lite",
  "claude-sonnet-5",
  "hf:zai-org/GLM-5.3-Flash" # GLM-5.3-Flash, via the Synthetic API
)

llmModels <- function() {
  configured <- Sys.getenv("RKORAP_LLM_MODELS", unset = "")
  if (nzchar(configured)) {
    trimws(strsplit(configured, ",", fixed = TRUE)[[1]])
  } else {
    defaultLlmModels
  }
}

# Provider and API key environment variable belonging to a model id
llmProvider <- function(model) {
  if (grepl("^gpt-", model, ignore.case = TRUE)) {
    list(name = "openai", keyVar = "OPENAI_API_KEY")
  } else if (grepl("^claude-", model, ignore.case = TRUE)) {
    list(name = "claude", keyVar = "ANTHROPIC_API_KEY")
  } else if (grepl("^gemini-", model, ignore.case = TRUE)) {
    list(name = "gemini", keyVar = "GOOGLE_API_KEY")
  } else if (grepl("^hf:", model, ignore.case = TRUE)) {
    # OpenAI compatible endpoint, but tidyllm's openai provider does not allow
    # for a custom base url, so these are queried directly (see below)
    list(name = "synthetic", keyVar = "SYNTHETIC_API_KEY")
  } else {
    stop(paste(
      "Unsupported model:", model,
      "- supported prefixes: gpt-, claude-, gemini-, hf: (Synthetic)"
    ))
  }
}

# Helper function to skip if the API key of the given model is not available
skip_if_no_api_key <- function(model) {
  keyVar <- llmProvider(model)$keyVar
  skip_if_not(
    nzchar(Sys.getenv(keyVar)),
    paste0("No API key for ", model, " found (need ", keyVar, ")")
  )
}

# Helper function to find README.md file in current or parent directories
find_readme_path <- function() {
  readme_paths <- c("Readme.md", "../Readme.md", "../../Readme.md")
  for (path in readme_paths) {
    if (file.exists(path)) {
      return(path)
    }
  }
  return(NULL)
}

# Helper function to read README content
read_readme_content <- function() {
  readme_path <- find_readme_path()
  if (is.null(readme_path)) {
    return(NULL)
  }
  readme_content <- readLines(readme_path)

  # Find the line with "## Installation" and truncate before it
  installation_line <- grep("^## Installation", readme_content, ignore.case = TRUE)
  if (length(installation_line) > 0) {
    readme_content <- readme_content[1:(installation_line[1] - 1)]
  }

  paste(readme_content, collapse = "\n")
}

# Helper function to call an OpenAI compatible endpoint that tidyllm cannot be
# pointed at, because its openai provider takes no custom base url
call_openai_compatible_api <- function(prompt, model, temperature, baseUrl, keyVar) {
  response <- httr2::request(paste0(baseUrl, "/chat/completions")) |>
    httr2::req_auth_bearer_token(Sys.getenv(keyVar)) |>
    httr2::req_body_json(list(
      model = model,
      temperature = temperature,
      messages = list(list(role = "user", content = prompt))
    )) |>
    httr2::req_retry(max_tries = 3) |>
    httr2::req_timeout(120) |>
    httr2::req_perform()

  httr2::resp_body_json(response)$choices[[1]]$message$content
}

# Helper function to call LLM API using tidyllm
call_llm_api <- function(prompt, model, max_tokens = 500, temperature = 0.1) {
  cat("Calling LLM API with model:", model, "\n")
  # Only print prompt up to the beginning of README content
  readme_start <- regexpr("README Documentation:", prompt, fixed = TRUE)
  if (readme_start > 0) {
    prompt_preview <- substr(prompt, 1, readme_start - 1)
    cat("Prompt (up to README):\n", prompt_preview, "\n")
  } else {
    cat("Prompt:\n", prompt, "\n")
  }
  tryCatch(
    {
      provider <- llmProvider(model)

      if (provider$name == "synthetic") {
        call_openai_compatible_api(
          prompt,
          model = model,
          temperature = temperature,
          baseUrl = "https://api.synthetic.new/openai/v1",
          keyVar = provider$keyVar
        )
      } else {
        # Use tidyllm unified API
        result <- tidyllm::llm_message(prompt) |>
          tidyllm::chat(
            .provider = switch(provider$name,
              openai = tidyllm::openai(),
              claude = tidyllm::claude(),
              gemini = tidyllm::gemini()
            ),
            .model = model,
            .temperature = temperature,
            .max_tries = 3
          )

        # Extract the reply text
        tidyllm::get_reply(result)
      }
    },
    error = function(e) {
      message <- as.character(e)
      # Conditions of the account rather than of the documentation: these must
      # not turn a documentation test red
      if (grepl("429", message)) {
        skip("LLM API rate limit exceeded - please try again later or check your API key/credits")
      } else if (grepl("401|403", message)) {
        skip(paste0(
          "LLM API authentication failed - please check ",
          llmProvider(model)$keyVar
        ))
      } else if (grepl("402|credit balance|billing|quota|insufficient", message, ignore.case = TRUE)) {
        skip(paste0("No credits available for ", model, ": ", message))
      } else {
        stop(paste("LLM API error:", message))
      }
    }
  )
}

# Helper function to create README-guided prompt
create_readme_prompt <- function(task_description, specific_task) {
  readme_text <- read_readme_content()
  if (is.null(readme_text)) {
    stop("README.md not found")
  }

  paste0(
    "You are an expert R programmer. Based on the following README documentation for the RKorAPClient package, ",
    task_description, "\n\n",
    "README Documentation:\n",
    readme_text,
    "\n\nTask: ", specific_task,
    "\n\nProvide only the R code without explanations."
  )
}

# Helper function to extract R code from markdown code blocks
extract_r_code <- function(response_text) {
  # Remove markdown code blocks if present
  code <- gsub("```[rR]?\\n?", "", response_text)
  code <- gsub("```\\n?$", "", code)
  # Remove leading/trailing whitespace
  trimws(code)
}

# Helper function to test code syntax
test_code_syntax <- function(code) {
  tryCatch(
    {
      parse(text = code)
      TRUE
    },
    error = function(e) {
      cat("Syntax error:", as.character(e), "\n")
      FALSE
    }
  )
}

# Helper function to run code if RUN_LLM_CODE is set
run_code_if_enabled <- function(code, test_name) {
  if (nzchar(Sys.getenv("RUN_LLM_CODE")) && Sys.getenv("RUN_LLM_CODE") == "true") {
    cat("Running generated code for", test_name, "...\n")
    tryCatch(
      {
        result <- eval(parse(text = code))
        cat("Code executed successfully. Result type:", class(result), "\n")
        if (is.data.frame(result)) {
          cat("Result dimensions:", nrow(result), "rows,", ncol(result), "columns\n")
          if (nrow(result) > 0) {
            cat("First few rows:\n")
            print(head(result, 3))
          }
        } else {
          cat("Result preview:\n")
          print(result)
        }
        return(TRUE)
      },
      error = function(e) {
        cat("Runtime error:", as.character(e), "\n")
        return(FALSE)
      }
    )
  } else {
    cat("Skipping code execution (set RUN_LLM_CODE=true to enable)\n")
    return(NA)
  }
}

for (model in llmModels()) {
  test_that(paste(model, "can solve frequency query task with README guidance"), {
    # Skip if offline
    skip_if_offline()

    # Skip if no API keys are set
    skip_if_no_api_key(model)

    # tidyllm is only suggested, so the tests must not fail without it
    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")

    # Check for README file
    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")

    # Create the prompt with README context and task
    prompt <- create_readme_prompt(
      "write R code to perform a frequency query for the word 'Demokratie' across the past three years. The code should use the RKorAPClient package and return a data frame.",
      "Write R code to query frequency of 'Demokratie' from the past three years using RKorAPClient."
    )

    # Call LLM API
    generated_response <- call_llm_api(prompt, model, max_tokens = 500)
    generated_code <- extract_r_code(generated_response)

    # Basic checks on the generated code
    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
    expect_true(grepl("frequencyQuery", generated_code), "Generated code should include frequencyQuery")
    expect_true(grepl("Demokratie", generated_code), "Generated code should include the search term 'Demokratie'")
    last_year <- as.numeric(format(Sys.Date(), "%Y")) - 1

    expect_true(grepl("Date in", generated_code), "Generated code should vc restriction on years")

    # Check that the generated code contains essential RKorAPClient patterns
    # expect_true(grepl("\\|>", generated_code) || grepl("%>%", generated_code), "Generated code should use pipe operators")

    # Test code syntax
    syntax_valid <- test_code_syntax(generated_code)
    expect_true(syntax_valid, "Generated code should be syntactically valid R code")

    # Print the generated code for manual inspection
    cat("Generated code:\n", generated_code, "\n")

    # Run the code if RUN_LLM_CODE is set
    execution_result <- run_code_if_enabled(generated_code, "frequency query")
    if (!is.na(execution_result)) {
      expect_true(execution_result, "Generated code should execute without runtime errors")
    }
  })


  test_that(paste(model, "can solve collocation analysis task with README guidance"), {
    # Skip if offline
    skip_if_offline()

    # Skip if no API keys are set
    skip_if_no_api_key(model)

    # tidyllm is only suggested, so the tests must not fail without it
    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")

    # Check for README file
    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")

    # Create the prompt for collocation analysis
    prompt <- create_readme_prompt(
      paste("Write R code to perform a collocation analysis for the lemma 'leverage' based on the current English Wikipedia Corpus using default parameters", "and show the three highest collocates according to their log dice score.
  "),
      "Write R code to perform collocation analysis for lemma 'leverage' using RKorAPClient."
    )

    # Call LLM API
    generated_response <- call_llm_api(prompt, model, max_tokens = 500)
    generated_code <- extract_r_code(generated_response)

    # Basic checks on the generated code
    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
    expect_true(grepl("collocationAnalysis", generated_code), "Generated code should include collocationAnalysis")
    expect_true(grepl("tt/l=leverage", generated_code), "Generated code should include the search the lemma 'leverage'")
    # expect_true(grepl("auth", generated_code), "Generated code should include auth() for collocation analysis")
    expect_true(grepl("instance/english", generated_code, fixed = TRUE), "Generated code should include the specified KorAP URL")

    # Test code syntax
    syntax_valid <- test_code_syntax(generated_code)
    expect_true(syntax_valid, "Generated code should be syntactically valid R code")

    # Print the generated code for manual inspection
    cat("Generated collocation analysis code:\n", generated_code, "\n")

    # Run the code if RUN_LLM_CODE is set
    execution_result <- run_code_if_enabled(generated_code, "collocation analysis")
    if (!is.na(execution_result)) {
      expect_true(execution_result, "Generated code should execute without runtime errors")
    }
  })

  test_that(paste(model, "can solve corpus query task with README guidance"), {
    # Skip if offline
    skip_if_offline()

    # Skip if no API keys are set
    skip_if_no_api_key(model)

    # tidyllm is only suggested, so the tests must not fail without it
    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")

    # Check for README file
    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")

    # Create the prompt for corpus query
    prompt <- create_readme_prompt(
      "write R code to perform a simple corpus query for 'Hello world' and fetch all results. The code should use the RKorAPClient package.",
      "Write R code to query 'Hello world' and fetch all results using RKorAPClient."
    )

    # Call LLM API
    generated_response <- call_llm_api(prompt, model, max_tokens = 300)
    generated_code <- extract_r_code(generated_response)

    # Basic checks on the generated code
    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
    expect_true(grepl("corpusQuery", generated_code), "Generated code should include corpusQuery")
    expect_true(grepl("Hello world", generated_code), "Generated code should include the search term 'Hello world'")
    expect_true(grepl("fetchAll", generated_code), "Generated code should include fetchAll")

    # Check that the generated code follows the README example pattern
    expect_true(
      grepl("\\|>", generated_code) || grepl("%>%", generated_code),
      "Generated code should use pipe operators"
    )

    # Test code syntax
    syntax_valid <- test_code_syntax(generated_code)
    expect_true(syntax_valid, "Generated code should be syntactically valid R code")

    # Print the generated code for manual inspection
    cat("Generated corpus query code:\n", generated_code, "\n")

    # Run the code if RUN_LLM_CODE is set
    execution_result <- run_code_if_enabled(generated_code, "corpus query")
    if (!is.na(execution_result)) {
      expect_true(execution_result, "Generated code should execute without runtime errors")
    }
  })
}