diff --git a/agenta-backend/agenta_backend/resources/evaluators/evaluators.py b/agenta-backend/agenta_backend/resources/evaluators/evaluators.py index 20ec5be92c..8e94d2c24e 100644 --- a/agenta-backend/agenta_backend/resources/evaluators/evaluators.py +++ b/agenta-backend/agenta_backend/resources/evaluators/evaluators.py @@ -4,12 +4,13 @@ "key": "auto_exact_match", "direct_use": True, "settings_template": { - "label": "Exact Match Settings", - "description": "Settings for the Exact Match evaluator", - "correct_answer_keys": { - "label": "Correct Answer", - "default": ["correct_answer"], - "type": "array", + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", + "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", }, }, "description": "Exact Match evaluator determines if the output exactly matches the specified correct answer, ensuring precise alignment with expected results.", @@ -18,10 +19,7 @@ "name": "Contains Json", "key": "auto_contains_json", "direct_use": True, - "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the JSON output contains the specified JSON structure.", - }, + "settings_template": {}, "description": "Contains Json evaluator checks if the output contains the specified JSON structure.", }, { @@ -38,10 +36,13 @@ "max": 1, "required": True, }, - "correct_answer_keys": { - "label": "Correct Answer", - "default": ["correct_answer"], - "type": "array", + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", + "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", }, }, "description": "Similarity Match evaluator checks if the generated answer is similar to the expected answer. You need to provide the similarity threshold. It uses the Jaccard similarity to compare the answers.", @@ -79,10 +80,13 @@ "description": "The name of the field in the JSON output that you wish to evaluate", "required": True, }, - "correct_answer_keys": { - "label": "Correct Answer", - "default": ["correct_answer"], + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", }, }, "description": "JSON Field Match evaluator compares specific fields within JSON (JavaScript Object Notation) data. This matching can involve finding similarities or correspondences between fields in different JSON objects.", @@ -95,10 +99,18 @@ "prompt_template": { "label": "Prompt Template", "type": "text", - "default": "We have an LLM App that we want to evaluate its outputs. Based on the prompt and the parameters provided below evaluate the output based on the evaluation strategy below:\nEvaluation strategy: 0 to 10 0 is very bad and 10 is very good.\nPrompt: {llm_app_prompt_template}\nInputs: country: {country}\nCorrect Answer:{correct_answer}\nEvaluate this: {variant_output}\n\nAnswer ONLY with one of the given grading or evaluation options.", + "default": "We have an LLM App that we want to evaluate its outputs. Based on the prompt and the parameters provided below evaluate the output based on the evaluation strategy below:\nEvaluation strategy: 0 to 10 0 is very bad and 10 is very good.\nPrompt: {llm_app_prompt_template}\nInputs: country: {country}\nExpected Answer Column:{correct_answer}\nEvaluate this: {variant_output}\n\nAnswer ONLY with one of the given grading or evaluation options.", "description": "Template for AI critique prompts", "required": True, - } + }, + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", + "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", + }, }, "description": "AI Critique evaluator sends the generated answer and the correct_answer to an LLM model and uses it to evaluate the correctness of the answer. You need to provide the evaluation prompt (or use the default prompt).", }, @@ -128,10 +140,13 @@ "description": "https://your-webhook-url.com", "required": True, }, - "correct_answer_keys": { - "label": "Correct Answer", - "default": ["correct_answer"], + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", }, }, "description": "Webhook test evaluator sends the generated answer and the correct_answer to a webhook and expects a response indicating the correctness of the answer. You need to provide the URL of the webhook and the response of the webhook must be between 0 and 1.", @@ -141,13 +156,17 @@ "key": "auto_starts_with", "direct_use": False, "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the output starts with the specified prefix.", - "prefix": {"label": "prefix", "type": "string", "required": True}, + "prefix": { + "label": "prefix", + "type": "string", + "required": True, + "description": "The string to match at the start of the output.", + }, "case_sensitive": { "label": "Case Sensitive", "type": "boolean", "default": True, + "description": "If the evaluation should be case sensitive.", }, }, "description": "Starts With evaluator checks if the output starts with a specified prefix, considering case sensitivity based on the settings.", @@ -157,8 +176,6 @@ "key": "auto_ends_with", "direct_use": False, "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the output ends with the specified suffix.", "case_sensitive": { "label": "Case Sensitive", "type": "boolean", @@ -179,8 +196,6 @@ "key": "auto_contains", "direct_use": False, "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the output contains the specified substring.", "case_sensitive": { "label": "Case Sensitive", "type": "boolean", @@ -201,8 +216,6 @@ "key": "auto_contains_any", "direct_use": False, "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the output contains any of the specified substrings.", "case_sensitive": { "label": "Case Sensitive", "type": "boolean", @@ -223,8 +236,6 @@ "key": "auto_contains_all", "direct_use": False, "settings_template": { - "label": "Single Model Testing Settings", - "description": "Checks if the output contains all of the specified substrings.", "case_sensitive": { "label": "Case Sensitive", "type": "boolean", @@ -245,13 +256,19 @@ "key": "auto_levenshtein_distance", "direct_use": False, "settings_template": { - "label": "Levenshtein Distance Settings", - "description": "Evaluates the Levenshtein distance between the output and the correct answer. If a threshold is specified, it checks if the distance is below this threshold and returns a boolean value. If no threshold is specified, it returns the numerical Levenshtein distance.", - "threshold": {"label": "Threshold", "type": "number", "required": False}, - "correct_answer_keys": { - "label": "Correct Answer", - "default": ["correct_answer"], + "threshold": { + "label": "Threshold", + "type": "number", + "required": False, + "description": "The maximum allowed Levenshtein distance between the output and the correct answer.", + }, + "correct_answer_key": { + "label": "Expected Answer Column", + "default": "correct_answer", "type": "string", + "advanced": True, # Tells the frontend that this setting is advanced and should be hidden by default + "ground_truth_key": True, # Tells the frontend that is the name of the column in the test set that should be shown as a ground truth to the user + "description": "The name of the column in the test data that contains the correct answer", }, }, "description": "This evaluator calculates the Levenshtein distance between the output and the correct answer. If a threshold is provided in the settings, it returns a boolean indicating whether the distance is within the threshold. If no threshold is provided, it returns the actual Levenshtein distance as a numerical value.", diff --git a/agenta-backend/agenta_backend/services/evaluator_manager.py b/agenta-backend/agenta_backend/services/evaluator_manager.py index f6396edc79..c4cc49ace3 100644 --- a/agenta-backend/agenta_backend/services/evaluator_manager.py +++ b/agenta-backend/agenta_backend/services/evaluator_manager.py @@ -141,19 +141,22 @@ async def create_ready_to_use_evaluators(app: AppDB): Returns: Nothing. The function works by side effect, modifying the database. """ - evaluators = get_evaluators() - direct_use_evaluators = [ - evaluator for evaluator in evaluators if evaluator.get("direct_use") + evaluator for evaluator in get_evaluators() if evaluator.get("direct_use") ] for evaluator in direct_use_evaluators: - settings_values = {} - settings_template = evaluator.get("settings_template", {}) - if "correct_answer_keys" in settings_template: - settings_values["correct_answer_keys"] = settings_template[ - "correct_answer_keys" - ].get("default", []) + settings_values = { + setting_name: setting.get("default") + for setting_name, setting in evaluator.get("settings_template", {}).items() + if setting.get("ground_truth_key") is True and setting.get("default", "") + } + + for setting_name, default_value in settings_values.items(): + assert ( + default_value != "" + ), f"Default value for ground truth key '{setting_name}' in Evaluator is empty" + await db_manager.create_evaluator_config( app=app, organization=app.organization if isCloudEE() else None, # noqa, diff --git a/agenta-backend/agenta_backend/services/evaluators_service.py b/agenta-backend/agenta_backend/services/evaluators_service.py index 2502404890..d73a4229d9 100644 --- a/agenta-backend/agenta_backend/services/evaluators_service.py +++ b/agenta-backend/agenta_backend/services/evaluators_service.py @@ -1,105 +1,96 @@ -import re import json +import logging +import re +from typing import Any, Dict, List, Tuple + import httpx -from typing import Any, Dict, Tuple, List +from openai import OpenAI -from agenta_backend.services.security import sandbox from agenta_backend.models.db_models import Error, Result - -from langchain.llms import OpenAI -from langchain.chains import LLMChain -from langchain.prompts import PromptTemplate -import logging +from agenta_backend.services.security import sandbox logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) +def get_correct_answer( + data_point: Dict[str, Any], settings_values: Dict[str, Any] +) -> Any: + """ + Helper function to retrieve the correct answer from the data point based on the settings values. + + Args: + data_point (Dict[str, Any]): The data point containing the correct answer. + settings_values (Dict[str, Any]): The settings values containing the key for the correct answer. + + Returns: + Any: The correct answer from the data point. + + Raises: + ValueError: If the correct answer key is not provided or not found in the data point. + """ + correct_answer_key = settings_values.get("correct_answer_key") + if correct_answer_key is None: + raise ValueError("No correct answer keys provided.") + if correct_answer_key not in data_point: + raise ValueError( + f"Correct answer column '{correct_answer_key}' not found in the test set." + ) + return data_point[correct_answer_key] + + def auto_exact_match( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: + """ + Evaluator function to determine if the output exactly matches the correct answer. + + Args: + inputs (Dict[str, Any]): The inputs for the evaluation. + output (str): The output generated by the model. + data_point (Dict[str, Any]): The data point containing the correct answer. + app_params (Dict[str, Any]): The application parameters. + settings_values (Dict[str, Any]): The settings values containing the key for the correct answer. + lm_providers_keys (Dict[str, Any]): The language model provider keys. + + Returns: + Result: A Result object containing the evaluation result. + """ try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) - - correct_answer_key = correct_answer_keys[0] - - exact_match = True if output == data_point[correct_answer_key] else False + correct_answer = get_correct_answer(data_point, settings_values) + exact_match = True if output == correct_answer else False result = Result(type="bool", value=exact_match) return result - except Exception as e: + except ValueError as e: return Result( type="error", value=None, error=Error( - message="Error during Auto Exact Match evaluation", stacktrace=str(e) + message=str(e), ), ) - - -def auto_similarity_match( - inputs: Dict[str, Any], - output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], - settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], -) -> Result: - try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) - - correct_answer_key = correct_answer_keys[0] - - set1 = set(output.split()) - set2 = set(data_point[correct_answer_key].split()) - intersect = set1.intersection(set2) - union = set1.union(set2) - - similarity = len(intersect) / len(union) - - is_similar = ( - True if similarity > settings_values["similarity_threshold"] else False - ) - result = Result(type="bool", value=is_similar) - return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, error=Error( - message="Error during Auto Similarity Match evaluation", - stacktrace=str(e), + message="Error during Auto Exact Match evaluation", stacktrace=str(e) ), ) def auto_regex_test( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: re_pattern = re.compile(settings_values["regex_pattern"], re.IGNORECASE) @@ -107,7 +98,7 @@ def auto_regex_test( bool(re_pattern.search(output)) == settings_values["regex_should_match"] ) return Result(type="bool", value=result) - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -118,33 +109,28 @@ def auto_regex_test( def field_match_test( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, data_point: Dict[str, Any], - app_params: Dict[str, Any], + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) - - correct_answer_key = correct_answer_keys[0] - + correct_answer = get_correct_answer(data_point, settings_values) output_json = json.loads(output) - result = ( - output_json[settings_values["json_field"]] == data_point[correct_answer_key] - ) + result = output_json[settings_values["json_field"]] == correct_answer return Result(type="bool", value=result) - except Exception as e: - logging.debug("Field Match Test Failed because of Error: " + str(e)) + except ValueError as e: + return Result( + type="error", + value=None, + error=Error( + message=str(e), + ), + ) + except Exception as e: # pylint: disable=broad-except + logging.debug("Field Match Test Failed because of Error: %s", str(e)) return Result(type="bool", value=False) @@ -152,26 +138,16 @@ def auto_webhook_test( inputs: Dict[str, Any], output: str, data_point: Dict[str, Any], - app_params: Dict[str, Any], + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) - - correct_answer_key = correct_answer_keys[0] + correct_answer = get_correct_answer(data_point, settings_values) with httpx.Client() as client: payload = { - "correct_answer": data_point[correct_answer_key], + "correct_answer": correct_answer, "output": output, "inputs": inputs, } @@ -196,6 +172,14 @@ def auto_webhook_test( ), ) return Result(type="number", value=score) + except ValueError as e: + return Result( + type="error", + value=None, + error=Error( + message=str(e), + ), + ) except httpx.HTTPError as e: return Result( type="error", @@ -205,7 +189,7 @@ def auto_webhook_test( stacktrace=str(e), ), ) - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -221,7 +205,7 @@ def auto_custom_code_run( data_point: Dict[str, Any], app_params: Dict[str, Any], settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: result = sandbox.execute_code_safely( @@ -232,7 +216,7 @@ def auto_custom_code_run( code=settings_values["code"], ) return Result(type="number", value=result) - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -249,7 +233,7 @@ def auto_ai_critique( app_params: Dict[str, Any], settings_values: Dict[str, Any], lm_providers_keys: Dict[str, Any], -) -> str: +) -> Result: """ Evaluate a response using an AI critique based on provided inputs, output, correct answer, app parameters, and settings. @@ -262,47 +246,36 @@ def auto_ai_critique( lm_providers_keys (Dict[str, Any]): Keys for language model providers. Returns: - str: Evaluation result. + Result: Evaluation result. """ try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) - - correct_answer_key = correct_answer_keys[0] - llm = OpenAI( - openai_api_key=lm_providers_keys["OPENAI_API_KEY"], - temperature=0.8, - model="gpt-3.5-turbo-instruct", - ) + correct_answer = get_correct_answer(data_point, settings_values) + openai_api_key = lm_providers_keys["OPENAI_API_KEY"] chain_run_args = { "llm_app_prompt_template": app_params.get("prompt_user", ""), "variant_output": output, - "correct_answer": data_point[correct_answer_key], + "correct_answer": correct_answer, } for key, value in inputs.items(): chain_run_args[key] = value - prompt = PromptTemplate( - input_variables=list( - chain_run_args.keys() - ), # Use the keys from chain_run_args - template=settings_values["prompt_template"], + prompt_template = settings_values["prompt_template"] + messages = [ + {"role": "system", "content": prompt_template}, + {"role": "user", "content": str(chain_run_args)}, + ] + + client = OpenAI(api_key=openai_api_key) + response = client.chat.completions.create( + model="gpt-3.5-turbo", messages=messages, temperature=0.8 ) - chain = LLMChain(llm=llm, prompt=prompt) - evaluation_output = chain.run(**chain_run_args) + evaluation_output = response.choices[0].message["content"].strip() - return Result(type="text", value=evaluation_output.strip()) - except Exception as e: + return Result(type="text", value=evaluation_output) + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -311,12 +284,12 @@ def auto_ai_critique( def auto_starts_with( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: prefix = settings_values.get("prefix", "") @@ -328,7 +301,7 @@ def auto_starts_with( result = Result(type="bool", value=output.startswith(prefix)) return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -339,12 +312,12 @@ def auto_starts_with( def auto_ends_with( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument app_params: Dict[str, Any], settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: suffix = settings_values.get("suffix", "") @@ -356,7 +329,7 @@ def auto_ends_with( result = Result(type="bool", value=output.endswith(suffix)) return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -365,12 +338,12 @@ def auto_ends_with( def auto_contains( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: substring = settings_values.get("substring", "") @@ -382,7 +355,7 @@ def auto_contains( result = Result(type="bool", value=substring in output) return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -391,12 +364,12 @@ def auto_contains( def auto_contains_any( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: substrings_str = settings_values.get("substrings", "") @@ -411,7 +384,7 @@ def auto_contains_any( type="bool", value=any(substring in output for substring in substrings) ) return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -422,12 +395,12 @@ def auto_contains_any( def auto_contains_all( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: substrings_str = settings_values.get("substrings", "") @@ -442,7 +415,7 @@ def auto_contains_all( type="bool", value=all(substring in output for substring in substrings) ) return result - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -453,12 +426,12 @@ def auto_contains_all( def auto_contains_json( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, - data_point: Dict[str, Any], - app_params: Dict[str, Any], - settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + data_point: Dict[str, Any], # pylint: disable=unused-argument + app_params: Dict[str, Any], # pylint: disable=unused-argument + settings_values: Dict[str, Any], # pylint: disable=unused-argument + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: try: @@ -472,7 +445,7 @@ def auto_contains_json( contains_json = False return Result(type="bool", value=contains_json) - except Exception as e: + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -484,7 +457,7 @@ def auto_contains_json( def levenshtein_distance(s1, s2): if len(s1) < len(s2): - return levenshtein_distance(s2, s1) + return levenshtein_distance(s2, s1) # pylint: disable=arguments-out-of-order if len(s2) == 0: return len(s1) @@ -503,27 +476,17 @@ def levenshtein_distance(s1, s2): def auto_levenshtein_distance( - inputs: Dict[str, Any], + inputs: Dict[str, Any], # pylint: disable=unused-argument output: str, data_point: Dict[str, Any], - app_params: Dict[str, Any], + app_params: Dict[str, Any], # pylint: disable=unused-argument settings_values: Dict[str, Any], - lm_providers_keys: Dict[str, Any], + lm_providers_keys: Dict[str, Any], # pylint: disable=unused-argument ) -> Result: try: - correct_answer_keys = settings_values.get("correct_answer_keys") - if correct_answer_keys is None: - return Result( - type="error", - value=None, - error=Error( - message="No correct answer keys provided.", - ), - ) + correct_answer = get_correct_answer(data_point, settings_values) - correct_answer_key = correct_answer_keys[0] - - distance = levenshtein_distance(output, data_point[correct_answer_key]) + distance = levenshtein_distance(output, correct_answer) if "threshold" in settings_values: threshold = settings_values["threshold"] @@ -532,7 +495,15 @@ def auto_levenshtein_distance( return Result(type="number", value=distance) - except Exception as e: + except ValueError as e: + return Result( + type="error", + value=None, + error=Error( + message=str(e), + ), + ) + except Exception as e: # pylint: disable=broad-except return Result( type="error", value=None, @@ -543,6 +514,65 @@ def auto_levenshtein_distance( ) +def auto_similarity_match( + inputs: Dict[str, Any], + output: str, + data_point: Dict[str, Any], + app_params: Dict[str, Any], + settings_values: Dict[str, Any], + lm_providers_keys: Dict[str, Any], +) -> Result: + try: + correct_answer = get_correct_answer(data_point, settings_values) + set1 = set(output.split()) + set2 = set(correct_answer.split()) + intersect = set1.intersection(set2) + union = set1.union(set2) + + similarity = len(intersect) / len(union) + + is_similar = ( + True if similarity > settings_values["similarity_threshold"] else False + ) + result = Result(type="bool", value=is_similar) + return result + except ValueError as e: + return Result( + type="error", + value=None, + error=Error( + message=str(e), + ), + ) + except Exception as e: # pylint: disable=broad-except + return Result( + type="error", + value=None, + error=Error( + message="Error during Auto Similarity Match evaluation", + stacktrace=str(e), + ), + ) + + +EVALUATOR_FUNCTIONS = { + "auto_exact_match": auto_exact_match, + "auto_regex_test": auto_regex_test, + "field_match_test": field_match_test, + "auto_webhook_test": auto_webhook_test, + "auto_custom_code_run": auto_custom_code_run, + "auto_ai_critique": auto_ai_critique, + "auto_starts_with": auto_starts_with, + "auto_ends_with": auto_ends_with, + "auto_contains": auto_contains, + "auto_contains_any": auto_contains_any, + "auto_contains_all": auto_contains_all, + "auto_contains_json": auto_contains_json, + "auto_levenshtein_distance": auto_levenshtein_distance, + "auto_similarity_match": auto_similarity_match, +} + + def evaluate( evaluator_key: str, inputs: Dict[str, Any], @@ -552,9 +582,15 @@ def evaluate( settings_values: Dict[str, Any], lm_providers_keys: Dict[str, Any], ) -> Result: - evaluation_function = globals().get(evaluator_key, None) + evaluation_function = EVALUATOR_FUNCTIONS.get(evaluator_key, None) if not evaluation_function: - raise ValueError(f"Evaluation method '{evaluator_key}' not found.") + return Result( + type="error", + value=None, + error=Error( + message=f"Evaluation method '{evaluator_key}' not found.", + ), + ) try: return evaluation_function( inputs, @@ -565,6 +601,11 @@ def evaluate( lm_providers_keys, ) except Exception as exc: - raise RuntimeError( - f"Error occurred while running {evaluator_key} evaluation. Exception: {str(exc)}" + return Result( + type="error", + value=None, + error=Error( + message="Error occurred while running {evaluator_key} evaluation. ", + stacktrace=str(exc), + ), ) diff --git a/agenta-backend/agenta_backend/tasks/evaluations.py b/agenta-backend/agenta_backend/tasks/evaluations.py index 381aa18e25..15a055b825 100644 --- a/agenta-backend/agenta_backend/tasks/evaluations.py +++ b/agenta-backend/agenta_backend/tasks/evaluations.py @@ -3,17 +3,19 @@ import asyncio import logging import traceback - from typing import Any, Dict, List + from celery import shared_task, states from agenta_backend.utils.common import isCloudEE from agenta_backend.models.db_engine import DBEngine -from agenta_backend.services import evaluators_service, llm_apps_service - -from agenta_backend.models.api.evaluation_model import ( - EvaluationStatusEnum, +from agenta_backend.services import ( + evaluators_service, + llm_apps_service, + deployment_manager, + aggregation_service, ) +from agenta_backend.models.api.evaluation_model import EvaluationStatusEnum from agenta_backend.models.db_models import ( AggregatedResult, AppDB, @@ -25,12 +27,6 @@ Error, Result, ) -from agenta_backend.services import ( - evaluators_service, - llm_apps_service, - deployment_manager, - aggregation_service, -) from agenta_backend.services.db_manager import ( create_new_evaluation_scenario, fetch_app_by_id, @@ -44,23 +40,28 @@ EvaluationScenarioResult, check_if_evaluation_contains_failed_evaluation_scenarios, ) +from agenta_backend.services.evaluator_manager import get_evaluators if isCloudEE(): from agenta_backend.commons.models.db_models import AppDB_ as AppDB else: from agenta_backend.models.db_models import AppDB -from agenta_backend.models.db_models import ( - Result, - AggregatedResult, - EvaluationScenarioResult, - EvaluationScenarioInputDB, - EvaluationScenarioOutputDB, -) # Set logger logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) +# Fetch all evaluators and precompute ground truth keys +all_evaluators = get_evaluators() +ground_truth_keys_dict = { + evaluator["key"]: [ + key + for key, value in evaluator.get("settings_template", {}).items() + if value.get("ground_truth_key") is True + ] + for evaluator in all_evaluators +} + @shared_task(queue="agenta_backend.tasks.evaluations.evaluate", bind=True) def evaluate( @@ -74,16 +75,17 @@ def evaluate( lm_providers_keys: Dict[str, Any], ): """ - Evaluate function that performs the evaluation of an app variant using the provided evaluators and testset. - Saves the results in the Database + Evaluates an app variant using the provided evaluators and testset, and saves the results in the database. Args: + self: The task instance. app_id (str): The ID of the app. variant_id (str): The ID of the app variant. evaluators_config_ids (List[str]): The IDs of the evaluators configurations to be used. testset_id (str): The ID of the testset. - rate_limit_config (Dict[str,int]): See LLMRunRateLimit - correct_answer_column (str): The name of the column in the testset that contains the correct answer. + evaluation_id (str): The ID of the evaluation. + rate_limit_config (Dict[str, int]): Configuration for rate limiting. + lm_providers_keys (Dict[str, Any]): Keys for language model providers. Returns: None @@ -212,12 +214,15 @@ def evaluate( evaluators_results: List[EvaluationScenarioResult] = [] # Loop over each evaluator configuration to gather the correct answers and evaluate - all_correct_answers = [] + ground_truth_column_names = [] for evaluator_config_db in evaluator_config_dbs: - correct_answers = parse_correct_answers(evaluator_config_db, data_point) - - all_correct_answers.extend(correct_answers) - + ground_truth_keys = ground_truth_keys_dict.get( + evaluator_config_db.evaluator_key, [] + ) + ground_truth_column_names.extend( + evaluator_config_db.settings_values.get(key, "") + for key in ground_truth_keys + ) logger.debug(f"Evaluating with evaluator: {evaluator_config_db}") result = evaluators_service.evaluate( @@ -243,6 +248,15 @@ def evaluate( logger.debug(f"Result: {result_object}") evaluators_results.append(result_object) + all_correct_answers = [ + CorrectAnswer( + key=ground_truth_column_name, + value=data_point[ground_truth_column_name], + ) + if ground_truth_column_name in data_point + else CorrectAnswer(key=ground_truth_column_name, value="") + for ground_truth_column_name in ground_truth_column_names + ] # 4. We save the result of the eval scenario in the db loop.run_until_complete( @@ -417,20 +431,3 @@ def get_app_inputs(app_variant_parameters, openapi_parameters) -> List[Dict[str, elif param["type"] == "file_url": list_inputs.append({"name": param["name"], "type": "file_url"}) return list_inputs - - -def parse_correct_answers(evaluator_config_db, data_point) -> List[CorrectAnswer]: - """ - Extracts correct answers based on the evaluator configuration. - """ - correct_answers = [] - correct_answer_keys = evaluator_config_db.settings_values.get("correct_answer_keys") - - if not correct_answer_keys: - return [] - - for key in correct_answer_keys: - correct_answer_value = data_point.get(key, "") - correct_answers.append(CorrectAnswer(key=key, value=correct_answer_value)) - - return correct_answers diff --git a/agenta-backend/agenta_backend/tests/unit/test_evaluators.py b/agenta-backend/agenta_backend/tests/unit/test_evaluators.py index a8e5e58391..ff70bbfd9b 100644 --- a/agenta-backend/agenta_backend/tests/unit/test_evaluators.py +++ b/agenta-backend/agenta_backend/tests/unit/test_evaluators.py @@ -180,37 +180,37 @@ def test_auto_contains_json(output, expected): ( "hello world", {"correct_answer": "hello world"}, - {"threshold": 5, "correct_answer_keys": ["correct_answer"]}, + {"threshold": 5, "correct_answer_key": "correct_answer"}, True, ), ( "hello world", {"correct_answer": "hola mundo"}, - {"threshold": 5, "correct_answer_keys": ["correct_answer"]}, + {"threshold": 5, "correct_answer_key": "correct_answer"}, False, ), ( "hello world", {"correct_answer": "hello world!"}, - {"threshold": 2, "correct_answer_keys": ["correct_answer"]}, + {"threshold": 2, "correct_answer_key": "correct_answer"}, True, ), ( "hello world", {"correct_answer": "hello wor"}, - {"threshold": 10, "correct_answer_keys": ["correct_answer"]}, + {"threshold": 10, "correct_answer_key": "correct_answer"}, True, ), ( "hello world", {"correct_answer": "hello worl"}, - {"correct_answer_keys": ["correct_answer"]}, + {"correct_answer_key": "correct_answer"}, 1, ), ( "hello world", {"correct_answer": "helo world"}, - {"correct_answer_keys": ["correct_answer"]}, + {"correct_answer_key": "correct_answer"}, 1, ), ], diff --git a/agenta-backend/agenta_backend/tests/variants_main_router/test_variant_evaluators_router.py b/agenta-backend/agenta_backend/tests/variants_main_router/test_variant_evaluators_router.py index 87c3f6f2ca..9ff8ad60b2 100644 --- a/agenta-backend/agenta_backend/tests/variants_main_router/test_variant_evaluators_router.py +++ b/agenta-backend/agenta_backend/tests/variants_main_router/test_variant_evaluators_router.py @@ -59,7 +59,7 @@ async def test_create_auto_exact_match_evaluator_config( app = await AppDB.find_one(AppDB.app_name == APP_NAME) payload = auto_exact_match_evaluator_config payload["app_id"] = str(app.id) - payload["settings_values"]["correct_answer_keys"] = ["correct_answer"] + payload["settings_values"]["correct_answer_key"] = "correct_answer" response = await test_client.post( f"{BACKEND_API_HOST}/evaluators/configs/", json=payload, timeout=timeout @@ -76,7 +76,7 @@ async def test_create_auto_similarity_match_evaluator_config( app = await AppDB.find_one(AppDB.app_name == APP_NAME) payload = auto_similarity_match_evaluator_config payload["app_id"] = str(app.id) - payload["settings_values"]["correct_answer_keys"] = ["correct_answer"] + payload["settings_values"]["correct_answer_key"] = "correct_answer" response = await test_client.post( f"{BACKEND_API_HOST}/evaluators/configs/", json=payload, timeout=timeout @@ -94,7 +94,7 @@ async def test_create_auto_regex_test_evaluator_config( payload = auto_regex_test_evaluator_config payload["app_id"] = str(app.id) payload["settings_values"]["regex_pattern"] = "^ig\\d{3}$" - payload["settings_values"]["correct_answer_keys"] = ["correct_answer"] + payload["settings_values"]["correct_answer_key"] = "correct_answer" response = await test_client.post( f"{BACKEND_API_HOST}/evaluators/configs/", json=payload, timeout=timeout @@ -111,7 +111,7 @@ async def test_create_auto_webhook_test_evaluator_config( app = await AppDB.find_one(AppDB.app_name == APP_NAME) payload = auto_webhook_test_evaluator_config payload["app_id"] = str(app.id) - payload["settings_values"]["correct_answer_keys"] = ["correct_answer"] + payload["settings_values"]["correct_answer_key"] = "correct_answer" response = await test_client.post( f"{BACKEND_API_HOST}/evaluators/configs/", json=payload, timeout=timeout @@ -128,7 +128,7 @@ async def test_create_auto_ai_critique_evaluator_config( app = await AppDB.find_one(AppDB.app_name == APP_NAME) payload = auto_ai_critique_evaluator_config payload["app_id"] = str(app.id) - payload["settings_values"]["correct_answer_keys"] = ["correct_answer"] + payload["settings_values"]["correct_answer_key"] = "correct_answer" response = await test_client.post( f"{BACKEND_API_HOST}/evaluators/configs/", json=payload, timeout=timeout @@ -150,7 +150,31 @@ async def test_get_evaluator_configs(): @pytest.mark.asyncio -async def test_create_evaluation(): +async def test_create_evaluation_auto_exact_match(): + await create_evaluation_with_evaluator("auto_exact_match_evaluator_config") + + +@pytest.mark.asyncio +async def test_create_evaluation_auto_similarity_match(): + await create_evaluation_with_evaluator("auto_similarity_match_evaluator_config") + + +@pytest.mark.asyncio +async def test_create_evaluation_auto_regex_test(): + await create_evaluation_with_evaluator("auto_regex_test_evaluator_config") + + +@pytest.mark.asyncio +async def test_create_evaluation_auto_webhook_test(): + await create_evaluation_with_evaluator("auto_webhook_test_evaluator_config") + + +@pytest.mark.asyncio +async def test_create_evaluation_auto_ai_critique(): + await create_evaluation_with_evaluator("auto_ai_critique_evaluator_config") + + +async def create_evaluation_with_evaluator(evaluator_config_name): # Fetch app, app_variant and testset app = await AppDB.find_one(AppDB.app_name == APP_NAME) app_variant = await AppVariantDB.find_one(AppVariantDB.app.id == app.id) @@ -179,7 +203,8 @@ async def test_create_evaluation(): list_of_configs_ids = [] evaluator_configs = response.json() for evaluator_config in evaluator_configs: - list_of_configs_ids.append(evaluator_config["id"]) + if evaluator_config["evaluator_key"] == evaluator_config_name: + list_of_configs_ids.append(evaluator_config["id"]) # Update payload with list of configs ids payload["evaluators_configs"] = list_of_configs_ids @@ -201,20 +226,20 @@ async def test_create_evaluation(): ) assert response_data is not None + # Wait for evaluation to finish + evaluation_id = response_data["id"] + await wait_for_evaluation_to_finish(evaluation_id) + + # Fetch evaluation results + await fetch_evaluation_results(evaluation_id) -@pytest.mark.asyncio -async def test_fetch_evaluation_status(): - evaluations = ( - await EvaluationDB.find().to_list() - ) # will return only one in this case - evaluation = evaluations[0] - # Prepare and start short-polling request +async def wait_for_evaluation_to_finish(evaluation_id): max_attempts = 12 intervals = 5 # seconds for _ in range(max_attempts): response = await test_client.get( - f"{BACKEND_API_HOST}/evaluations/{str(evaluation.id)}/status/", + f"{BACKEND_API_HOST}/evaluations/{evaluation_id}/status/", timeout=timeout, ) response_data = response.json() @@ -228,21 +253,14 @@ async def test_fetch_evaluation_status(): ), f"Evaluation status did not become '{EvaluationStatusEnum.EVALUATION_FINISHED}' within the specified polling time" -@pytest.mark.asyncio -async def test_fetch_evaluation_results(): - evaluations = ( - await EvaluationDB.find().to_list() - ) # will return only one in this case - evaluation = evaluations[0] - +async def fetch_evaluation_results(evaluation_id): response = await test_client.get( - f"{BACKEND_API_HOST}/evaluations/{str(evaluation.id)}/results/", timeout=timeout + f"{BACKEND_API_HOST}/evaluations/{evaluation_id}/results/", timeout=timeout ) response_data = response.json() assert response.status_code == 200 - assert response_data["evaluation_id"] == str(evaluation.id) - assert len(response_data["results"]) == 7 + assert response_data["evaluation_id"] == evaluation_id @pytest.mark.asyncio diff --git a/agenta-backend/poetry.lock b/agenta-backend/poetry.lock index 488127f4ac..9fafec4bc4 100644 --- a/agenta-backend/poetry.lock +++ b/agenta-backend/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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uvicorn = "^0.22.0" sqlmodel = "^0.0.8" @@ -19,8 +19,6 @@ python-multipart = "^0.0.6" backoff = "^2.2.1" redis = "^4.6.0" aiodocker = "^0.21.0" -openai = "^0.27.8" -langchain = "^0.0.329" supertokens-python = "^0.15.1" sendgrid = "^6.10.0" restrictedpython = { version = "^6.2", python = ">=3.9,<3.12" } @@ -35,6 +33,8 @@ watchdog = {extras = ["watchmedo"], version = "^3.0.0"} beanie = "^1.25.0" newrelic = "^9.8.0" aioboto3 = "^12.4.0" +ragas = "^0.1.8" +openai = "^1.30.4" [tool.poetry.group.dev.dependencies] pytest = "^7.3.1" diff --git a/agenta-web/src/components/pages/evaluations/evaluators/AdvancedSettings.tsx b/agenta-web/src/components/pages/evaluations/evaluators/AdvancedSettings.tsx index b86c54d8b7..9e3c4ea4a4 100644 --- a/agenta-web/src/components/pages/evaluations/evaluators/AdvancedSettings.tsx +++ b/agenta-web/src/components/pages/evaluations/evaluators/AdvancedSettings.tsx @@ -1,11 +1,9 @@ import React from "react" -import {Form, Input, Collapse} from "antd" -import {CaretRightOutlined} from "@ant-design/icons" +import {Form, Input, InputNumber, Switch, Tooltip, Collapse, theme} from "antd" +import {CaretRightOutlined, InfoCircleOutlined} from "@ant-design/icons" import {createUseStyles} from "react-jss" - -type AdvancedSettings = { - correct_answer_keys: string[] -} +import {Editor} from "@monaco-editor/react" +import {useAppTheme} from "@/components/Layout/ThemeContextProvider" const useStyles = createUseStyles((theme: any) => ({ label: { @@ -13,6 +11,11 @@ const useStyles = createUseStyles((theme: any) => ({ alignItems: "center", gap: "0.5rem", }, + editor: { + border: `1px solid ${theme.colorBorder}`, + borderRadius: theme.borderRadius, + overflow: "hidden", + }, })) type AdvancedSettingsProps = { @@ -21,11 +24,8 @@ type AdvancedSettingsProps = { const AdvancedSettings: React.FC = ({settings}) => { const classes = useStyles() - - const initialValues = settings.reduce((acc, field) => { - acc[field.key] = field.default - return acc - }, {}) + const {appTheme} = useAppTheme() + const {token} = theme.useToken() return ( = ({settings}) => { className={"my-[10px]"} > - {settings.map((field) => ( - - {field.label} - - } - rules={[ - {required: field.required ?? true, message: "This field is required"}, - ]} - > - {field.key === "correct_answer_keys" && } - - ))} + {settings.map((field) => { + const rules = [ + {required: field.required ?? true, message: "This field is required"}, + ] + + return ( + + {field.label} + {field.description && ( + + + + )} + + } + initialValue={field.default} + rules={rules} + > + {field.type === "string" || field.type === "regex" ? ( + + ) : field.type === "number" ? ( + + ) : field.type === "boolean" || field.type === "bool" ? ( + + ) : field.type === "text" ? ( + + ) : field.type === "code" ? ( + + ) : field.type === "object" ? ( + + ) : null} + + ) + })} ) diff --git a/agenta-web/src/components/pages/evaluations/evaluators/EvaluatorCard.tsx b/agenta-web/src/components/pages/evaluations/evaluators/EvaluatorCard.tsx index 7c813ad6c5..bd3550bd0b 100644 --- a/agenta-web/src/components/pages/evaluations/evaluators/EvaluatorCard.tsx +++ b/agenta-web/src/components/pages/evaluations/evaluators/EvaluatorCard.tsx @@ -84,22 +84,14 @@ const EvaluatorCard: React.FC = ({evaluatorConfig, onEdit, onSuccessDelet return ( , - , - ] - } + actions={[ + , + , + ]} data-cy="evaluator-card" >
diff --git a/agenta-web/src/components/pages/evaluations/evaluators/NewEvaluatorModal.tsx b/agenta-web/src/components/pages/evaluations/evaluators/NewEvaluatorModal.tsx index e1875a676e..5fd8fc1bf8 100644 --- a/agenta-web/src/components/pages/evaluations/evaluators/NewEvaluatorModal.tsx +++ b/agenta-web/src/components/pages/evaluations/evaluators/NewEvaluatorModal.tsx @@ -8,26 +8,9 @@ import { createEvaluatorConfig, updateEvaluatorConfig, } from "@/services/evaluations" -import { - ArrowLeftOutlined, - CaretRightOutlined, - EditOutlined, - InfoCircleOutlined, - PlusOutlined, -} from "@ant-design/icons" +import {ArrowLeftOutlined, EditOutlined, InfoCircleOutlined, PlusOutlined} from "@ant-design/icons" import {Editor} from "@monaco-editor/react" -import { - Button, - Collapse, - Form, - Input, - InputNumber, - Modal, - Switch, - Table, - Tooltip, - theme, -} from "antd" +import {Button, Form, Input, InputNumber, Modal, Switch, Table, Tooltip, theme} from "antd" import {Rule} from "antd/es/form" import {useAtom} from "jotai" import Image from "next/image" @@ -240,9 +223,7 @@ const NewEvaluatorModal: React.FC = ({ ...props }) => { const classes = useStyles() - const evaluators = useAtom(evaluatorsAtom)[0].filter( - (item) => !item.direct_use || item.settings_template.correct_answer_keys, - ) + const evaluators = useAtom(evaluatorsAtom)[0] const [selectedEval, setSelectedEval] = useState(null) const [submitLoading, setSubmitLoading] = useState(false) const [searchTerm, setSearchTerm] = useState("") @@ -268,6 +249,7 @@ const NewEvaluatorModal: React.FC = ({ .map((key) => ({ key, ...selectedEval?.settings_template[key]!, + advanced: selectedEval?.settings_template[key]?.advanced || false, })), [selectedEval], ) @@ -282,24 +264,22 @@ const NewEvaluatorModal: React.FC = ({ } }, [newEvalModalConfigOpen]) - const advancedSettingsFields = evalFields.filter((field) => field.key === "correct_answer_keys") + const advancedSettingsFields = evalFields.filter((field) => field.advanced) + const basicSettingsFields = evalFields.filter((field) => !field.advanced) + console.log(evalFields) + console.log(basicSettingsFields) const onSubmit = (values: CreateEvaluationConfigData) => { setSubmitLoading(true) if (!selectedEval?.key) throw new Error("No selected key") const settingsValues = values.settings_values || {} - if (settingsValues.correct_answer_keys) { - settingsValues.correct_answer_keys = Array.isArray(settingsValues.correct_answer_keys) - ? settingsValues.correct_answer_keys - : [settingsValues.correct_answer_keys] - } - if ( - !settingsValues.correct_answer_keys && - selectedEval.settings_template.correct_answer_keys + !settingsValues.correct_answer_key && + selectedEval.settings_template.correct_answer_key.default ) { - settingsValues["correct_answer_keys"] = ["correct_answer"] + settingsValues["correct_answer_key"] = + selectedEval.settings_template.correct_answer_key.default } const data = { @@ -435,7 +415,7 @@ const NewEvaluatorModal: React.FC = ({ - {evalFields.map((field) => ( + {basicSettingsFields.map((field) => (