diff --git a/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py b/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py index e1a8d252f..85879b673 100644 --- a/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py +++ b/assessment_module_manager/assessment_module_manager/endpoints/health_endpoint.py @@ -27,6 +27,7 @@ class HealthResponse(BaseModel): """ Response indicating whether the Assessment Module Manager is healthy, and whether all the modules are healthy (i.e. reachable). + Additional information about the modules is also provided. """ status: str = Field(const=True, default="ok", example="ok") modules: dict = Field( @@ -35,7 +36,8 @@ class HealthResponse(BaseModel): "module_example": { "url": "http://localhost:5001", "type": "programming", - "healthy": True + "healthy": True, + "supportsEvaluation": True } } ] @@ -56,6 +58,7 @@ async def get_health() -> HealthResponse: "url": module.url, "type": module.type, "healthy": await is_healthy(module), + "supportsEvaluation": module.supports_evaluation } for module in get_modules() } diff --git a/assessment_module_manager/assessment_module_manager/module/list_modules.py b/assessment_module_manager/assessment_module_manager/module/list_modules.py index da4a0daa9..de4a4aa05 100644 --- a/assessment_module_manager/assessment_module_manager/module/list_modules.py +++ b/assessment_module_manager/assessment_module_manager/module/list_modules.py @@ -20,6 +20,7 @@ def list_modules() -> List[Module]: name=module, url=cast(AnyHttpUrl, os.environ.get(f"{module.upper()}_URL", modules_config[module]["url"])), type=ExerciseType(modules_config[module]["type"]), + supports_evaluation=modules_config[module].getboolean("supports_evaluation"), ) for module in modules_config.sections() ] diff --git a/assessment_module_manager/assessment_module_manager/module/module.py b/assessment_module_manager/assessment_module_manager/module/module.py index 35dbb6da1..65e99931f 100644 --- a/assessment_module_manager/assessment_module_manager/module/module.py +++ b/assessment_module_manager/assessment_module_manager/module/module.py @@ -8,3 +8,4 @@ class Module(BaseModel): name: str = Field(example="module_example") url: AnyHttpUrl = Field(example="http://localhost:5001") type: ExerciseType = Field(example=ExerciseType.text) + supports_evaluation: bool = Field(description="Whether the module supports evaluation", example=True) diff --git a/assessment_module_manager/modules.docker.ini b/assessment_module_manager/modules.docker.ini index db9d22854..4c5f74e9b 100644 --- a/assessment_module_manager/modules.docker.ini +++ b/assessment_module_manager/modules.docker.ini @@ -1,19 +1,24 @@ [module_example] url = http://module-example:5001 type = programming +supports_evaluation = true [module_programming_llm] url = http://module-programming-llm:5002 type = programming +supports_evaluation = false [module_text_llm] url = http://module-text-llm:5003 type = text +supports_evaluation = true [module_text_cofee] url = http://module-text-cofee:5004 type = text +supports_evaluation = false [module_programming_themisml] url = http://module-programming-themisml:5005 -type = programming \ No newline at end of file +type = programming +supports_evaluation = false \ No newline at end of file diff --git a/assessment_module_manager/modules.ini b/assessment_module_manager/modules.ini index 0dde7b074..73fd4c91b 100644 --- a/assessment_module_manager/modules.ini +++ b/assessment_module_manager/modules.ini @@ -1,19 +1,24 @@ [module_example] url = http://localhost:5001 type = programming +supports_evaluation = true [module_programming_llm] url = http://localhost:5002 type = programming +supports_evaluation = false [module_text_llm] url = http://localhost:5003 type = text +supports_evaluation = true [module_text_cofee] url = http://localhost:5004 type = text +supports_evaluation = false [module_programming_themisml] url = http://localhost:5005 type = programming +supports_evaluation = false diff --git a/athena/athena/__init__.py b/athena/athena/__init__.py index 90fb46e62..8a67e5315 100644 --- a/athena/athena/__init__.py +++ b/athena/athena/__init__.py @@ -6,7 +6,7 @@ from .schemas import ExerciseType, GradingCriterion, StructuredGradingInstruction from .metadata import emit_meta, get_meta from .experiment import get_experiment_environment -from .endpoints import submission_selector, submissions_consumer, feedback_consumer, feedback_provider, config_schema_provider # type: ignore +from .endpoints import submission_selector, submissions_consumer, feedback_consumer, feedback_provider, config_schema_provider, evaluation_provider # type: ignore @app.get("/") @@ -28,6 +28,7 @@ def run_module(): "feedback_consumer", "feedback_provider", "config_schema_provider", + "evaluation_provider", "emit_meta", "get_meta", "get_experiment_environment", diff --git a/athena/athena/endpoints.py b/athena/athena/endpoints.py index 8e9d4dcc5..72787eb31 100644 --- a/athena/athena/endpoints.py +++ b/athena/athena/endpoints.py @@ -358,4 +358,63 @@ def config_schema_provider(cls: Type[C]) -> Type[C]: async def wrapper(): return cls.schema() - return cls \ No newline at end of file + return cls + + +def evaluation_provider(func: Union[ + Callable[[E, S, List[F], List[F]], Any], + Callable[[E, S, List[F], List[F]], Coroutine[Any, Any, Any]] +]): + """ + Provide evaluated feedback to the Assessment Module Manager. + + Note: The evaluation provider is usually called during the research and development phase (by the Playground). + Return arbitrary evaluation results. + + This decorator can be used with several types of functions: synchronous or asynchronous. + + Examples: + Below are some examples of possible functions that you can decorate with this decorator: + + Without using module config (both synchronous and asynchronous forms): + >>> @evaluation_provider + ... def sync_evaluate_feedback( + ... exercise: Exercise, submission: Submission, + ... true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback] + ... ) -> Any: + ... # evaluate predicted feedback here and return evaluation results + + >>> @feedback_provider + ... async def async_evaluate_feedback( + ... exercise: Exercise, submission: Submission, + ... true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback] + ... ) -> Any: + ... # evaluate predicted feedback here and return evaluation results + """ + exercise_type = inspect.signature(func).parameters["exercise"].annotation + submission_type = inspect.signature(func).parameters["submission"].annotation + feedback_type = inspect.signature(func).parameters["predicted_feedbacks"].annotation.__args__[0] + + @app.post("/evaluation", responses=module_responses) + @authenticated + @with_meta + async def wrapper( + exercise: exercise_type, + submission: submission_type, + true_feedbacks: List[feedback_type], + predicted_feedbacks: List[feedback_type], + ): + # Retrieve existing metadata for the exercise, submission and feedback + exercise.meta.update(get_stored_exercise_meta(exercise) or {}) + submission.meta.update(get_stored_submission_meta(submission) or {}) + for feedback in true_feedbacks + predicted_feedbacks: + feedback.meta.update(get_stored_feedback_meta(feedback) or {}) + + # Call the actual provider + if inspect.iscoroutinefunction(func): + evaluation = await func(exercise, submission, true_feedbacks, predicted_feedbacks) + else: + evaluation = func(exercise, submission, true_feedbacks, predicted_feedbacks) + + return evaluation + return wrapper \ No newline at end of file diff --git a/docs/module/structure.rst b/docs/module/structure.rst index 31c206f55..126ab8b98 100644 --- a/docs/module/structure.rst +++ b/docs/module/structure.rst @@ -94,7 +94,7 @@ Example: ) ] -Provide Config Schema +Provide Config Schema (Optional) ~~~~~~~~~~~~~~~~~~~~~~ Get a schema for config options of the module as json schema. The config complying to the schema can then be provided in the header of a request `X-Module-Config` to override the default values. The module can decorate one pydantic model with ``@config_schema_provider`` to provide the schema and should have default values set for all fields as default configuration. The configuration class can be appended to the function signature of all other decorators to provide the configuration to the function. @@ -108,6 +108,37 @@ Example: debug: bool = Field(False, description="Whether the module is in debug mode.") ... +Provide Evaluation (Optional) +~~~~~~~~~~~~~~~~~~ +Get an arbitrary evaluation for a submission with historical ``true_feedback`` and feedback suggestions ``predicted_feedback``. The Playground would usually call this when conducting an evaluation during an experiment. The module will receive the request at the function annotated with ``@evaluation_provider``. + +If you want to have the ``/evaluation`` endpoint available during the Playground evaluation mode, you need to set ``supports_evaluation = true`` in the ``modules.ini`` and ``modules.docker.ini`` files. + +Example: + .. code-block:: python + + from athena import * + + @evaluation_provider + def evaluate_feedback(exercise: Exercise, submission: Submission, true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback]) -> Any: + # Do something with the true and predicted feedback and return the evaluation result + ... + # Example: Generate some example evaluation result + evaluation_results = [] + true_feedback_embeddings = [random.random() for _ in true_feedbacks] + predicted_feedback_embeddings = [random.random() for _ in predicted_feedbacks] + for feedback, embedding in zip(predicted_feedbacks, predicted_feedback_embeddings): + feedback_evaluation = { + "feedback_id": feedback.id, + "embedding": embedding, + "has_match": len([t for t in true_feedback_embeddings if abs(t - embedding) < 0.1]) > 0, + "correctness": random.random() + } + evaluation_results.append(feedback_evaluation) + ... + # Return arbitrary evaluation results + return evaluation_results + Environment Variables --------------------- You should provide at least the following environment variables for your module to work properly: diff --git a/module_example/module_example/__main__.py b/module_example/module_example/__main__.py index 7bdef91fe..182f160bb 100644 --- a/module_example/module_example/__main__.py +++ b/module_example/module_example/__main__.py @@ -1,10 +1,11 @@ """ Entry point for the module_example module. """ -from typing import List +import random +from typing import List, Any from pydantic import BaseModel, Field -from athena import app, config_schema_provider, submissions_consumer, submission_selector, feedback_consumer, feedback_provider, emit_meta +from athena import app, config_schema_provider, submissions_consumer, submission_selector, feedback_consumer, feedback_provider, evaluation_provider, emit_meta from athena.programming import Exercise, Submission, Feedback from athena.logger import logger from athena.storage import store_exercise, store_submissions, store_feedback @@ -139,5 +140,30 @@ def suggest_feedback(exercise: Exercise, submission: Submission, module_config: ] +# Only if it makes sense for a module (Optional) +@evaluation_provider +def evaluate_feedback(exercise: Exercise, submission: Submission, true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback]) -> Any: + logger.info( + "evaluate_feedback: Evaluation for submission %d of exercise %d was requested with %d true and %d predicted feedbacks", + submission.id, exercise.id, len(true_feedbacks), len(predicted_feedbacks) + ) + + # Do something with the true and predicted feedback and return the evaluation result + # Generate some example evaluation result + evaluation_results = [] + true_feedback_embeddings = [random.random() for _ in true_feedbacks] + predicted_feedback_embeddings = [random.random() for _ in predicted_feedbacks] + for feedback, embedding in zip(predicted_feedbacks, predicted_feedback_embeddings): + feedback_evaluation = { + "feedback_id": feedback.id, + "embedding": embedding, + "has_match": len([t for t in true_feedback_embeddings if abs(t - embedding) < 0.1]) > 0, + "correctness": random.random() + } + evaluation_results.append(feedback_evaluation) + + return evaluation_results + + if __name__ == "__main__": app.start() diff --git a/module_programming_llm/module_programming_llm/generate_suggestions_by_file.py b/module_programming_llm/module_programming_llm/generate_suggestions_by_file.py index 3d2238b31..60a43b6ad 100644 --- a/module_programming_llm/module_programming_llm/generate_suggestions_by_file.py +++ b/module_programming_llm/module_programming_llm/generate_suggestions_by_file.py @@ -227,12 +227,19 @@ async def generate_suggestions_by_file(exercise: Exercise, submission: Submissio ] ) + grading_instruction_ids = set( + grading_instruction.id + for criterion in exercise.grading_criteria or [] + for grading_instruction in criterion.structured_grading_instructions + ) + feedbacks: List[Feedback] = [] for prompt_input, result in zip(prompt_inputs, results): file_path = prompt_input["file_path"] if result is None: continue for feedback in result.feedbacks: + grading_instruction_id = feedback.grading_instruction_id if feedback.grading_instruction_id in grading_instruction_ids else None feedbacks.append(Feedback( exercise_id=exercise.id, submission_id=submission.id, @@ -242,7 +249,7 @@ async def generate_suggestions_by_file(exercise: Exercise, submission: Submissio line_start=feedback.line_start, line_end=feedback.line_end, credits=feedback.credits, - structured_grading_instruction_id=feedback.grading_instruction_id, + structured_grading_instruction_id=grading_instruction_id, meta={} )) diff --git a/module_text_llm/.env.example b/module_text_llm/.env.example index 2ecf0a8f4..aedc5bdba 100644 --- a/module_text_llm/.env.example +++ b/module_text_llm/.env.example @@ -14,6 +14,12 @@ DATABASE_URL=sqlite:///../data/data.sqlite # See below for options, available models are also logged on startup LLM_DEFAULT_MODEL="azure_openai_gpt-35" +# Enable LLM-as-a-judge approach 0 = disabled, 1 = enabled +LLM_ENABLE_LLM_AS_A_JUDGE=1 +# Evaluation model to use for the LLM-as-a-judge approach [Only important if you want to use it in the /evaluate endpoint] +# See below for options, available models are also logged on startup +LLM_EVALUATION_MODEL="azure_openai_gpt-4" + # Standard OpenAI (Non-Azure) [leave blank if not used] # Model names prefixed with `openai_` followed by the model name, e.g. `openai_text-davinci-003` # A list of models can be found in `module_text_llm/helpers/models/openai.py` (openai_models) diff --git a/module_text_llm/module_text_llm/__main__.py b/module_text_llm/module_text_llm/__main__.py index e9bf8d448..49d069635 100644 --- a/module_text_llm/module_text_llm/__main__.py +++ b/module_text_llm/module_text_llm/__main__.py @@ -1,14 +1,18 @@ -from typing import List +import json +import os +from typing import List, Any import nltk import tiktoken -from athena import app, submission_selector, submissions_consumer, feedback_consumer, feedback_provider +from athena import app, submission_selector, submissions_consumer, feedback_consumer, feedback_provider, evaluation_provider from athena.text import Exercise, Submission, Feedback from athena.logger import logger from module_text_llm.config import Configuration +from module_text_llm.evaluation import get_feedback_statistics, get_llm_statistics from module_text_llm.generate_suggestions import generate_suggestions +from module_text_llm.generate_evaluation import generate_evaluation @submissions_consumer @@ -33,6 +37,32 @@ async def suggest_feedback(exercise: Exercise, submission: Submission, module_co return await generate_suggestions(exercise, submission, module_config.approach, module_config.debug) +@evaluation_provider +async def evaluate_feedback( + exercise: Exercise, submission: Submission, + true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback], +) -> Any: + logger.info( + "evaluate_feedback: Evaluation for submission %d of exercise %d was requested with %d true and %d predicted feedbacks", + submission.id, exercise.id, len( + true_feedbacks), len(predicted_feedbacks) + ) + + evaluation = {} + + # 1. LLM as a judge + if len(predicted_feedbacks) > 0 and bool(os.environ.get("LLM_ENABLE_LLM_AS_A_JUDGE")): + evaluation["llm_as_a_judge"] = await generate_evaluation(exercise, submission, true_feedbacks, predicted_feedbacks) + + # 2. LangSmith runs, token usage, and respose times + if bool(os.environ.get("LANGCHAIN_TRACING_V2")): + evaluation["llm_statistics"] = get_llm_statistics(submission) + + # 3. Feedback statistics + evaluation["feedback_statistics"] = get_feedback_statistics(exercise, true_feedbacks, predicted_feedbacks) + + return evaluation + if __name__ == "__main__": nltk.download("punkt") tiktoken.get_encoding("cl100k_base") diff --git a/module_text_llm/module_text_llm/evaluation.py b/module_text_llm/module_text_llm/evaluation.py new file mode 100644 index 000000000..f0af94e3e --- /dev/null +++ b/module_text_llm/module_text_llm/evaluation.py @@ -0,0 +1,111 @@ +import json +import os +from typing import List + +from langsmith import Client as LangSmithClient +from langsmith.schemas import Run + +from athena import get_experiment_environment +from athena.text import Exercise, Submission, Feedback + + +def get_llm_statistics(submission: Submission): + experiment = get_experiment_environment() + client = LangSmithClient() + project_name = os.environ.get("LANGCHAIN_PROJECT") + runs = list(client.list_runs( + project_name=project_name, + filter=f'has(tags, "submission-{submission.id}")' if experiment.run_id is None else f'and(has(tags, "run-{experiment.run_id}"), has(tags, "submission-{submission.id}"))' + )) + + def get_statistics(runs: List[Run]): + return { + "response_time": sum((run.end_time - run.start_time).total_seconds() for run in runs if run.end_time is not None), + "prompt_tokens": sum(run.prompt_tokens for run in runs if run.prompt_tokens is not None), + "completion_tokens": sum(run.completion_tokens for run in runs if run.completion_tokens is not None), + "total_tokens": sum(run.total_tokens for run in runs if run.total_tokens is not None), + } + + suggestion_runs = [] + evaluation_runs = [] + for run in runs: + if "evaluation" in (run.tags or []): + evaluation_runs.append(run) + else: + suggestion_runs.append(run) + + llm_statistics = {} + if suggestion_runs or evaluation_runs: + if suggestion_runs: + llm_statistics["suggestions"] = { + "count": len(suggestion_runs), + **get_statistics(suggestion_runs), + "runs": [json.loads(run.json()) for run in suggestion_runs] + } + if evaluation_runs: + llm_statistics["evaluation"] = { + "count": len(evaluation_runs), + **get_statistics(evaluation_runs), + "runs": [json.loads(run.json()) for run in evaluation_runs] + } + + return llm_statistics + + +def get_feedback_statistics(exercise: Exercise, true_feedbacks: List[Feedback], predicted_feedbacks: List[Feedback]): + actual_feedback_count = len(true_feedbacks) + actual_feedback_with_grading_instructions = [] + suggestions_count = len(predicted_feedbacks) + suggestions_with_grading_instructions = [] + + # Init usage counts for SGIs + actual_sgi_usage = { + sgi.id: 0 for criterion in exercise.grading_criteria or [] for sgi in criterion.structured_grading_instructions + } + suggested_sgi_usage = { + sgi.id: 0 for criterion in exercise.grading_criteria or [] for sgi in criterion.structured_grading_instructions + } + + # Count SGIs in actual feedbacks + for feedback in true_feedbacks: + if feedback.structured_grading_instruction_id and feedback.structured_grading_instruction_id in actual_sgi_usage: + actual_feedback_with_grading_instructions.append(feedback) + actual_sgi_usage[feedback.structured_grading_instruction_id] += 1 + + # Count SGIs in suggested feedbacks + for feedback in predicted_feedbacks: + if feedback.structured_grading_instruction_id and feedback.structured_grading_instruction_id in suggested_sgi_usage: + suggestions_with_grading_instructions.append(feedback) + suggested_sgi_usage[feedback.structured_grading_instruction_id] += 1 + + unmatched_suggestions_with_grading_instructions = suggestions_with_grading_instructions.copy() + + # Match SGIs + matched_feedback = 0 + unmatched_feedback = actual_feedback_count - len(actual_feedback_with_grading_instructions) + unmatched_suggestions = suggestions_count - len(suggestions_with_grading_instructions) + + for feedback in actual_feedback_with_grading_instructions: + for index, suggestion in enumerate(unmatched_suggestions_with_grading_instructions): + if feedback.structured_grading_instruction_id == suggestion.structured_grading_instruction_id: + matched_feedback += 1 + del unmatched_suggestions_with_grading_instructions[index] + break + else: + unmatched_feedback += 1 + + unmatched_suggestions += len(unmatched_suggestions_with_grading_instructions) + + feedback_statistics = { + "actual_feedback_count": actual_feedback_count, + "suggestions_count": suggestions_count, + "actual_feedback_with_grading_instructions_count": len(actual_feedback_with_grading_instructions), + "suggestions_with_grading_instructions_count":len(suggestions_with_grading_instructions), + "actual_sgi_usage": actual_sgi_usage, + "suggested_sgi_usage": suggested_sgi_usage, + "matched_feedback": matched_feedback, + "unmatched_feedback": unmatched_feedback, + "unmatched_suggestions": unmatched_suggestions, + } + + return feedback_statistics diff --git a/module_text_llm/module_text_llm/generate_evaluation.py b/module_text_llm/module_text_llm/generate_evaluation.py new file mode 100644 index 000000000..4c4a9969b --- /dev/null +++ b/module_text_llm/module_text_llm/generate_evaluation.py @@ -0,0 +1,95 @@ +from typing import List, Sequence, Dict, Literal +from pydantic import BaseModel, Field +import json + +from athena.text import Exercise, Submission, Feedback +from athena.logger import logger + +from module_text_llm.helpers.models import evaluation_model +from module_text_llm.helpers.llm_utils import ( + get_chat_prompt_with_formatting_instructions, + check_prompt_length_and_omit_features_if_necessary, + predict_and_parse +) +from module_text_llm.helpers.utils import add_sentence_numbers, get_line_range_from_index_range +from module_text_llm.prompts.generate_evaluation import system_message, human_message + + +class AccuracyMetric(BaseModel): + id: int = Field(..., description="Feedback ID") + reasoning: str = Field(..., description="Step-by-step critical reasoning of the labels") + acceptance_label: Literal["accepted", "rejected"] = Field(..., description="Estimated acceptance label") + level_of_needed_modification_label: Literal["no", "minor", "major"] = Field(..., description="Estimated level of needed modification") + +class Evaluation(BaseModel): + metrics: Sequence[AccuracyMetric] = Field(...) + + +async def generate_evaluation( + exercise: Exercise, + submission: Submission, + true_feedbacks: List[Feedback], + predicted_feedbacks: List[Feedback] +) -> Dict[int, dict]: + + if evaluation_model is None: + raise EnvironmentError("No evaluation model available, please set up LLM_EVALUATION_MODEL correctly" + "by setting it to one of the available models logged during startup.") + max_input_tokens = 3000 + + def feedback_to_dict(feedback: Feedback): + line_start, line_end = get_line_range_from_index_range( + feedback.index_start, feedback.index_end, submission.text) + return { + "id": feedback.id, + "title": feedback.title, + "description": feedback.description, + "line_start": line_start, + "line_end": line_end, + "credits": feedback.credits + } + + prompt_input = { + "submission": add_sentence_numbers(submission.text), + "true_feedbacks": json.dumps([feedback_to_dict(feedback) for feedback in true_feedbacks]), + "predicted_feedbacks": json.dumps([feedback_to_dict(feedback) for feedback in predicted_feedbacks]), + } + + chat_prompt = get_chat_prompt_with_formatting_instructions( + model=evaluation_model, + system_message=system_message, + human_message=human_message, + pydantic_object=Evaluation + ) + + # Check if the prompt is too long and omit features if necessary (in order of importance) + omittable_features = ["submission"] + prompt_input, should_run = check_prompt_length_and_omit_features_if_necessary( + prompt=chat_prompt, + prompt_input=prompt_input, + max_input_tokens=max_input_tokens, + omittable_features=omittable_features, + debug=False + ) + + if not should_run: + logger.warning("Evaluation input too long. Skipping.") + return {} + + result = await predict_and_parse( + model=evaluation_model, + chat_prompt=chat_prompt, + prompt_input=prompt_input, + pydantic_object=Evaluation, + tags=[ + f"exercise-{exercise.id}", + f"submission-{submission.id}", + "evaluation" + ] + ) + + if result is None: + logger.warning("Evaluation failed. Skipping.") + return {} + + return { item.id: item.dict() for item in result.metrics } diff --git a/module_text_llm/module_text_llm/generate_suggestions.py b/module_text_llm/module_text_llm/generate_suggestions.py index a279d98c8..d43563ad6 100644 --- a/module_text_llm/module_text_llm/generate_suggestions.py +++ b/module_text_llm/module_text_llm/generate_suggestions.py @@ -94,9 +94,16 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi if result is None: return [] + grading_instruction_ids = set( + grading_instruction.id + for criterion in exercise.grading_criteria or [] + for grading_instruction in criterion.structured_grading_instructions + ) + feedbacks = [] for feedback in result.feedbacks: index_start, index_end = get_index_range_from_line_range(feedback.line_start, feedback.line_end, submission.text) + grading_instruction_id = feedback.grading_instruction_id if feedback.grading_instruction_id in grading_instruction_ids else None feedbacks.append(Feedback( exercise_id=exercise.id, submission_id=submission.id, @@ -105,7 +112,7 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi index_start=index_start, index_end=index_end, credits=feedback.credits, - structured_grading_instruction_id=feedback.grading_instruction_id, + structured_grading_instruction_id=grading_instruction_id, meta={} )) diff --git a/module_text_llm/module_text_llm/helpers/models/__init__.py b/module_text_llm/module_text_llm/helpers/models/__init__.py index 4d2fe5a65..144bcf923 100644 --- a/module_text_llm/module_text_llm/helpers/models/__init__.py +++ b/module_text_llm/module_text_llm/helpers/models/__init__.py @@ -1,10 +1,16 @@ import os -from typing import Type, Union, List +from typing import Type, Union, List, Optional +from langchain.base_language import BaseLanguageModel + from module_text_llm.helpers.models.model_config import ModelConfig DefaultModelConfig: Type[ModelConfig] default_model_name = os.environ.get("LLM_DEFAULT_MODEL") +evaluation_model_name = os.environ.get("LLM_EVALUATION_MODEL") + +# Model used during evaluation for judging the output (should be a more powerful model) +evaluation_model: Optional[BaseLanguageModel] = None types: List[Type[ModelConfig]] = [] try: @@ -12,6 +18,8 @@ types.append(openai_config.OpenAIModelConfig) if default_model_name in openai_config.available_models: DefaultModelConfig = openai_config.OpenAIModelConfig + if evaluation_model_name in openai_config.available_models: + evaluation_model = openai_config.available_models[evaluation_model_name] except AttributeError: pass @@ -20,6 +28,8 @@ types.append(replicate_config.ReplicateModelConfig) if default_model_name in replicate_config.available_models: DefaultModelConfig = replicate_config.ReplicateModelConfig + if evaluation_model_name in replicate_config.available_models: + evaluation_model = replicate_config.available_models[evaluation_model_name] except AttributeError: pass diff --git a/module_text_llm/module_text_llm/helpers/utils.py b/module_text_llm/module_text_llm/helpers/utils.py index 2ed05aec5..24cf41024 100644 --- a/module_text_llm/module_text_llm/helpers/utils.py +++ b/module_text_llm/module_text_llm/helpers/utils.py @@ -92,3 +92,28 @@ def get_index_range_from_line_range(line_start: Optional[int], line_end: Optiona line_end_index = min(max(int(line_end), 0), len(sentence_spans) - 1) return sentence_spans[line_start_index][0], sentence_spans[line_end_index][1] + + +def get_line_range_from_index_range(index_start: Optional[int], index_end: Optional[int], content: str) -> Tuple[Optional[int], Optional[int]]: + if index_start is None and index_end is None: + return None, None + + index_start = index_start or index_end or 0 + index_end = index_end or index_start or 0 + + if index_start > index_end: + index_start, index_end = index_end, index_start + + sentence_spans = get_sentence_spans(content) + + line_start = None + line_end = None + + for line_number, (start_index, end_index) in enumerate(sentence_spans, start=1): + if start_index <= index_start < end_index: + line_start = line_number + if start_index <= index_end <= end_index: + line_end = line_number + break + + return line_start, line_end \ No newline at end of file diff --git a/module_text_llm/module_text_llm/prompts/generate_evaluation.py b/module_text_llm/module_text_llm/prompts/generate_evaluation.py new file mode 100644 index 000000000..10daa84a4 --- /dev/null +++ b/module_text_llm/module_text_llm/prompts/generate_evaluation.py @@ -0,0 +1,26 @@ +system_message = """\ +You are now an evaluator for feedback accuracy generated by a machine-learning system. + +# Task +Your task is to estimate if a human tutor would accept or reject the feedback suggestion and how much modification is needed to make the feedback useful. + +# Score Criteria +Accept feedback that is useful to the tutor, meaning that it can be applied to the submission with minor or no modification. \ +Our goal is to reduce the workload of tutors and reduce their cognitive load. \ +Reject feedback that is not useful and would burden the tutor. + +Put the focus on the description of the feedback, the title is optional. \ +The `line_start` and `line_end` should make sense with respect to the submission but do not need to be exact. \ +Credits should make sense with respect to the feedback and the submission but also do not need to be exact. + +# Submission (with sentence numbers : ): +{submission} + +# Example (Human) Feedback: +{true_feedbacks} +""" + +human_message = """\ +### Model Output: +{predicted_feedbacks} +""" \ No newline at end of file diff --git a/module_text_llm/poetry.lock b/module_text_llm/poetry.lock index 8db9814e0..f5206889c 100644 --- a/module_text_llm/poetry.lock +++ b/module_text_llm/poetry.lock @@ -228,102 +228,102 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.3.1" +version = "3.3.2" description = "The Real First Universal Charset Detector. 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"53eeaab1a23bfa1b616447c8734e6fa34ed64242daaea0f94de00414e45460c8" diff --git a/module_text_llm/pyproject.toml b/module_text_llm/pyproject.toml index 6f7685a6f..b5b313aa4 100644 --- a/module_text_llm/pyproject.toml +++ b/module_text_llm/pyproject.toml @@ -15,6 +15,7 @@ nltk = "^3.8.1" gitpython = "^3.1.37" replicate = "^0.11.0" tiktoken = "^0.4.0" +langsmith = "^0.0.63" [tool.poetry.scripts] module = "athena:run_module" diff --git a/playground/src/components/details/exercise_detail/common.tsx b/playground/src/components/details/exercise_detail/common.tsx index 852b19a66..8c583a292 100644 --- a/playground/src/components/details/exercise_detail/common.tsx +++ b/playground/src/components/details/exercise_detail/common.tsx @@ -54,7 +54,7 @@ export default function CommonExerciseDetail({ Missing criterion title )} - Grading Criterion {criterion.id} + Grading Criterion {criterion.id} {criterion.structured_grading_instructions.map( diff --git a/playground/src/components/view_mode/evaluation_mode/conduct_experiment/batch_module_experiment.tsx b/playground/src/components/view_mode/evaluation_mode/conduct_experiment/batch_module_experiment.tsx index d2e9fdb02..560d8b204 100644 --- a/playground/src/components/view_mode/evaluation_mode/conduct_experiment/batch_module_experiment.tsx +++ b/playground/src/components/view_mode/evaluation_mode/conduct_experiment/batch_module_experiment.tsx @@ -2,7 +2,12 @@ import type { Submission } from "@/model/submission"; import type { Experiment } from "../define_experiment"; import type { ExperimentStep } from "@/hooks/batch_module_experiment"; -import React, { useImperativeHandle, useState, ForwardedRef, useEffect } from "react"; +import React, { + useImperativeHandle, + useState, + ForwardedRef, + useEffect, +} from "react"; import Modal from "react-modal"; import { FullScreenHandle } from "react-full-screen"; @@ -14,6 +19,7 @@ import { ModuleConfiguration } from "../configure_modules"; import ModuleExperimentProgress from "./module_experiment_progress"; import SubmissionDetail from "@/components/details/submission_detail"; import ModuleConfigSelect from "@/components/selectors/module_config_select"; +import { twMerge } from "tailwind-merge"; type ConductBatchModuleExperimentProps = { experiment: Experiment; @@ -53,7 +59,10 @@ const ConductBatchModuleExperiment = React.forwardRef< ref: ForwardedRef ) => { const { data: health } = useHealth(); - const moduleExperiment = useBatchModuleExperiment(experiment, moduleConfiguration); + const moduleExperiment = useBatchModuleExperiment( + experiment, + moduleConfiguration + ); const [showProgress, setShowProgress] = useState(true); const [isConfigModalOpen, setConfigModalOpen] = useState(false); @@ -88,14 +97,6 @@ const ConductBatchModuleExperiment = React.forwardRef<

{moduleConfiguration.name}

- {moduleExperiment.continueAfterTraining && ( - - )}
) { return ( - + { if (a.type === "results" && b.type !== "results") { return -1; diff --git a/playground/src/components/view_mode/evaluation_mode/conduct_experiment/module_experiment_progress.tsx b/playground/src/components/view_mode/evaluation_mode/conduct_experiment/module_experiment_progress.tsx index c2f4b9c65..75d0e9964 100644 --- a/playground/src/components/view_mode/evaluation_mode/conduct_experiment/module_experiment_progress.tsx +++ b/playground/src/components/view_mode/evaluation_mode/conduct_experiment/module_experiment_progress.tsx @@ -14,7 +14,7 @@ export default function ModuleExperimentProgress({ experiment, moduleExperiment, }: ModuleExperimentProgressProps) { - const data = moduleExperiment.data; + const { data, submissionsWithAutomaticEvaluation } = moduleExperiment; const moduleRequests = moduleExperiment.moduleRequests; const stepToIndex = (step: ExperimentStep) => { @@ -86,10 +86,11 @@ export default function ModuleExperimentProgress({ className={twMerge( "flex items-center justify-center w-6 h-6 border rounded-full shrink-0", stepToIndex(data.step) >= 2 - ? stepToIndex(data.step) > 2 || moduleExperiment.continueAfterTraining - ? "text-green-500 border-green-500" - : "text-yellow-500 border-yellow-500" - : "text-gray-500 border-gray-500" + ? stepToIndex(data.step) > 2 || + moduleExperiment.continueAfterTraining + ? "text-green-500 border-green-500" + : "text-yellow-500 border-yellow-500" + : "text-gray-500 border-gray-500" )} > 2 @@ -98,10 +99,11 @@ export default function ModuleExperimentProgress({ className={twMerge( "flex flex-col", stepToIndex(data.step) >= 2 - ? stepToIndex(data.step) > 2 || moduleExperiment.continueAfterTraining - ? "text-green-500" - : "text-yellow-500" - : "text-gray-500" + ? stepToIndex(data.step) > 2 || + moduleExperiment.continueAfterTraining + ? "text-green-500" + : "text-yellow-500" + : "text-gray-500" )} > Sending Training Feedback @@ -129,70 +131,121 @@ export default function ModuleExperimentProgress({ )} {/* Generate Feedback Suggestions */} -
  • - 3 - ? "text-green-500 border-green-500" - : stepToIndex(data.step) === 3 - ? "text-yellow-500 border-yellow-500" - : "text-gray-500 border-gray-500" - )} - > - {experiment.trainingSubmissions ? 3 : 2} - -
    3 - ? "text-green-500" - : stepToIndex(data.step) === 3 - ? "text-yellow-500" - : "text-gray-500" - )} - > - Generating Feedback Suggestions - {moduleRequests.requestFeedbackSuggestions.isLoading && ( - - Generating feedback suggestions... ( - {data.submissionsWithFeedbackSuggestions.size + 1}/ - {experiment.evaluationSubmissions.length}) - - )} - {moduleRequests.requestFeedbackSuggestions.isError && ( - - {moduleRequests.requestFeedbackSuggestions.error.message} - - )} - {moduleRequests.requestFeedbackSuggestions.isSuccess && ( - - Generated feedback suggestions ( - {data.submissionsWithFeedbackSuggestions.size}/ - {experiment.evaluationSubmissions.length}) - - )} +
  • +
    + 3 + ? "text-green-500 border-green-500" + : stepToIndex(data.step) === 3 + ? "text-yellow-500 border-yellow-500" + : "text-gray-500 border-gray-500" + )} + > + {experiment.trainingSubmissions ? 3 : 2} + +
    3 + ? "text-green-500" + : stepToIndex(data.step) === 3 + ? "text-yellow-500" + : "text-gray-500" + )} + > + Generating Feedback Suggestions + {moduleRequests.requestFeedbackSuggestions.isLoading && ( + + Generating feedback suggestions... ( + {data.submissionsWithFeedbackSuggestions.size + 1}/ + {experiment.evaluationSubmissions.length}) + + )} + {moduleRequests.requestFeedbackSuggestions.isError && ( + + {moduleRequests.requestFeedbackSuggestions.error.message} + + )} + {moduleRequests.requestFeedbackSuggestions.isSuccess && ( + + Generated feedback suggestions ( + {data.submissionsWithFeedbackSuggestions.size}/ + {experiment.evaluationSubmissions.length}) + + )} +
    + {moduleExperiment.continueAfterTraining && ( + + )}
    • -
  • - - {experiment.trainingSubmissions ? 4 : 3} - -
    - Finished + + {/* Run Automatic Evaluation */} +
  • +
    + + {experiment.trainingSubmissions ? 4 : 3} + +
    + Run Automatic Evaluation + {moduleRequests.requestEvaluation.isLoading && ( + + Evaluating submissions... ( + {(submissionsWithAutomaticEvaluation?.size ?? 0) + 1}/ + {experiment.evaluationSubmissions.length}) + + )} + {moduleRequests.requestEvaluation.isError && ( + + {moduleRequests.requestEvaluation.error.message} + + )} + {moduleRequests.requestEvaluation.isSuccess && ( + + Evaluated submissions ( + {submissionsWithAutomaticEvaluation?.size ?? 0}/ + {experiment.evaluationSubmissions.length}) + + )} +
    + {moduleExperiment.continueWithAutomaticEvaluation && ( + + )}
    • ); diff --git a/playground/src/components/view_mode/module_requests/index.tsx b/playground/src/components/view_mode/module_requests/index.tsx index dca29946d..dcda17bf2 100644 --- a/playground/src/components/view_mode/module_requests/index.tsx +++ b/playground/src/components/view_mode/module_requests/index.tsx @@ -3,12 +3,14 @@ import type { ModuleMeta } from "@/model/health_response"; import { useState } from "react"; import { ModuleProvider } from "@/hooks/module_context"; +import ModuleAndConfigSelect from "@/components/selectors/module_and_config_select"; import GetConfigSchema from "@/components/view_mode/module_requests/get_config_schema"; import SendSubmissions from "@/components/view_mode/module_requests/send_submissions"; +import SelectSubmission from "@/components/view_mode/module_requests/request_submission_selection"; import SendFeedbacks from "@/components/view_mode/module_requests/send_feedbacks"; import RequestFeedbackSuggestions from "@/components/view_mode/module_requests/request_feedback_suggestions"; -import SelectSubmission from "@/components/view_mode/module_requests/request_submission_selection"; -import ModuleAndConfigSelect from "@/components/selectors/module_and_config_select"; +import RequestEvaluation from "@/components/view_mode/module_requests/request_evaluation"; + export default function ModuleRequests() { const [moduleAndConfig, setModuleAndConfig] = useState<{ module: ModuleMeta; moduleConfig: any } | undefined>(undefined); @@ -34,6 +36,7 @@ export default function ModuleRequests() { +     )} diff --git a/playground/src/components/view_mode/module_requests/request_evaluation.tsx b/playground/src/components/view_mode/module_requests/request_evaluation.tsx new file mode 100644 index 000000000..f004708d6 --- /dev/null +++ b/playground/src/components/view_mode/module_requests/request_evaluation.tsx @@ -0,0 +1,172 @@ +import type { Submission } from "@/model/submission"; +import type { Exercise } from "@/model/exercise"; +import type { Feedback } from "@/model/feedback"; +import type ModuleResponse from "@/model/module_response"; + +import { useEffect, useState } from "react"; + +import { useModule } from "@/hooks/module_context"; +import { useBaseInfo } from "@/hooks/base_info_context"; +import useRequestEvaluation from "@/hooks/athena/request_evaluation"; +import useFeedbacks from "@/hooks/playground/feedbacks"; + +import ExerciseSelect from "@/components/selectors/exercise_select"; +import SubmissionSelect from "@/components/selectors/submission_select"; +import ModuleResponseView from "@/components/module_response_view"; +import Disclosure from "@/components/disclosure"; +import ExerciseDetail from "@/components/details/exercise_detail"; +import SubmissionDetail from "@/components/details/submission_detail"; + +export default function RequestEvaluation() { + const { module } = useModule(); + const { dataMode } = useBaseInfo(); + + const [exercise, setExercise] = useState(undefined); + const [submission, setSubmission] = useState( + undefined + ); + + const [predictedFeedbacks, setPredictedFeedbacks] = useState([]); + + const { + data: trueFeedbacks, + isLoading: isLoadingTrueFeedbacks, + error: errorTrueFeedbacks, + } = useFeedbacks(exercise, submission); + + const { + data: response, + isLoading, + error, + mutate, + reset, + } = useRequestEvaluation(undefined, true) // onlyUseContextModule = true for module requests only + + useEffect(() => setExercise(undefined), [module, dataMode]); + + return ( +
    +

    + Request Evaluation from Athena{" "} + (OPTIONAL) +

    +

    + Evaluate a list of feedback suggestions during the research and + development phase. Compare the predicted feedback with the actual + feedback using the function annotated with{" "} + @evaluation_provider. Each module can implement custom + metrics to evaluate the feedback suggestions during evaluation and + respond with arbitrary evaluation results. +

    + { + setExercise(exercise); + reset(); + setSubmission(undefined); + setPredictedFeedbacks([]); + }} + disabled={isLoading} + /> + {exercise && ( + <> + { + setSubmission(submission); + setPredictedFeedbacks([]); + }} + disabled={isLoading} + /> +
    + + {submission && + (trueFeedbacks ? ( + +

    + The following feedbacks given by the tutor in the past. +

    + f.submission_id === submission.id + )} + /> +     + ) : ( +
    + No true feedbacks available +
    + ))} + {submission && ( + +

    + Provide feedback as predicted feedbacks to + test the evaluation. +

    + f.submission_id === submission.id + )} + onFeedbacksChange={setPredictedFeedbacks} + /> +     + )} + {isLoadingTrueFeedbacks && ( +
    Loading feedbacks...
    + )} + {errorTrueFeedbacks && ( +
    + Failed to load feedbacks +
    + )} +
    + + )} + + +
    + ); +} diff --git a/playground/src/hooks/athena/request_evaluation.ts b/playground/src/hooks/athena/request_evaluation.ts new file mode 100644 index 000000000..620fb362b --- /dev/null +++ b/playground/src/hooks/athena/request_evaluation.ts @@ -0,0 +1,79 @@ +import type { Exercise } from "@/model/exercise"; +import type { Submission } from "@/model/submission"; +import type ModuleResponse from "@/model/module_response"; + +import { UseMutationOptions, useMutation } from "react-query"; +import { AthenaError, useAthenaFetcher } from "@/hooks/athena_fetcher"; +import { Feedback } from "@/model/feedback"; +import { useModule } from "@/hooks/module_context"; +import useHealth from "@/hooks/health"; + +/** + * Requests an evaluation for an exercise and a submission given the true and predicted feedbacks from healthy Athena modules. + * + * @param options The react-query options. + * @param onlyUseContextModule - If true, only the context module is used for the evaluation. Otherwise, all healthy modules are used. + * + * @example + * const { data, isLoading, error, mutate } = useRequestEvaluation(); + * mutate({ exercise, submission, trueFeedbacks, predictedFeedbacks }); + */ +export default function useRequestEvaluation( + options: Omit< + UseMutationOptions< + ModuleResponse[] | undefined, + AthenaError, + { + exercise: Exercise; + submission: Submission; + trueFeedbacks: Feedback[]; + predictedFeedbacks: Feedback[]; + } + >, + "mutationFn" + > = {}, + onlyUseContextModule = false +) { + const athenaFetcher = useAthenaFetcher(); + const { module: contextModule } = useModule(); + const { data: health } = useHealth(); + + return useMutation({ + mutationFn: async ({ + exercise, + submission, + trueFeedbacks, + predictedFeedbacks, + }) => { + const modules = onlyUseContextModule + ? [contextModule] + : Object.values(health?.modules ?? {}).filter( + (module) => module.healthy && module.type === contextModule.type && module.supportsEvaluation + ); + + const results = await Promise.allSettled( + modules.map((module) => + athenaFetcher( + "/evaluation", + { + exercise, + submission, + true_feedbacks: trueFeedbacks, + predicted_feedbacks: predictedFeedbacks, + }, + { module: module, moduleConfig: undefined } + ) + ) + ); + + return results.flatMap((result) => { + if (result.status === "fulfilled") { + return [result.value]; + } else { + return []; + } + }); + }, + ...options, + }); +} diff --git a/playground/src/hooks/athena_fetcher.ts b/playground/src/hooks/athena_fetcher.ts index 1f23f20ba..69b9a2278 100644 --- a/playground/src/hooks/athena_fetcher.ts +++ b/playground/src/hooks/athena_fetcher.ts @@ -1,4 +1,5 @@ import type ModuleResponse from "@/model/module_response"; +import type { Module } from "@/hooks/module_context"; import baseUrl from "@/helpers/base_url"; import { useBaseInfo } from "@/hooks/base_info_context"; @@ -36,27 +37,34 @@ export class AthenaError extends Error { * @returns A function that can be used to fetch data from the module or that returns undefined if the module is not set. */ export function useAthenaFetcher() { - const { module, moduleConfig } = useModule(); + const { module: contextModule, moduleConfig: contextModuleConfig } = useModule(); const { athenaUrl, athenaSecret } = useBaseInfo(); const { experimentId, moduleConfigurationId, runId } = useExperimentIdentifiers(); - const headers: { [key: string]: string } = {}; - if (moduleConfig) { - headers["X-Module-Config"] = JSON.stringify(moduleConfig); - } - if (experimentId) { - headers["X-Experiment-ID"] = experimentId; - } - if (moduleConfigurationId) { - headers["X-Module-Configuration-ID"] = moduleConfigurationId; - } - if (runId) { - headers["X-Run-ID"] = runId; - } - return ( - async (moduleRoute: string, body?: any) => { - const url = `${athenaUrl}/modules/${module.type}/${module.name}${moduleRoute}`; + async (moduleRoute: string, body?: any, overrideModule?: Module) => { + let targetModule = contextModule; + let targetModuleConfig = contextModuleConfig; + if (overrideModule) { + targetModule = overrideModule.module; + targetModuleConfig = overrideModule.moduleConfig; + } + + const headers: { [key: string]: string } = {}; + if (targetModuleConfig) { + headers["X-Module-Config"] = JSON.stringify(targetModuleConfig); + } + if (experimentId) { + headers["X-Experiment-ID"] = experimentId; + } + if (moduleConfigurationId) { + headers["X-Module-Configuration-ID"] = moduleConfigurationId; + } + if (runId) { + headers["X-Run-ID"] = runId; + } + + const url = `${athenaUrl}/modules/${targetModule.type}/${targetModule.name}${moduleRoute}`; const response = await fetch( `${baseUrl}/api/athena_request?${new URLSearchParams({ url: url, diff --git a/playground/src/hooks/batch_module_experiment.ts b/playground/src/hooks/batch_module_experiment.ts index ba99982c2..8c256bb5c 100644 --- a/playground/src/hooks/batch_module_experiment.ts +++ b/playground/src/hooks/batch_module_experiment.ts @@ -1,5 +1,6 @@ import type { Feedback } from "@/model/feedback"; import type { ManualRating } from "@/model/manual_rating"; +import type { AutomaticEvaluation } from "@/model/automatic_evaluation"; import type { Experiment } from "@/components/view_mode/evaluation_mode/define_experiment"; import type { ModuleConfiguration } from "@/components/view_mode/evaluation_mode/configure_modules"; @@ -9,6 +10,7 @@ import { useSendFeedbacks } from "./athena/send_feedbacks"; import useRequestSubmissionSelection from "./athena/request_submission_selection"; import useRequestFeedbackSuggestions from "./athena/request_feedback_suggestions"; import useSendSubmissions from "./athena/send_submissions"; +import useRequestEvaluation from "./athena/request_evaluation"; import { useExperimentIdentifiersSetRunId } from "./experiment_identifiers_context"; export type ExperimentStep = @@ -50,6 +52,11 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC Map >(new Map()); + // Stores automatic evaluation of submissions + const [submissionsWithAutomaticEvaluation, setSubmissionsWithAutomaticEvaluation] = useState< + Map | undefined + >(undefined); + const [processingStep, setProcessingStep] = useState< ExperimentStep | undefined >(undefined); @@ -95,6 +102,19 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC }, } : {} ), + ...( + submissionsWithAutomaticEvaluation && submissionsWithAutomaticEvaluation.size > 0 ? { + automaticEvaluation: { + type: "automaticEvaluation", + runId: data.runId, + experimentId: experiment.id, + moduleConfigurationId: moduleConfiguration.id, + submissionsWithAutomaticEvaluation: Object.fromEntries( + submissionsWithAutomaticEvaluation + ), + }, + } : {} + ), }; }; @@ -108,6 +128,7 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC throw new Error("Invalid results data"); } + setProcessingStep(undefined); setData(() => ({ runId: importedData.runId, step: importedData.step, @@ -134,7 +155,22 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC ) )); return; + } else if (importedData.type === "automaticEvaluation") { + // Relies on the fact that the automatic evaluations have to be imported after the results + if (importedData.runId !== data.runId) { + throw new Error("Run ID does not match, have you imported the results first?"); + } + if (importedData.submissionsWithAutomaticEvaluation === undefined) { + throw new Error("Invalid automatic evaluation data"); + } + setSubmissionsWithAutomaticEvaluation(() => new Map( + Object.entries(importedData.submissionsWithAutomaticEvaluation).map( + ([key, value]) => [Number(key), value as any] + ) + )); + return; } + throw new Error("Unknown import data type"); }; @@ -153,11 +189,19 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC })); }) : undefined; + const continueWithAutomaticEvaluation = (data.step === "finished" && submissionsWithAutomaticEvaluation === undefined) ? (() => { + setSubmissionsWithAutomaticEvaluation((prevState) => new Map(prevState)); + stepAutomaticEvaluation(); + }) : undefined; + // Module requests - const sendSubmissions = useSendSubmissions(); - const sendFeedbacks = useSendFeedbacks(); - const requestSubmissionSelection = useRequestSubmissionSelection(); - const requestFeedbackSuggestions = useRequestFeedbackSuggestions(); + // By default useMutation does not retry, but we want to retry a few times to not get stuck + // If we still get stuck we can just `Export` -> `Cancel Experiment` -> `Import` again to continue for now + const sendSubmissions = useSendSubmissions({ retry: 3 }); + const sendFeedbacks = useSendFeedbacks({ retry: 3 }); + const requestSubmissionSelection = useRequestSubmissionSelection({ retry: 3 }); + const requestFeedbackSuggestions = useRequestFeedbackSuggestions({ retry: 3 }); + const requestEvaluation = useRequestEvaluation({ retry: 3 }); // 1. Send submissions to Athena const stepSendSubmissions = () => { @@ -338,10 +382,66 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC setData((prevState) => ({ ...prevState, - step: "finished", + step: "finished", // Automatic evaluation is done separately })); }; + // 4. Automatic evaluation (after results are 'finished') + const stepAutomaticEvaluation = async () => { + setProcessingStep("finished"); + + console.log("Running automatic evaluation..."); + + let remainingSubmissions = experiment.evaluationSubmissions.filter( + (submission) => !submissionsWithAutomaticEvaluation?.has(submission.id) + ); + + let num = 0; + for (const submission of remainingSubmissions) { + num += 1; + console.log( + `Evaluating... (${num}/${ + remainingSubmissions.length + })` + ); + + const predictedFeedbacks = data.submissionsWithFeedbackSuggestions.get( + submission.id + )?.suggestions ?? []; + + try { + const responses = await requestEvaluation.mutateAsync({ + exercise: experiment.exercise, + submission, + trueFeedbacks: experiment.tutorFeedbacks.filter( + (feedback) => feedback.submission_id === submission.id + ), + predictedFeedbacks: predictedFeedbacks, + }); + if (!isMounted.current) { + return; + } + + const data = Object.fromEntries( + responses.map((response) => [response.module_name, response.data]) + ); + + console.log(`Received evaluation for submission ${submission.id}:`, data); + + setSubmissionsWithAutomaticEvaluation((prevState) => { + const newMap = new Map(prevState); + newMap.set(submission.id, data); + return newMap; + }); + } catch (error) { + console.error( + `Error while evaluating submission ${submission.id}:`, + error + ); + } + } + }; + useEffect(() => { isMounted.current = true; return () => { @@ -375,18 +475,18 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC processingStep !== "generatingFeedbackSuggestions" ) { stepGenerateFeedbackSuggestions(); - } - // TODO: Add automatic evaluation step here - // Note: Evaluate tutor feedback more globally to not do it multiple times - // Note 2: Actually, I probably want to have it in parallel with the feedback suggestions for the interactive mode! + } + // Automatic evaluation is triggered manually }, [data.step]); return { data, submissionsWithManualRatings, + submissionsWithAutomaticEvaluation, getManualRatingsSetter, startExperiment, continueAfterTraining, + continueWithAutomaticEvaluation, exportData, importData, moduleRequests: { @@ -394,6 +494,7 @@ export default function useBatchModuleExperiment(experiment: Experiment, moduleC sendFeedbacks, requestSubmissionSelection, requestFeedbackSuggestions, + requestEvaluation, }, }; } diff --git a/playground/src/hooks/module_context.tsx b/playground/src/hooks/module_context.tsx index c2a96232e..f7aab7666 100644 --- a/playground/src/hooks/module_context.tsx +++ b/playground/src/hooks/module_context.tsx @@ -2,7 +2,7 @@ import type { ModuleMeta } from '@/model/health_response'; import { ReactNode, createContext, useContext, useReducer } from 'react'; -type Module = { +export type Module = { module: ModuleMeta; moduleConfig: any; }; diff --git a/playground/src/model/automatic_evaluation.ts b/playground/src/model/automatic_evaluation.ts new file mode 100644 index 000000000..fb55b9c86 --- /dev/null +++ b/playground/src/model/automatic_evaluation.ts @@ -0,0 +1,3 @@ +export type AutomaticEvaluation = { + [module: string]: any; +}; diff --git a/playground/src/model/health_response.ts b/playground/src/model/health_response.ts index 7502b21d9..4ffe27de9 100644 --- a/playground/src/model/health_response.ts +++ b/playground/src/model/health_response.ts @@ -2,6 +2,7 @@ export type ModuleMeta = { name: string; type: string; healthy: boolean; + supportsEvaluation: boolean; }; export type HealthResponse = { diff --git a/playground/src/pages/api/athena_request.ts b/playground/src/pages/api/athena_request.ts index de94908fc..b3971db81 100644 --- a/playground/src/pages/api/athena_request.ts +++ b/playground/src/pages/api/athena_request.ts @@ -17,7 +17,20 @@ export default async function handler( const url = req.query.url; let response; const secret = req.headers["authorization"] as string; - const moduleConfig = req.headers["x-module-config"] as string | undefined; + const forwardHeaders = [ + "X-Module-Config", + "X-Experiment-ID", + "X-Module-Configuration-ID", + "X-Run-ID", + ] + + const headers = Object.fromEntries( + forwardHeaders.flatMap((header) => { + const value = req.headers[header.toLowerCase()] as string | undefined; + return value ? [[header, value]] : []; + }) + ) + if (!secret) { console.warn("No secret provided"); } @@ -27,7 +40,7 @@ export default async function handler( "Content-Type": "application/json", Accept: "application/json", "Authorization": secret, - ...(moduleConfig && { "X-Module-Config": moduleConfig }), + ...headers, }, method: req.method, ...(req.method === "POST" ? { body: JSON.stringify(req.body) } : {}),