From acf5d269e652f15b061e8a68d3f918c2d5345728 Mon Sep 17 00:00:00 2001 From: Leon Laurin Wehrhahn <58460654+LeonWehrhahn@users.noreply.github.com> Date: Fri, 13 Sep 2024 01:34:27 +0700 Subject: [PATCH] Preliminary AI Feedback for all Apollon UML Diagrams (#335) Co-authored-by: Dmytro Polityka <33299157+undernagruzez@users.noreply.github.com> --- .vscode/launch.json | 4 +- assessment_module_manager/modules.docker.ini | 2 +- assessment_module_manager/modules.ini | 2 +- .../modeling/module_modeling_llm/.env.example | 10 +- .../module_modeling_llm/__main__.py | 2 +- .../module_modeling_llm/config.py | 19 +- .../generate_suggestions.py | 123 +- .../module_modeling_llm/helpers/llm_utils.py | 145 +- .../helpers/models/__init__.py | 6 +- .../helpers/models/openai.py | 287 +--- .../helpers/serializers/bpmn_serializer.py | 858 ------------ .../serializers/diagram_model_serializer.py | 87 +- .../helpers/serializers/parser/element.py | 51 + .../helpers/serializers/parser/relation.py | 105 ++ .../helpers/serializers/parser/uml_parser.py | 91 ++ .../prompts/apollon_format.py | 97 ++ .../prompts/generate_suggestions.py | 116 +- .../prompts/submission_format/__init__.py | 0 .../prompts/submission_format/bpmn.py | 2 - .../prompts/submission_format/general.py | 1 - .../submission_format_remarks.py | 33 - .../modeling/module_modeling_llm/poetry.lock | 1225 ++++++++++------- .../module_modeling_llm/pyproject.toml | 22 +- 23 files changed, 1315 insertions(+), 1973 deletions(-) delete mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/bpmn_serializer.py create mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/element.py create mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/relation.py create mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/uml_parser.py create mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/prompts/apollon_format.py delete mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/__init__.py delete mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/bpmn.py delete mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/general.py delete mode 100644 modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/submission_format_remarks.py diff --git a/.vscode/launch.json b/.vscode/launch.json index f4ea6ff21..7d6705683 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -26,7 +26,7 @@ "request": "launch", "cwd": "${workspaceFolder}/modules/programming/module_programming_llm", "module": "module_programming_llm", - "justMyCode": true + "justMyCode": false }, { "name": "Module Programming ThemisML", @@ -62,7 +62,7 @@ "type": "python", "request": "launch", "cwd": "${workspaceFolder}/modules/modeling/module_modeling_llm", - "module": "module_text_cofee" + "module": "module_modeling_llm" } ] } diff --git a/assessment_module_manager/modules.docker.ini b/assessment_module_manager/modules.docker.ini index 618f7d6bc..53a2b83f8 100644 --- a/assessment_module_manager/modules.docker.ini +++ b/assessment_module_manager/modules.docker.ini @@ -44,6 +44,6 @@ supports_graded_feedback_requests = false url = http://module-modeling-llm:5008 type = modeling supports_evaluation = false -supports_non_graded_feedback_requests = false +supports_non_graded_feedback_requests = true supports_graded_feedback_requests = true diff --git a/assessment_module_manager/modules.ini b/assessment_module_manager/modules.ini index e601a5e08..cbe4a406c 100644 --- a/assessment_module_manager/modules.ini +++ b/assessment_module_manager/modules.ini @@ -44,6 +44,6 @@ supports_graded_feedback_requests = false url = http://localhost:5008 type = modeling supports_evaluation = false -supports_non_graded_feedback_requests = false +supports_non_graded_feedback_requests = true supports_graded_feedback_requests = true diff --git a/modules/modeling/module_modeling_llm/.env.example b/modules/modeling/module_modeling_llm/.env.example index aedc5bdba..68b0577a1 100644 --- a/modules/modeling/module_modeling_llm/.env.example +++ b/modules/modeling/module_modeling_llm/.env.example @@ -12,7 +12,7 @@ DATABASE_URL=sqlite:///../data/data.sqlite # Default model to use # See below for options, available models are also logged on startup -LLM_DEFAULT_MODEL="azure_openai_gpt-35" +LLM_DEFAULT_MODEL="azure_openai_gpt-4o" # Enable LLM-as-a-judge approach 0 = disabled, 1 = enabled LLM_ENABLE_LLM_AS_A_JUDGE=1 @@ -23,13 +23,13 @@ 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) -LLM_OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" +OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Azure OpenAI [leave blank if not used] # Model names prefixed with `azure_openai_` followed by the deployment id, e.g. `azure_openai_gpt-35` -LLM_AZURE_OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" -LLM_AZURE_OPENAI_API_BASE="https://ase-eu01.openai.azure.com/" # change base if needed -LLM_AZURE_OPENAI_API_VERSION="2023-07-01-preview" # change base if needed +AZURE_OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" +AZURE_OPENAI_ENDPOINT="https://ase-eu01.openai.azure.com/" # change base if needed +OPENAI_API_VERSION="2023-07-01-preview" # change base if needed # Replicate [leave blank if not used] # See https://replicate.com and adjust model config options in `module_text_llm/helpers/models/replicate.py` diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/__main__.py b/modules/modeling/module_modeling_llm/module_modeling_llm/__main__.py index 7e33cbf41..d2594df5a 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/__main__.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/__main__.py @@ -31,7 +31,7 @@ def process_incoming_feedback(exercise: Exercise, submission: Submission, feedba async def suggest_feedback(exercise: Exercise, submission: Submission, is_graded: bool, module_config: Configuration) -> List[Feedback]: logger.info("suggest_feedback: Suggestions for submission %d of exercise %d were requested", submission.id, exercise.id) - return await generate_suggestions(exercise, submission, module_config.approach, module_config.debug) + return await generate_suggestions(exercise, submission, is_graded, module_config.approach, module_config.debug) if __name__ == "__main__": diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/config.py b/modules/modeling/module_modeling_llm/module_modeling_llm/config.py index 738c72bab..3972801e7 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/config.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/config.py @@ -3,8 +3,10 @@ from athena import config_schema_provider from module_modeling_llm.helpers.models import ModelConfigType, DefaultModelConfig from module_modeling_llm.prompts.generate_suggestions import ( - system_message as generate_suggestions_system_message, - human_message as generate_suggestions_human_message + graded_feedback_system_message as default_graded_feedback_system_message, + graded_feedback_human_message as default_graded_feedback_human_message, + filter_feedback_system_message as default_filter_feedback_system_message, + filter_feedback_human_message as default_filter_feedback_human_message ) @@ -16,10 +18,15 @@ class GenerateSuggestionsPrompt(BaseModel): _Note: **{problem_statement}**, **{example_solution}**, or **{grading_instructions}** might be omitted if the input is too long._ """ - system_message: str = Field(default=generate_suggestions_system_message, - description="Message for priming AI behavior and instructing it what to do.") - human_message: str = Field(default=generate_suggestions_human_message, - description="Message from a human. The input on which the AI is supposed to act.") + graded_feedback_system_message: str = Field(default=default_graded_feedback_system_message, + description="Message for priming AI behavior and instructing it what to do.") + graded_feedback_human_message: str = Field(default=default_graded_feedback_human_message, + description="Message from a human. The input on which the AI is supposed to act.") + filter_feedback_system_message: str = Field(default=default_filter_feedback_system_message, + description="Message for priming AI behavior for filtering ungraded feedback.") + filter_feedback_human_message: str = Field(default=default_filter_feedback_human_message, + description="Message for instructing AI to filter ungraded feedback.") + class BasicApproachConfig(BaseModel): diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/generate_suggestions.py b/modules/modeling/module_modeling_llm/module_modeling_llm/generate_suggestions.py index 21c9eaf7c..287bdd536 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/generate_suggestions.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/generate_suggestions.py @@ -1,28 +1,23 @@ import json from typing import List, Optional, Sequence +from module_modeling_llm.prompts.apollon_format import apollon_format_description from pydantic import BaseModel, Field from athena import emit_meta -from athena.logger import logger from athena.modeling import Exercise, Submission, Feedback from module_modeling_llm.config import BasicApproachConfig -from module_modeling_llm.helpers.llm_utils import ( - get_chat_prompt_with_formatting_instructions, - check_prompt_length_and_omit_features_if_necessary, - num_tokens_from_prompt, - predict_and_parse -) -from module_modeling_llm.helpers.models.diagram_types import DiagramType +from langchain_core.output_parsers import PydanticOutputParser +from langchain_core.prompts import ChatPromptTemplate +from module_modeling_llm.helpers.llm_utils import predict_and_parse from module_modeling_llm.helpers.serializers.diagram_model_serializer import DiagramModelSerializer -from module_modeling_llm.helpers.utils import format_grading_instructions, get_elements -from module_modeling_llm.prompts.submission_format.submission_format_remarks import get_submission_format_remarks +from module_modeling_llm.helpers.utils import format_grading_instructions class FeedbackModel(BaseModel): title: str = Field(description="Very short title, i.e. feedback category or similar", example="Logic Error") description: str = Field(description="Feedback description") - element_ids: Optional[str] = Field(description="Referenced diagram element IDs, or empty if unreferenced") + element_names: Optional[List[str]] = Field(description="Referenced diagram element names, and relations (R) or empty if unreferenced") credits: float = Field(0.0, description="Number of points received/deducted") grading_instruction_id: Optional[int] = Field( description="ID of the grading instruction that was used to generate this feedback, or empty if no grading instruction was used" @@ -35,25 +30,13 @@ class Config: class AssessmentModel(BaseModel): """Collection of feedbacks making up an assessment""" - feedbacks: Sequence[FeedbackModel] = Field(description="Assessment feedbacks") + feedbacks: Sequence[FeedbackModel] = Field(description="Assessment feedbacks, make sure to include all grading instructions") class Config: title = "Assessment" -def filter_ids_for_model(ids: List[str], model: dict) -> List[str]: - """ - Filter a list of element ids based on whether a corresponding element is present in a given diagram model. - :param ids: List of ids that should be filtered - :param model: Diagram model in which elements with the given ids should be contained - :return The filtered list of IDs - """ - elements: list[dict] = get_elements(model) - model_ids: set[str] = {str(element.get("id")) for element in elements} - return list(filter(lambda id: id in model_ids, ids)) - - -async def generate_suggestions(exercise: Exercise, submission: Submission, config: BasicApproachConfig, debug: bool) -> \ +async def generate_suggestions(exercise: Exercise, submission: Submission, is_graded: bool, config: BasicApproachConfig, debug: bool) -> \ List[Feedback]: """ Generate feedback suggestions for modeling exercise submissions @@ -66,55 +49,28 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi model = config.model.get_model() # type: ignore[attr-defined] serialized_example_solution = None - if exercise.example_solution: example_solution_diagram = json.loads(exercise.example_solution) serialized_example_solution, _ = DiagramModelSerializer.serialize_model(example_solution_diagram) submission_diagram = json.loads(submission.model) - submission_format_remarks = get_submission_format_remarks(submission_diagram.get("type")) - - # Having the LLM reference IDs that a specific feedback item applies to seems to work a lot more reliable with - # shorter IDs, especially if they are prefixed with "id_". We therefore map the UUIDs used in Apollon diagrams to - # shortened IDs and have the diagram model serializer return a reverse mapping dictionary which allows us to map - # the shortened IDs back to the original ones. - serialized_submission, reverse_id_map = DiagramModelSerializer.serialize_model(submission_diagram) + serialized_submission, element_id_mapping = DiagramModelSerializer.serialize_model(submission_diagram) prompt_input = { "max_points": exercise.max_points, "bonus_points": exercise.bonus_points, "grading_instructions": format_grading_instructions(exercise.grading_instructions, exercise.grading_criteria), - "submission_format_remarks": submission_format_remarks, + "submission_format": submission_diagram.get("type"), "problem_statement": exercise.problem_statement or "No problem statement.", "example_solution": serialized_example_solution or "No example solution.", - "submission": serialized_submission + "submission": serialized_submission, + "uml_diagram_format": apollon_format_description, + "format_instructions": PydanticOutputParser(pydantic_object=AssessmentModel).get_format_instructions() } - chat_prompt = get_chat_prompt_with_formatting_instructions( - model=model, - system_message=config.generate_suggestions_prompt.system_message, - human_message=config.generate_suggestions_prompt.human_message, - pydantic_object=AssessmentModel - ) - - # Check if the prompt is too long and omit features if necessary (in order of importance) - omittable_features = ["example_solution", "problem_statement", "grading_instructions"] - prompt_input, should_run = check_prompt_length_and_omit_features_if_necessary( - prompt=chat_prompt, - prompt_input=prompt_input, - max_input_tokens=10000, # config.max_input_tokens, - omittable_features=omittable_features, - debug=debug - ) - - # Skip if the prompt is too long - if not should_run: - logger.warning("Input too long. Skipping.") - if debug: - emit_meta("prompt", chat_prompt.format(**prompt_input)) - emit_meta("error", - f"Input too long {num_tokens_from_prompt(chat_prompt, prompt_input)} > {config.max_input_tokens}") - return [] + chat_prompt = ChatPromptTemplate.from_messages([ + ("system", config.generate_suggestions_prompt.graded_feedback_system_message), + ("human", config.generate_suggestions_prompt.graded_feedback_human_message)]) result = await predict_and_parse( model=model, @@ -136,6 +92,38 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi if result is None: return [] + # Check if is graded + if is_graded is False: + filter_chat_prompt = ChatPromptTemplate.from_messages([ + ("system", config.generate_suggestions_prompt.filter_feedback_system_message), + ("human", config.generate_suggestions_prompt.filter_feedback_human_message) + ]) + + filter_prompt_input = { + "original_feedback": result.dict(), + "format_instructions": PydanticOutputParser(pydantic_object=AssessmentModel).get_format_instructions() + } + + result = await predict_and_parse( + model=model, + chat_prompt=filter_chat_prompt, + prompt_input=filter_prompt_input, + pydantic_object=AssessmentModel, + tags=[ + f"exercise-{exercise.id}-filter", + f"submission-{submission.id}-filter", + ] + ) + + if debug: + emit_meta("filter_feedback", { + "prompt": filter_chat_prompt.format(**filter_prompt_input), + "result": result.dict() if result is not None else None + }) + + if result is None: + return [] + grading_instruction_ids = set( grading_instruction.id for criterion in exercise.grading_criteria or [] @@ -145,21 +133,20 @@ async def generate_suggestions(exercise: Exercise, submission: Submission, confi feedbacks = [] for feedback in result.feedbacks: grading_instruction_id = feedback.grading_instruction_id if feedback.grading_instruction_id in grading_instruction_ids else None - element_ids = list( - map(lambda element_id: reverse_id_map[ - element_id.strip() - ] if reverse_id_map else element_id.strip(), feedback.element_ids.split(",")) - ) if feedback.element_ids else [] + element_ids = [element_id_mapping[element] for element in (feedback.element_names or [])] + feedbacks.append(Feedback( exercise_id=exercise.id, submission_id=submission.id, title=feedback.title, description=feedback.description, - element_ids=filter_ids_for_model(element_ids, submission_diagram), + element_ids=element_ids, credits=feedback.credits, structured_grading_instruction_id=grading_instruction_id, - meta={} + meta={}, + id=None, + is_graded=is_graded )) - return feedbacks + return feedbacks \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/llm_utils.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/llm_utils.py index 7cbf026f6..9c136027d 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/llm_utils.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/llm_utils.py @@ -1,121 +1,13 @@ from typing import Optional, Type, TypeVar, List - -import tiktoken -from langchain.base_language import BaseLanguageModel -from langchain.chains import LLMChain -from langchain.chains.openai_functions import create_structured_output_chain -from langchain.chat_models import ChatOpenAI -from langchain.output_parsers import PydanticOutputParser -from langchain.prompts import ( - ChatPromptTemplate, - SystemMessagePromptTemplate, - HumanMessagePromptTemplate, -) -from langchain.schema import OutputParserException -from pydantic import BaseModel, ValidationError - -from athena import emit_meta, get_experiment_environment +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel, ValidationError +from langchain_core.runnables import RunnableSequence +from athena import get_experiment_environment +from athena.logger import logger T = TypeVar("T", bound=BaseModel) - -def num_tokens_from_string(string: str) -> int: - """Returns the number of tokens in a text string.""" - encoding = tiktoken.get_encoding("cl100k_base") - num_tokens = len(encoding.encode(string)) - return num_tokens - - -def num_tokens_from_prompt(chat_prompt: ChatPromptTemplate, prompt_input: dict) -> int: - """Returns the number of tokens in a chat prompt.""" - return num_tokens_from_string(chat_prompt.format(**prompt_input)) - - -def check_prompt_length_and_omit_features_if_necessary(prompt: ChatPromptTemplate, - prompt_input: dict, - max_input_tokens: int, - omittable_features: List[str], - debug: bool): - """Check if the input is too long and omit features if necessary. - - Note: Omitted features will be replaced with "omitted" in the prompt - - Args: - prompt (ChatPromptTemplate): Prompt template - prompt_input (dict): Prompt input - max_input_tokens (int): Maximum number of tokens allowed - omittable_features (List[str]): List of features that can be omitted, ordered by priority (least important first) - debug (bool): Debug flag - - Returns: - (dict, bool): Tuple of (prompt_input, should_run) where prompt_input is the input with omitted features and - should_run is True if the model should run, False otherwise - """ - if num_tokens_from_prompt(prompt, prompt_input) <= max_input_tokens: - # Full prompt fits into LLM context => should run with full prompt - return prompt_input, True - - omitted_features = [] - - # Omit features until the input is short enough - for feature in omittable_features: - if feature in prompt_input: - omitted_features.append(feature) - prompt_input[feature] = "omitted" - if num_tokens_from_prompt(prompt, prompt_input) <= max_input_tokens: - if debug: - emit_meta("omitted_features", omitted_features) - return prompt_input, True - - # If we get here, we couldn't omit enough features - return prompt_input, False - - -def supports_function_calling(model: BaseLanguageModel): - """Returns True if the model supports function calling, False otherwise - - Args: - model (BaseLanguageModel): The model to check - - Returns: - boolean: True if the model supports function calling, False otherwise - """ - return isinstance(model, ChatOpenAI) - - -def get_chat_prompt_with_formatting_instructions( - model: BaseLanguageModel, - system_message: str, - human_message: str, - pydantic_object: Type[T] -) -> ChatPromptTemplate: - """Returns a ChatPromptTemplate with formatting instructions (if necessary) - - Note: Does nothing if the model supports function calling - - Args: - model (BaseLanguageModel): The model to check if it supports function calling - system_message (str): System message - human_message (str): Human message - pydantic_object (Type[T]): Model to parse the output - - Returns: - ChatPromptTemplate: ChatPromptTemplate with formatting instructions (if necessary) - """ - if supports_function_calling(model): - system_message_prompt = SystemMessagePromptTemplate.from_template(system_message) - human_message_prompt = HumanMessagePromptTemplate.from_template(human_message) - return ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) - - output_parser = PydanticOutputParser(pydantic_object=pydantic_object) - system_message_prompt = SystemMessagePromptTemplate.from_template(system_message + "\n{format_instructions}") - system_message_prompt.prompt.partial_variables = {"format_instructions": output_parser.get_format_instructions()} - system_message_prompt.prompt.input_variables.remove("format_instructions") - human_message_prompt = HumanMessagePromptTemplate.from_template( - human_message + "\n\nJSON response following the provided schema:") - return ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) - - async def predict_and_parse( model: BaseLanguageModel, chat_prompt: ChatPromptTemplate, @@ -145,19 +37,16 @@ async def predict_and_parse( if experiment.run_id is not None: tags.append(f"run-{experiment.run_id}") - if supports_function_calling(model): - chain = create_structured_output_chain(pydantic_object, llm=model, prompt=chat_prompt, tags=tags) - - try: - return await chain.arun(**prompt_input) - except (OutputParserException, ValidationError): - # In the future, we should probably have some recovery mechanism here (i.e. fix the output with another prompt) - return None + structured_output_llm = model.with_structured_output(pydantic_object, method="json_mode") + chain = RunnableSequence( + chat_prompt, + structured_output_llm + ) - output_parser = PydanticOutputParser(pydantic_object=pydantic_object) - chain = LLMChain(llm=model, prompt=chat_prompt, output_parser=output_parser, tags=tags) try: - return await chain.arun(**prompt_input) - except (OutputParserException, ValidationError): - # In the future, we should probably have some recovery mechanism here (i.e. fix the output with another prompt) - return None + return await chain.ainvoke(prompt_input, config={"tags": tags}) + except (ValidationError) as e: + + logger.error("Exception type: %s, Message: %s", type(e).__name__, e) + + return None \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/__init__.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/__init__.py index ce987b33b..9233a644b 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/__init__.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/__init__.py @@ -1,10 +1,10 @@ import os from typing import Type, Union, List, Optional - from langchain.base_language import BaseLanguageModel from module_modeling_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") @@ -15,7 +15,6 @@ types: List[Type[ModelConfig]] = [] try: import module_modeling_llm.helpers.models.openai as openai_config - types.append(openai_config.OpenAIModelConfig) if default_model_name in openai_config.available_models: DefaultModelConfig = openai_config.OpenAIModelConfig @@ -24,7 +23,6 @@ except AttributeError: pass - if not types: raise EnvironmentError( "No model configurations available, please set up at least one provider in the environment variables.") @@ -37,4 +35,4 @@ ModelConfigType = type0 else: type1 = types[1] - ModelConfigType = Union[type0, type1] # type: ignore + ModelConfigType = Union[type0, type1] # type: ignore diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/openai.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/openai.py index fec3d2fa2..bb08e409d 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/openai.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/models/openai.py @@ -1,266 +1,68 @@ import os -from contextlib import contextmanager -from enum import Enum -from typing import Any, Callable, Dict, List - import openai -from langchain.base_language import BaseLanguageModel -from langchain.chat_models import AzureChatOpenAI, ChatOpenAI -from langchain.llms import AzureOpenAI, OpenAI -from langchain.llms.openai import BaseOpenAI +import requests + +from typing import Dict, List +from enum import Enum from pydantic import Field, validator, PositiveInt +from langchain.base_language import BaseLanguageModel +from langchain_openai import AzureChatOpenAI, ChatOpenAI from athena.logger import logger from .model_config import ModelConfig OPENAI_PREFIX = "openai_" AZURE_OPENAI_PREFIX = "azure_openai_" - -######################################################################### -# Monkey patching openai/langchain api # -# ===================================================================== # -# This allows us to have multiple api keys i.e. mixing # -# openai and azure openai api keys so we can use not only deployed # -# models but also models from the non-azure openai api. # -# This is mostly for testing purposes, in production we can just deploy # -# the models to azure that we want to use. # -######################################################################### - -# Prevent LangChain error, we will set the key later -os.environ["OPENAI_API_KEY"] = "" - - -def _wrap(old: Any, new: Any) -> Callable: - def repl(*args: Any, **kwargs: Any) -> Any: - new(args[0]) # args[0] is self - return old(*args, **kwargs) - - return repl - - -def _async_wrap(old: Any, new: Any): - async def repl(*args, **kwargs): - new(args[0]) # args[0] is self - return await old(*args, **kwargs) - - return repl - - -def _set_credentials(self): - openai.api_key = self.openai_api_key - - api_type = "open_ai" - api_base = "https://api.openai.com/v1" - api_version = None - if hasattr(self, "openai_api_type"): - api_type = self.openai_api_type - - if api_type == "azure": - if hasattr(self, "openai_api_base"): - api_base = self.openai_api_base - if hasattr(self, "openai_api_version"): - api_version = self.openai_api_version - - openai.api_type = api_type - openai.api_base = api_base - openai.api_version = api_version - - -# Monkey patching langchain -# pylint: disable=protected-access -ChatOpenAI._generate = _wrap(ChatOpenAI._generate, _set_credentials) # type: ignore -ChatOpenAI._agenerate = _async_wrap(ChatOpenAI._agenerate, _set_credentials) # type: ignore -BaseOpenAI._generate = _wrap(BaseOpenAI._generate, _set_credentials) # type: ignore -BaseOpenAI._agenerate = _async_wrap(BaseOpenAI._agenerate, _set_credentials) # type: ignore - - -# pylint: enable=protected-access - -######################################################################### -# Monkey patching end # -######################################################################### - - -def _use_azure_credentials(): - openai.api_type = "azure" - openai.api_key = os.environ.get("LLM_AZURE_OPENAI_API_KEY") - openai.api_base = os.environ.get("LLM_AZURE_OPENAI_API_BASE") - # os.environ.get("LLM_AZURE_OPENAI_API_VERSION") - openai.api_version = "2023-03-15-preview" - - -def _use_openai_credentials(): - openai.api_type = "open_ai" - openai.api_key = os.environ.get("LLM_OPENAI_API_KEY") - openai.api_base = "https://api.openai.com/v1" - openai.api_version = None - - -openai_available = bool(os.environ.get("LLM_OPENAI_API_KEY")) -azure_openai_available = bool(os.environ.get("LLM_AZURE_OPENAI_API_KEY")) - -print("azure_openai_available", azure_openai_available) - - -# This is a hack to make sure that the openai api is set correctly -# Right now it is overkill, but it will be useful when the api gets fixed and we no longer -# hardcode the model names (i.e. OpenAI fixes their api) -@contextmanager -def _openai_client(use_azure_api: bool, is_preference: bool): - """Set the openai client to use the correct api type, if available - - Args: - use_azure_api (bool): If true, use the azure api, else use the openai api - is_preference (bool): If true, it can fall back to the other api if the preferred one is not available - """ - if use_azure_api: - if azure_openai_available: - _use_azure_credentials() - elif is_preference and openai_available: - _use_openai_credentials() - elif is_preference: - raise EnvironmentError( - "No OpenAI api available, please set LLM_AZURE_OPENAI_API_KEY, LLM_AZURE_OPENAI_API_BASE and " - "LLM_AZURE_OPENAI_API_VERSION environment variables or LLM_OPENAI_API_KEY environment variable" - ) - else: - raise EnvironmentError( - "Azure OpenAI api not available, please set LLM_AZURE_OPENAI_API_KEY, LLM_AZURE_OPENAI_API_BASE and " - "LLM_AZURE_OPENAI_API_VERSION environment variables" - ) - else: - if openai_available: - _use_openai_credentials() - elif is_preference and azure_openai_available: - _use_azure_credentials() - elif is_preference: - raise EnvironmentError( - "No OpenAI api available, please set LLM_OPENAI_API_KEY environment variable or LLM_AZURE_OPENAI_API_KEY, " - "LLM_AZURE_OPENAI_API_BASE and LLM_AZURE_OPENAI_API_VERSION environment variables" - ) - else: - raise EnvironmentError( - "OpenAI api not available, please set LLM_OPENAI_API_KEY environment variable" - ) - - # API client is setup correctly - yield - - -def _get_available_deployments(openai_models: Dict[str, List[str]], model_aliases: Dict[str, str]): - available_deployments: Dict[str, Dict[str, Any]] = { - "chat_completion": {}, - "completion": {}, - "fine_tuneing": {}, - } - - if azure_openai_available: - with _openai_client(use_azure_api=True, is_preference=False): - deployments = openai.Deployment.list().get("data") or [] # type: ignore - for deployment in deployments: - model_name = deployment.model - if model_name in model_aliases: - model_name = model_aliases[model_name] - if model_name in openai_models["chat_completion"]: - available_deployments["chat_completion"][deployment.id] = deployment - elif model_name in openai_models["completion"]: - available_deployments["completion"][deployment.id] = deployment - elif model_name in openai_models["fine_tuneing"]: - available_deployments["fine_tuneing"][deployment.id] = deployment - - return available_deployments - - -def _get_available_models(openai_models: Dict[str, List[str]], - available_deployments: Dict[str, Dict[str, Any]]): - available_models: Dict[str, BaseLanguageModel] = {} - - if openai_available: - openai_api_key = os.environ["LLM_OPENAI_API_KEY"] - for model_name in openai_models["chat_completion"]: - available_models[OPENAI_PREFIX + model_name] = ChatOpenAI( - model=model_name, - openai_api_key=openai_api_key, - client="", - temperature=0 - ) - for model_name in openai_models["completion"]: - available_models[OPENAI_PREFIX + model_name] = OpenAI( - model=model_name, - openai_api_key=openai_api_key, - client="", - temperature=0 - ) - - if azure_openai_available: - azure_openai_api_key = os.environ["LLM_AZURE_OPENAI_API_KEY"] - azure_openai_api_base = os.environ["LLM_AZURE_OPENAI_API_BASE"] - azure_openai_api_version = os.environ["LLM_AZURE_OPENAI_API_VERSION"] - - for model_type, Model in [("chat_completion", AzureChatOpenAI), ("completion", AzureOpenAI)]: - for deployment_name, deployment in available_deployments[model_type].items(): - available_models[AZURE_OPENAI_PREFIX + deployment_name] = Model( - model=deployment.model, - deployment_name=deployment_name, - openai_api_base=azure_openai_api_base, - openai_api_version=azure_openai_api_version, - openai_api_key=azure_openai_api_key, - client="", - temperature=0 - ) - - return available_models - - -_model_aliases = { - "gpt-35-turbo": "gpt-3.5-turbo", -} - -# Hardcoded because openai can't provide a trustworthly api to get the list of models and capabilities... -openai_models = { - "chat_completion": [ - "gpt-4", - # "gpt-4-32k", # Not publicly available - "gpt-3.5-turbo", - "gpt-3.5-turbo-16k" - ], - "completion": [ - "text-davinci-003", - "text-curie-001", - "text-babbage-001", - "text-ada-001", - ], - "fine_tuneing": [ - "davinci", - "curie", - "babbage", - "ada", - ] -} -available_deployments = _get_available_deployments(openai_models, _model_aliases) -available_models = _get_available_models(openai_models, available_deployments) - -print("available_models", available_models) +openai_available = bool(os.environ.get("OPENAI_API_KEY")) +azure_openai_available = bool(os.environ.get("AZURE_OPENAI_API_KEY")) + +available_models: Dict[str, BaseLanguageModel] = {} + +# Load Non-Azure OpenAI models +if openai_available: + openai.api_type = "openai" + for model in openai.models.list(): + if "gpt" in model.id: + available_models[OPENAI_PREFIX + model.id] = ChatOpenAI(model=model.id) + +# Load Azure OpenAI models +if azure_openai_available: + def _get_azure_openai_deployments() -> List[str]: + # If this breaks in the future we have to use azure-mgmt-cognitiveservices which needs 6 additional environment variables + base_url = f"{os.environ.get('AZURE_OPENAI_ENDPOINT')}/openai" + headers = { + "api-key": os.environ["AZURE_OPENAI_API_KEY"] + } + + models_response = requests.get(f"{base_url}/models?api-version=2023-03-15-preview", headers=headers, timeout=60) + models_data = models_response.json()["data"] + deployments_response = requests.get(f"{base_url}/deployments?api-version=2023-03-15-preview", headers=headers, timeout=60) + deployments_data = deployments_response.json()["data"] + + # Check if deployment["model"] is a substring of model["id"], i.e. "gpt-4o" is substring "gpt-4o-2024-05-13" + chat_completion_models = ",".join(model["id"] for model in models_data if model["capabilities"]["chat_completion"]) + return [deployment["id"] for deployment in deployments_data if deployment["model"] in chat_completion_models] + + for deployment in _get_azure_openai_deployments(): + available_models[AZURE_OPENAI_PREFIX + deployment] = AzureChatOpenAI(azure_deployment=deployment) if available_models: logger.info("Available openai models: %s", ", ".join(available_models.keys())) - OpenAIModel = Enum('OpenAIModel', {name: name for name in available_models}) # type: ignore - - default_model_name = "gpt-3.5-turbo" + OpenAIModel = Enum('OpenAIModel', {name: name for name in available_models}) # type: ignore + default_model_name = None if "LLM_DEFAULT_MODEL" in os.environ and os.environ["LLM_DEFAULT_MODEL"] in available_models: default_model_name = os.environ["LLM_DEFAULT_MODEL"] if default_model_name not in available_models: default_model_name = list(available_models.keys())[0] - default_openai_model = OpenAIModel[default_model_name] - + default_openai_model = OpenAIModel[default_model_name] # type: ignore # Long descriptions will be displayed in the playground UI and are copied from the OpenAI docs class OpenAIModelConfig(ModelConfig): """OpenAI LLM configuration.""" - model_name: OpenAIModel = Field(default=default_openai_model, # type: ignore + model_name: OpenAIModel = Field(default=default_openai_model, # type: ignore description="The name of the model to use.") max_tokens: PositiveInt = Field(1000, description="""\ The maximum number of [tokens](https://platform.openai.com/tokenizer) to generate in the chat completion. @@ -313,7 +115,7 @@ def get_model(self) -> BaseLanguageModel: BaseLanguageModel: The model. """ model = available_models[self.model_name.value] - kwargs = model._lc_kwargs + kwargs = model.__dict__ secrets = {secret: getattr(model, secret) for secret in model.lc_secrets.keys()} kwargs.update(secrets) @@ -334,5 +136,6 @@ def get_model(self) -> BaseLanguageModel: model = model.__class__(**kwargs) return model + class Config: - title = 'OpenAI' + title = 'OpenAI' \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/bpmn_serializer.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/bpmn_serializer.py deleted file mode 100644 index 90452224b..000000000 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/bpmn_serializer.py +++ /dev/null @@ -1,858 +0,0 @@ -import uuid -from enum import Enum -from functools import reduce -from itertools import chain -from typing import Optional -from xml.etree import ElementTree - -DEFAULT_XSI_PREFIX = "xsi" -DEFAULT_BPMN_PREFIX = "bpmn" -DEFAULT_BPMNDI_PREFIX = "bpmndi" -DEFAULT_DC_PREFIX = "dc" -DEFAULT_DI_PREFIX = "di" - -INTERMEDIATE_CATCH_EVENT = "intermediateCatchEvent" -INTERMEDIATE_THROW_EVENT = "intermediateThrowEvent" - - -class BPMNElementType(str, Enum): - BPMN_ANNOTATION = "BPMNAnnotation" - BPMN_CALL_ACTIVITY = "BPMNCallActivity" - BPMN_DATA_OBJECT = "BPMNDataObject" - BPMN_DATA_STORE = "BPMNDataStore" - BPMN_END_EVENT = "BPMNEndEvent" - BPMN_GATEWAY = "BPMNGateway" - BPMN_GROUP = "BPMNGroup" - BPMN_INTERMEDIATE_EVENT = "BPMNIntermediateEvent" - BPMN_POOL = "BPMNPool" - BPMN_START_EVENT = "BPMNStartEvent" - BPMN_SUBPROCESS = "BPMNSubprocess" - BPMN_SWIMLANE = "BPMNSwimlane" - BPMN_TASK = "BPMNTask" - BPMN_TRANSACTION = "BPMNTransaction" - - -class BPMNFlowType(str, Enum): - SEQUENCE = "sequence" - MESSAGE = "message" - - -class BPMNStartEventType(str, Enum): - DEFAULT = "default" - MESSAGE = "message" - TIMER = "timer" - CONDITIONAL = "conditional" - SIGNAL = "signal" - - -class BPMNIntermediateEventType(str, Enum): - DEFAULT = "default" - MESSAGE_CATCH = "message-catch" - MESSAGE_THROW = "message-throw" - TIMER_CATCH = "timer-catch" - ESCALATION_CATCH = "escalation-catch" - CONDITIONAL_CATCH = "conditional-catch" - LINK_CATCH = "link-catch" - LINK_THROW = "link-throw" - COMPENSATION_THROW = "compensation-throw" - SIGNAL_CATCH = "signal-catch" - SIGNAL_THROW = "signal-throw" - - -class BPMNEndEventType(str, Enum): - DEFAULT = "default" - MESSAGE = "message" - ESCALATION = "escalation" - ERROR = "error" - COMPENSATION = "compensation" - SIGNAL = "signal" - TERMINATE = "terminate" - - -class BPMNTaskType(str, Enum): - DEFAULT = "default" - USER = "user" - SEND = "send" - RECEIVE = "error" - MANUAL = "manual" - BUSINESS_RULE = "business-rule" - SCRIPT = "script" - - -class BPMNGatewayType(str, Enum): - COMPLEX = "complex" - EVENT_BASED = "event-based" - EXCLUSIVE = "exclusive" - INCLUSIVE = "inclusive" - PARALLEL = "parallel" - - -class IDShortener: - id_map: dict[str, str] = {} - id_counter: int = 0 - - def shorten_id(self, id: str, prefix: Optional[str] = None) -> str: - """ - Shorten an ID to a minimum length numeric ID. This function mainly serves the purpose to shorten IDs to decrease - the token count for LLM queries. - :param id: The ID that should be shortened - :param prefix: An optional prefix for the shortened IDs - """ - - if prefix is None: - prefix = "id" - - if id not in self.id_map: - self.id_map[id] = f"{prefix}{'_' if prefix else ''}{self.id_counter}" - self.id_counter += 1 - - return self.id_map[id] - - def get_id_map(self) -> dict[str, str]: - """ - Retrieve the dictionary used to map a given ID to a shortened ID - """ - return self.id_map - - def get_reverse_id_map(self) -> dict[str, str]: - """ - Retrieve a dictionary allowing to map a shortened ID back to a full ID - """ - return {value: key for key, value in self.id_map.items()} - - def reset(self): - """ - Reset the IDShortener's internal ID map - """ - self.id_map = {} - - -class BPMNSerializer: - __start_event_definition_map = { - BPMNStartEventType.DEFAULT: None, - BPMNStartEventType.MESSAGE: "messageEventDefinition", - BPMNStartEventType.TIMER: "timerEventDefinition", - BPMNStartEventType.SIGNAL: "signalEventDefinition", - BPMNStartEventType.CONDITIONAL: "conditionalEventDefinition" - } - - __intermediate_event_type_map = { - BPMNIntermediateEventType.DEFAULT: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.MESSAGE_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.MESSAGE_THROW: INTERMEDIATE_THROW_EVENT, - BPMNIntermediateEventType.TIMER_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.ESCALATION_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.CONDITIONAL_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.LINK_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.LINK_THROW: INTERMEDIATE_THROW_EVENT, - BPMNIntermediateEventType.COMPENSATION_THROW: INTERMEDIATE_THROW_EVENT, - BPMNIntermediateEventType.SIGNAL_CATCH: INTERMEDIATE_CATCH_EVENT, - BPMNIntermediateEventType.SIGNAL_THROW: INTERMEDIATE_THROW_EVENT - } - - __intermediate_event_definition_map = { - BPMNIntermediateEventType.DEFAULT: None, - BPMNIntermediateEventType.MESSAGE_CATCH: "messageEventDefinition", - BPMNIntermediateEventType.MESSAGE_THROW: "messageEventDefinition", - BPMNIntermediateEventType.TIMER_CATCH: "timerEventDefinition", - BPMNIntermediateEventType.ESCALATION_CATCH: "escalationEventDefinition", - BPMNIntermediateEventType.CONDITIONAL_CATCH: "conditionalEventDefinition", - BPMNIntermediateEventType.LINK_CATCH: "linkEventDefinition", - BPMNIntermediateEventType.LINK_THROW: "linkEventDefinition", - BPMNIntermediateEventType.COMPENSATION_THROW: "compensationEventDefinition", - BPMNIntermediateEventType.SIGNAL_CATCH: "signalEventDefinition", - BPMNIntermediateEventType.SIGNAL_THROW: "signalEventDefinition" - } - - __end_event_definition_map = { - BPMNEndEventType.DEFAULT: None, - BPMNEndEventType.MESSAGE: "messageEventDefinition", - BPMNEndEventType.ESCALATION: "escalationEventDefinition", - BPMNEndEventType.ERROR: "errorEventDefinition", - BPMNEndEventType.COMPENSATION: "compensationEventDefinition", - BPMNEndEventType.SIGNAL: "signalEventDefinition", - BPMNEndEventType.TERMINATE: "terminateEventDefinition" - } - - __flow_type_map = { - BPMNFlowType.SEQUENCE: "sequenceFlow", - BPMNFlowType.MESSAGE: "messageFlow" - } - - __task_type_map = { - BPMNTaskType.DEFAULT: "task", - BPMNTaskType.USER: "userTask", - BPMNTaskType.SEND: "sendTask", - BPMNTaskType.RECEIVE: "receive", - BPMNTaskType.MANUAL: "manualTask", - BPMNTaskType.BUSINESS_RULE: "businessRuleTask", - BPMNTaskType.SCRIPT: "scriptTask" - } - - __gateway_type_map = { - BPMNGatewayType.COMPLEX: "exclusiveGateway", - BPMNGatewayType.EVENT_BASED: "eventBasedGateway", - BPMNGatewayType.EXCLUSIVE: "exclusiveGateway", - BPMNGatewayType.INCLUSIVE: "inclusiveGateway", - BPMNGatewayType.PARALLEL: "parallelGateway" - } - - __xsi_prefix: Optional[str] = "" - __bpmn_prefix: Optional[str] = "" - __bpmndi_prefix: Optional[str] = "" - __dc_prefix: Optional[str] = "" - __di_prefix: Optional[str] = "" - - id_shortener: Optional[IDShortener] = None - - def __init__(self, - xsi_prefix: str | None = DEFAULT_XSI_PREFIX, - bpmn_prefix: str | None = DEFAULT_BPMN_PREFIX, - bpmndi_prefix: str | None = DEFAULT_BPMNDI_PREFIX, - dc_prefix: str | None = DEFAULT_DC_PREFIX, - di_prefix: str | None = DEFAULT_DI_PREFIX - ): - """ - Create a new instance of the BPMNSerializer class. This class is used to serialize BPMN diagrams in Apollon's - native JSON format into the BPMN 2.0 XML standard representation. - :param xsi_prefix: - :param bpmn_prefix: - :param bpmndi_prefix: - :param dc_prefix: - :param di_prefix: - """ - super().__init__() - - self.__xsi_prefix = xsi_prefix - self.__bpmn_prefix = bpmn_prefix - self.__bpmndi_prefix = bpmndi_prefix - self.__dc_prefix = dc_prefix - self.__di_prefix = di_prefix - - self.id_shortener = IDShortener() - - @staticmethod - def __prefix_tag(tag: str, prefix: str | None) -> str: - """ - Prefix a XML tag with a given prefix - :param tag: The prefixed tag - :param prefix: The prefix to prepend - """ - return f"{prefix if prefix else ''}{':' if prefix else ''}{tag}" - - def __shorten_id(self, element_id: str, prefix: Optional[str] = None) -> str: - """ - Prefix an element id with a given prefix. - If no prefix is provided, 'bpmn' is used as default. - :param element_id: The element id that should be prefixed - :param prefix: The prefix to prepend - """ - - if self.id_shortener is None: - return element_id - - return self.id_shortener.shorten_id(element_id, prefix=prefix) - - @staticmethod - def __omit_keys(dictionary: dict, omitted_keys: list) -> dict: - """ - Copy a dictionary while omitting keys - :param dictionary: The dictionary that is omitted from - :param omitted_keys: A list of keys that should be omitted - """ - return {key: dictionary[key] for key in dictionary.keys() if key not in omitted_keys} - - @staticmethod - def __get_flows_connected_to_element(flows: list[dict], element_id: str) -> list[dict]: - """ - Get all flows connected to an element - :param flows: All flows in the diagram model - :param element_id: The id of the element to which the retrieved flows should be connected to - """ - - def is_element_connected(flow: dict) -> bool: - return flow.get("source", {}).get("element") == element_id or flow.get("target", {}).get( - "element") == element_id - - return list(filter(is_element_connected, flows)) - - def __serialize_base_element(self, element: dict, tag: str, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN flow to XML - :param element: A dictionary representing a BPMN flow element - :param tag: The name of the tag the element should be serialized as - :return: An Elementtree Element representing the serialized BPMN flow element - """ - - if connected_flows is None: - connected_flows = [] - - serialized_element: ElementTree.Element = ElementTree.Element(tag) - serialized_element.set("id", self.__shorten_id(element.get("id", ""))) - serialized_element.set("name", element.get("name", "")) - - for connected_flow in connected_flows: - source = connected_flow.get("source") - if source and source.get("element") == element.get("id"): - outgoing = ElementTree.Element(self.__prefix_tag("outgoing", self.__bpmn_prefix)) - outgoing.text = self.__shorten_id(connected_flow.get("id", "")) - serialized_element.append(outgoing) - - # This is deliberately not an "else" case as otherwise connections connecting an element with itself - # would not be correctly serialized. - target = connected_flow.get("target") - if target and target.get("element") == element.get("id"): - incoming = ElementTree.Element(self.__prefix_tag("incoming", self.__bpmn_prefix)) - incoming.text = self.__shorten_id(connected_flow.get("id", "")) - serialized_element.append(incoming) - - return serialized_element - - def __serialize_annotation(self, annotation: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN annotation object to XML - :param annotation: A dictionary representing a BPMN annotation element - :return: An Elementtree Element representing the serialized BPMN annotation element - """ - - tag: str = self.__prefix_tag("annotation", self.__bpmn_prefix) - serialized_annotation: ElementTree.Element = self.__serialize_base_element(annotation, tag, connected_flows) - - return serialized_annotation - - def __serialize_call_activity(self, call_activity: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize an BPMN call activity to XML - :param call_activity: A dictionary representing an BPMN call activity element - :return: An Elementtree Element representing the serialized BPMN call activity element - """ - tag: str = self.__prefix_tag("callActivity", self.__bpmn_prefix) - serialized_call_activity: ElementTree.Element = self.__serialize_base_element(call_activity, tag, - connected_flows) - - return serialized_call_activity - - def __serialize_data_object(self, data_object: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN data object to XML - :param data_object: A dictionary representing a BPMN data object element - :return: An Elementtree Element representing the serialized BPMN data object element - """ - tag: str = self.__prefix_tag("dataObject", self.__bpmn_prefix) - serialized_data_object: ElementTree.Element = self.__serialize_base_element(data_object, tag, connected_flows) - - return serialized_data_object - - def __serialize_data_store(self, data_store: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN data store object to XML - :param data_store: A dictionary representing a BPMN data store element - :return: An Elementtree Element representing the serialized BPMN data store element - """ - tag: str = self.__prefix_tag("dataStore", self.__bpmn_prefix) - serialized_data_store: ElementTree.Element = self.__serialize_base_element(data_store, tag, connected_flows) - - return serialized_data_store - - def __serialize_end_event(self, end_event: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN end event object to XML - :param end_event: A dictionary representing a BPMN end event element - :return: An Elementtree Element representing the serialized BPMN end event element - """ - tag: str = self.__prefix_tag("endEvent", self.__bpmn_prefix) - serialized_end_event: ElementTree.Element = self.__serialize_base_element(end_event, tag, connected_flows) - - event_type_tag: Optional[str] = self.__end_event_definition_map[ - end_event.get("eventType", BPMNEndEventType.DEFAULT)] - - if event_type_tag: - event_type_element: ElementTree.Element = ElementTree.Element( - self.__prefix_tag(event_type_tag, self.__bpmn_prefix)) - event_type_element.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_end_event.append(event_type_element) - - return serialized_end_event - - def __serialize_flow(self, flow: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN flow to XML - :param flow: A dictionary representing a BPMN flow element - :return: An Elementtree Element representing the serialized BPMN flow element - """ - tag: str = self.__prefix_tag(self.__flow_type_map.get(flow.get("flowType", BPMNFlowType.SEQUENCE), ""), - self.__bpmn_prefix) - serialized_flow: ElementTree.Element = self.__serialize_base_element(flow, tag, connected_flows) - - source: Optional[dict] = flow.get("source") - target: Optional[dict] = flow.get("target") - - if source: - source_element: str = self.__shorten_id(source.get("element", "")) - serialized_flow.set("sourceRef", source_element) - - if target: - target_element: str = self.__shorten_id(target.get("element", "")) - serialized_flow.set("targetRef", target_element) - - return serialized_flow - - def __serialize_gateway(self, gateway: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN gateway object to XML - :param gateway: A dictionary representing a BPMN gateway element - :return: An Elementtree Element representing the serialized BPMN gateway element - """ - gateway_type_tag: str = self.__prefix_tag( - self.__gateway_type_map[gateway.get("gatewayType", BPMNGatewayType.EXCLUSIVE)], - self.__bpmn_prefix) - serialized_gateway = self.__serialize_base_element(gateway, gateway_type_tag, connected_flows) - - return serialized_gateway - - def __serialize_group(self, group: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN group to XML - :param group: A dictionary representing a BPMN group element - :return: An Elementtree Element representing the serialized BPMN group element - """ - tag: str = self.__prefix_tag("group", self.__bpmn_prefix) - serialized_group: ElementTree.Element = self.__serialize_base_element(group, tag, connected_flows) - - return serialized_group - - def __serialize_intermediate_event(self, intermediate_event: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN intermediate event to XML - :param intermediate_event: A dictionary representing a BPMN intermediate event element - :return: An Elementtree Element representing the serialized BPMN intermediate event element - """ - tag: str = self.__prefix_tag( - self.__intermediate_event_type_map[intermediate_event.get("eventType", BPMNIntermediateEventType.DEFAULT)], - self.__bpmn_prefix) - serialized_intermediate_event: ElementTree.Element = self.__serialize_base_element(intermediate_event, tag, - connected_flows) - - event_type_tag: Optional[str] = self.__intermediate_event_definition_map[ - intermediate_event.get("eventType", BPMNIntermediateEventType.DEFAULT)] - - if event_type_tag: - event_type_element = ElementTree.Element(self.__prefix_tag(event_type_tag, self.__bpmn_prefix)) - event_type_element.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_intermediate_event.append(event_type_element) - - return serialized_intermediate_event - - def __serialize_pool(self, pool: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN pool to XML - :param pool: A dictionary representing a BPMN pool element - :return: An Elementtree Element representing the serialized BPMN pool element - """ - tag: str = self.__prefix_tag("laneSet", self.__bpmn_prefix) - serialized_pool: ElementTree.Element = self.__serialize_base_element(pool, tag, connected_flows) - - return serialized_pool - - def __serialize_start_event(self, start_event: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN start event to XML - :param start_event: A dictionary representing a BPMN start event element - :return: An Elementtree Element representing the serialized BPMN start event element - """ - tag: str = self.__prefix_tag("startEvent", self.__bpmn_prefix) - serialized_start_event: ElementTree.Element = self.__serialize_base_element(start_event, tag, connected_flows) - - event_type_tag: Optional[str] = self.__start_event_definition_map[ - start_event.get("eventType", BPMNStartEventType.DEFAULT)] - - if event_type_tag: - event_type_element = ElementTree.Element(self.__prefix_tag(event_type_tag, self.__bpmn_prefix)) - event_type_element.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_start_event.append(event_type_element) - - return serialized_start_event - - def __serialize_subprocess(self, subprocess: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN subprocess to XML - :param subprocess: A dictionary representing a BPMN subprocess element - :return: An Elementtree Element representing the serialized BPMN subprocess element - """ - tag: str = self.__prefix_tag("subprocess", self.__bpmn_prefix) - serialized_subprocess: ElementTree.Element = self.__serialize_base_element(subprocess, tag, connected_flows) - - return serialized_subprocess - - def __serialize_swimlane(self, swimlane: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN swimlane to XML - :param swimlane: A dictionary representing a BPMN swimlane element - :return: An Elementtree Element representing the serialized BPMN swimlane element - """ - tag: str = self.__prefix_tag("lane", self.__bpmn_prefix) - serialized_swimlane: ElementTree.Element = self.__serialize_base_element(swimlane, tag, connected_flows) - - return serialized_swimlane - - def __serialize_task(self, task: dict, connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN task to XML - :param task: A dictionary representing a BPMN task element - :return: An Elementtree Element representing the serialized BPMN task element - """ - task_tag: str = self.__prefix_tag(self.__task_type_map[task.get("taskType", BPMNTaskType.DEFAULT)], - self.__bpmn_prefix) - serialized_task: ElementTree.Element = self.__serialize_base_element(task, task_tag, connected_flows) - - return serialized_task - - def __serialize_transaction(self, transaction: dict, - connected_flows: Optional[list[dict]] = None) -> ElementTree.Element: - """ - Serialize a BPMN transaction to XML - :param transaction: A dictionary representing a BPMN transaction element - :return: An Elementtree Element representing the serialized BPMN transaction element - """ - tag: str = self.__prefix_tag("transaction", self.__bpmn_prefix) - serialized_transaction: ElementTree.Element = self.__serialize_base_element(transaction, tag, connected_flows) - - return serialized_transaction - - def __serialize_element(self, element: dict, connected_flows: Optional[list[dict]] = None) -> Optional[ - ElementTree.Element]: - """ - Serialize a BPMN element to XML - - This method selects the serializer corresponding to the given element based on its type property - :param element: A dictionary representing a BPMN element - :return: An Elementtree Element representing the serialized BPMN element - """ - - match element.get("type"): - case BPMNElementType.BPMN_ANNOTATION: - return self.__serialize_annotation(element, connected_flows) - case BPMNElementType.BPMN_CALL_ACTIVITY: - return self.__serialize_call_activity(element, connected_flows) - case BPMNElementType.BPMN_DATA_OBJECT: - return self.__serialize_data_object(element, connected_flows) - case BPMNElementType.BPMN_DATA_STORE: - return self.__serialize_data_store(element, connected_flows) - case BPMNElementType.BPMN_END_EVENT: - return self.__serialize_end_event(element, connected_flows) - case BPMNElementType.BPMN_GATEWAY: - return self.__serialize_gateway(element, connected_flows) - case BPMNElementType.BPMN_GROUP: - return self.__serialize_group(element, connected_flows) - case BPMNElementType.BPMN_INTERMEDIATE_EVENT: - return self.__serialize_intermediate_event(element, connected_flows) - case BPMNElementType.BPMN_POOL: - return self.__serialize_pool(element, connected_flows) - case BPMNElementType.BPMN_START_EVENT: - return self.__serialize_start_event(element, connected_flows) - case BPMNElementType.BPMN_SUBPROCESS: - return self.__serialize_subprocess(element, connected_flows) - case BPMNElementType.BPMN_SWIMLANE: - return self.__serialize_swimlane(element, connected_flows) - case BPMNElementType.BPMN_TASK: - return self.__serialize_task(element, connected_flows) - case BPMNElementType.BPMN_TRANSACTION: - return self.__serialize_transaction(element, connected_flows) - - return None - - def __serialize_shape(self, element: dict) -> ElementTree.Element: - """ - Serialize a BPMN element to a shape - :param element: A dictionary representing a BPMN element - :return: An Elementtree Element representing the serialized BPMN shape - """ - - tag: str = self.__prefix_tag("BPMNShape", self.__bpmndi_prefix) - serialized_shape: ElementTree.Element = ElementTree.Element(tag) - serialized_shape.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_shape.set("bpmnElement", self.__shorten_id(element.get("id", ""))) - - bounds: Optional[dict] = element.get("bounds") - - if bounds: - serialized_bounds = ElementTree.Element("dc:Bounds") - serialized_bounds.set("x", str(bounds.get("x", 0))) - serialized_bounds.set("y", str(bounds.get("y", 0))) - serialized_bounds.set("width", str(bounds.get("width", 0))) - serialized_bounds.set("height", str(bounds.get("height", 0))) - serialized_shape.append(serialized_bounds) - - return serialized_shape - - def __serialize_edge(self, relationship: dict) -> ElementTree.Element: - """ - Serialize a BPMN relationship to an edge - :param relationship: A dictionary representing a BPMN relationship - :return: An Elementtree Element representing the serialized BPMN edge - """ - - tag: str = self.__prefix_tag("BPMNEdge", self.__bpmndi_prefix) - serialized_edge: ElementTree.Element = ElementTree.Element(tag) - serialized_edge.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_edge.set("bpmnElement", self.__shorten_id(relationship.get("id", ""))) - - for point in relationship.get("path", []): - serialized_waypoint: ElementTree.Element = ElementTree.Element( - self.__prefix_tag("waypoint", self.__di_prefix)) - serialized_waypoint.set("x", str(point.get("x", 0))) - serialized_waypoint.set("y", str(point.get("y", 0))) - # serialized_edge.append(serialized_waypoint) - - return serialized_edge - - def __serialize_process(self, pool: dict, owned_elements: list[dict], - relationships: list[dict]) -> ElementTree.Element: - """ - Serialize the process tree of a given BPMN model - :param owned_elements: A dictionary containing the elements owned by serialized process - :param relationships: A dictionary the relationships contained in the diagram - :return: An Elementtree Element representing the serialized BPMN process tree - """ - - process: ElementTree.Element = ElementTree.Element(self.__prefix_tag("process", self.__bpmn_prefix)) - process.set("id", self.__shorten_id(pool.get("id", ""))) - process.set("isExecutable", "false") - - serialized_elements_by_type: dict[str, dict] = {} - serialized_elements_by_owner: dict[str, dict] = {} - - # We first gather all diagram elements and store them in index structures indexed - # by the element type and by their owner - for element in owned_elements: - connected_flows = self.__get_flows_connected_to_element(relationships, element.get("id", "")) - - serialized_element: Optional[ElementTree.Element] = self.__serialize_element(element, connected_flows) - - element_type = element.get("type", "") - - if serialized_element: - if element_type: - serialized_elements_by_type[element_type] = { - **serialized_elements_by_type.get(element_type, {}), - self.__shorten_id(element.get("id", "")): serialized_element - } - - element_owner = self.__shorten_id(serialized_element.get("owner", "")) - - if element_owner: - serialized_elements_by_owner[element_owner] = { - **serialized_elements_by_owner.get(element_owner, {}), - self.__shorten_id(serialized_element.get("id", "")): serialized_element - } - - serialized_pool: ElementTree.Element = ElementTree.Element(self.__prefix_tag("laneSet", self.__bpmn_prefix)) - serialized_pool.set("id", self.__shorten_id(str(uuid.uuid4()))) - serialized_pool.set("name", pool.get("name", "")) - - owned_serialized_elements: list[ElementTree.Element] = list( - chain.from_iterable([entry.values() for entry in serialized_elements_by_owner.values()])) - - for serialized_element in owned_serialized_elements: - - swimlanes = serialized_elements_by_type.get(BPMNElementType.BPMN_SWIMLANE) - - if swimlanes and swimlanes.get(serialized_element.get("id", "")): - # We know that the serialized element is a swimlane and therefore directly - # serialize it - serialized_pool.append(serialized_element) - - process.append(serialized_pool) - - # Iterate over all swimlanes and add flowNodeRefs for all elements within the current lane - for serialized_swimlane in serialized_elements_by_type.get(BPMNElementType.BPMN_SWIMLANE, {}).values(): - owned_serialized_elements = list(serialized_elements_by_owner[serialized_swimlane.get("id", "")].values()) - for serialized_element in owned_serialized_elements: - tag: str = self.__prefix_tag("flowNodeRef", self.__bpmn_prefix) - flow_node_ref = ElementTree.Element(tag) - flow_node_ref.text = serialized_element.get("id") - serialized_swimlane.append(flow_node_ref) - - serialized_pool.append(serialized_swimlane) - - # We get the dictionary of elements without pools and swimlanes - non_bounding_elements = self.__omit_keys(serialized_elements_by_type, [BPMNElementType.BPMN_SWIMLANE]) - - for serialized_element in chain.from_iterable([entry.values() for entry in non_bounding_elements.values()]): - if serialized_element: - process.append(serialized_element) - - return process - - def __serialize_plane(self, model: dict) -> ElementTree.Element: - """ - Serialize the diagram tree of a given BPMN model - :param model: A dictionary representing a BPMN diagram model - :return: An Elementtree Element representing the serialized BPMN diagram tree - """ - - plane: ElementTree.Element = ElementTree.Element(self.__prefix_tag("BPMNPlane", self.__bpmndi_prefix)) - plane.set("id", self.__shorten_id(str(uuid.uuid4()))) - - elements: Optional[dict] = model.get("elements") - relationships: Optional[dict] = model.get("relationships") - - if elements: - for element in elements.values(): - # We do not serialize BPMN pools directly as they are serialized based on participants - # later in the process - if element.get("type") != BPMNElementType.BPMN_POOL: - serialized_shape: ElementTree.Element = self.__serialize_shape(element) - plane.append(serialized_shape) - - if relationships: - for relationship in relationships.values(): - serialized_edge: ElementTree.Element = self.__serialize_edge(relationship) - plane.append(serialized_edge) - - return plane - - def __split_elements_by_owning_pool(self, elements: dict) -> dict: - """ - Splits a given list of elements into a dictionary with the IDs of the root - pool elements as keys and lists of elements contained within the respective pools - as values. Elements that are not contained in a pool are returned as a list. - :param elements: The list of elements that should be split - :return: A tuple containing the dictionary of elements by owning pools and a list - containing the elements without an owner. - """ - elements_by_owning_pool: dict[str, list] = reduce(lambda acc, element: { - **acc, - (element.get("id", "")): [] - } if element.get("type") == BPMNElementType.BPMN_POOL else acc, elements.values(), {}) - - unowned_elements = [] - - for element in elements.values(): - if not element.get("type") == BPMNElementType.BPMN_POOL: - owner = self.__find_root_owner_id(elements, element.get("id", "")) - - # If the owner of an element is returned as the element's own ID, - # the element is not contained in a pool. If the owner is not a key in the - # elementy_by_owning_pool dictionary, we assume that the element is owned by - # another container like a group that is not located within a pool. - if owner == element.get("id") or elements_by_owning_pool.get(owner, None) is None: - unowned_elements.append(element) - else: - elements_by_owning_pool[owner].append(element) - - # Add all elements that are not contained in a pool to an unnamed pool - if len(unowned_elements) > 0: - elements_by_owning_pool[""] = unowned_elements - - return elements_by_owning_pool - - def __find_root_owner_id(self, elements: dict, current_id: str) -> str: - """ - Find the id of the root owner of an element. - If the element is contained within a pool, the root owner is said pool. - If the element is not contained within another element at all, the element's own ID is returned. - :param elements: The elements of the current diagram - :param current_id: The element of the element whose root owner should be determined - :return: The id of the root owner of the given current_id - """ - - current_element: Optional[dict] = elements.get(current_id) - - if current_element: - owner_id: Optional[str] = current_element.get("owner") - - if owner_id: - return self.__find_root_owner_id(elements, owner_id) - - return current_id - - def serialize(self, model: dict, omit_layout_info: bool = False) -> ElementTree.Element: - """ - Serialize a BPMN diagram in Apollon's native JSON format to XML according to the BPMN 2.0 standard - :param model: A dictionary representing a BPMN diagram model - :param omit_layout_info: Indicates whether layouting information should be included in the diagram - :return: An Elementtree Element representing the serialized BPMN diagram - """ - - if self.id_shortener: - # The IDShortener is reset to ensure no prior ID mappings are cached - self.id_shortener.reset() - - definitions: ElementTree.Element = ElementTree.Element(self.__prefix_tag("definitions", self.__bpmn_prefix)) - - definitions.set("id", "Definition") - definitions.set(f"xmlns:{self.__xsi_prefix}", "http://www.w3.org/2001/XMLSchema-instance") - definitions.set(f"xmlns:{self.__bpmn_prefix}", "http://www.omg.org/spec/BPMN/20100524/MODEL") - definitions.set(f"xmlns:{self.__bpmndi_prefix}", "http://www.omg.org/spec/BPMN/20100524/DI") - definitions.set(f"xmlns:{self.__dc_prefix}", "http://www.omg.org/spec/DD/20100524/DC") - definitions.set(f"xmlns:{self.__di_prefix}", "http://www.omg.org/spec/DD/20100524/DI") - definitions.set("targetNamespace", "http://bpmn.io/schema/bpmn") - - diagram: ElementTree.Element = ElementTree.Element(self.__prefix_tag("BPMNDiagram", self.__bpmndi_prefix)) - serialized_plane = self.__serialize_plane(model) - diagram.append(serialized_plane) - - elements_by_owning_pool = self.__split_elements_by_owning_pool(model.get("elements", {})) - - relationships: list[dict] = list(model.get("relationships", {}).values()) - - if len(elements_by_owning_pool.keys()) > 0: - - collaboration: ElementTree.Element = ElementTree.Element( - self.__prefix_tag("collaboration", self.__bpmn_prefix)) - collaboration.set("id", self.__shorten_id(str(uuid.uuid4()))) - definitions.append(collaboration) - - for index, pool_id in enumerate(elements_by_owning_pool): - - pool: dict = model.get("elements", {}).get(pool_id, {}) - serialized_process = self.__serialize_process(pool, elements_by_owning_pool.get(pool_id, {}), - relationships) - definitions.append(serialized_process) - - # We append all flows to the first process as the flows coming from the Apollon - # diagrams do not have owners assigned. - if index == 0: - for relationship in relationships: - serialized_relationship = self.__serialize_flow(relationship) - - # TODO: Add support for handling associations. - # It might make sense to either treat them similar to messages flows or to explicitly check if - # an element is fully contained within a pool or not an then either attach it to the pool's - # process or to the collaboration element. - match relationship.get("flowType"): - case BPMNFlowType.SEQUENCE: - serialized_process.append(serialized_relationship) - case BPMNFlowType.MESSAGE: - collaboration.append(serialized_relationship) - - participant: ElementTree.Element = ElementTree.Element( - self.__prefix_tag("participant", self.__bpmn_prefix)) - participant_id: str = self.__shorten_id(str(uuid.uuid4())) - participant.set("id", participant_id) - participant.set("processRef", self.__shorten_id(pool_id)) - participant.set("name", pool.get("name", "")) - collaboration.append(participant) - - pool_shape = self.__serialize_shape(pool) - pool_shape.set("bpmnElement", participant_id) - # We add this attribute by default as Apollon diagrams currently only support horizontal pools - pool_shape.set("isHorizontal", "true") - serialized_plane.append(pool_shape) - - serialized_plane.set("bpmnElement", collaboration.get("id", "")) - - if not omit_layout_info: - definitions.append(diagram) - - return definitions diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/diagram_model_serializer.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/diagram_model_serializer.py index 36e5dc42c..4b4a78bf8 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/diagram_model_serializer.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/diagram_model_serializer.py @@ -1,74 +1,29 @@ -import json from typing import Optional -from xml.dom import minidom -from xml.etree import ElementTree - -from module_modeling_llm.helpers.models.diagram_types import DiagramType -from module_modeling_llm.helpers.serializers.bpmn_serializer import BPMNSerializer +from module_modeling_llm.helpers.serializers.parser.uml_parser import UMLParser class DiagramModelSerializer: @staticmethod - def serialize_model(model: dict) -> tuple[Optional[str], Optional[dict[str, str]]]: + def serialize_model(model: dict) -> tuple[Optional[str], dict[str, str]]: """ - Serialize a given Apollon diagram model to string. At the moment, this method returns a native JSON - serialization of the diagram for all diagram types other than BPMN diagrams. This diagram type is serialized in - the BPMN 2.0 XML format as this format is following an official standard and therefore well understood by most - LLMs. - - The function returns a tuple consisting of string representing the serialized diagram and a dictionary allowing - to map shortened IDs back to full IDs if needed. + Serialize a given Apollon diagram model to a string representation. + This method converts the UML diagram model into a format similar to mermaid syntax, called "apollon". + + :param model: The Apollon diagram model to serialize. + :return: A tuple containing the serialized model as a string and a dictionary mapping element and relation names + to their corresponding IDs. """ - match model.get("type"): - case DiagramType.CLASS_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.OBJECT_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.ACTIVITY_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.USE_CASE_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.COMMUNICATION_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.COMPONENT_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.DEPLOYMENT_DIAGRAM: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.PETRI_NET: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.REACHABILITY_GRAPH: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.SYNTAX_TREE: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.FLOWCHART: - # TODO: Evaluate if there is a more sensible serialization format for this diagram type - return json.dumps(model), None - case DiagramType.BPMN: - serializer = BPMNSerializer( - xsi_prefix=None, - bpmn_prefix=None, - bpmndi_prefix=None, - dc_prefix=None, - di_prefix=None - ) - - serialized_model: str = ElementTree.tostring(serializer.serialize(model, omit_layout_info=True), - encoding='utf8') - - reverse_id_map = serializer.id_shortener.get_reverse_id_map() if serializer.id_shortener else None - - # The use of minidom is only required here "pretty-print" the XML output for easier debugging - return minidom.parseString(serialized_model).toprettyxml(indent="\t"), reverse_id_map - - return None, None + parser = UMLParser(model) + + # Convert the UML diagram to the apollon representation + apollon_representation = parser.to_apollon() + + # Extract elements and relations with their corresponding IDs and names + names = { + **{element['name']: element['id'] for element in parser.get_elements()}, + **{relation['name']: relation['id'] for relation in parser.get_relations()} + } + + return apollon_representation, names + \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/element.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/element.py new file mode 100644 index 000000000..3b22a0269 --- /dev/null +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/element.py @@ -0,0 +1,51 @@ +from typing import Dict, Any, List, Optional + +class Element: + """ + Represents an element in a UML diagram. + + This class encapsulates the properties and behavior of a UML element, + including its attributes and methods. + """ + + def __init__(self, data: Dict[str, Any], element_dict: Optional[Dict[str, Any]] = None): + self.id: str = data.get('id', '') + self.type: str = data.get('type', '') + self.name: str = data.get('name', '') + self.owner: str = data.get('owner', '') + self.attribute_refs: List[str] = data.get('attributes', []) + self.method_refs: List[str] = data.get('methods', []) + self.attributes: List[str] = [] + self.methods: List[str] = [] + if element_dict is not None: + self.resolve_references(element_dict) + + def resolve_references(self, element_dict: Dict[str, Any]): + """ + Resolve attribute and method references using the provided element dictionary. The json data contains only references to other elements that represent attributes and methods. This method resolves these references to the actual names of the attributes and methods by looking up the corresponding elements via their IDs in the provided element dictionary. + """ + self.attributes = [element_dict[ref].get("name", "") for ref in self.attribute_refs if ref in element_dict] + self.methods = [element_dict[ref].get('name', '') for ref in self.method_refs if ref in element_dict] + + for ref_list, target_list in [(self.attribute_refs, self.attributes), (self.method_refs, self.methods)]: + target_list.extend( + element_dict.get(ref, {}).get("name", "") for ref in ref_list if ref in element_dict + ) + + def to_apollon(self) -> str: + parts = [f"[{self.type}] {self.name}"] + + if self.attributes or self.methods: + details = [] + if self.attributes: + details.append(" attributes:") + details.extend(f" {attr}" for attr in self.attributes) + if self.methods: + details.append(" methods:") + details.extend(f" {method}" for method in self.methods) + parts.append("{\n" + "\n".join(details) + "\n}") + + return " ".join(parts) + + def __getitem__(self, key): + return self.__dict__[key] \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/relation.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/relation.py new file mode 100644 index 000000000..c47506b30 --- /dev/null +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/relation.py @@ -0,0 +1,105 @@ +from typing import Dict, Any, List, Optional + +class Relation: + """ + Represents a relationship between elements in a UML diagram. + + This class encapsulates the properties and behavior of a UML relationship, + including its type, source and target elements, and associated messages. + """ + def __init__(self, data: Dict[str, Any], element_dict: Optional[Dict[str, Any]], index: int): + self.id: str = data.get('id', '') + self.type: str = data.get('type', '') + # Check if flowType exists, if so use that as the type + self.type = data.get('flowType', self.type) + self.label: str = data.get('name', '') + self.source: Dict[str, Any] = data.get('source', {}) + self.target: Dict[str, Any] = data.get('target', {}) + self.messages: List[Dict[str, str]] = data.get('message', []) + self.name = f"R{index}" + if element_dict is not None: + self.resolve_references(element_dict) + + def resolve_references(self, element_dict: Dict[str, Any]): + """ + Resolve the source and target element references using the provided element dictionary. The json data contains only references to other elements that represent the source and target elements. This method resolves these references to the actual names of the elements by looking up the corresponding elements via their IDs in the provided element dictionary. + """ + if self.source['element'] in element_dict: + self.source['element'] = element_dict[self.source['element']].get("name", "") + if self.target['element'] in element_dict: + self.target['element'] = element_dict[self.target['element']].get("name", "") + + def to_apollon(self) -> str: + parts = [f"{self.name}: {self.source['element']} {get_relation_arrow(self.type)} {self.target['element']}"] + + if self.label: + parts[0] += f": {self.label}" + + details = [] + for end in ['source', 'target']: + end_data = getattr(self, end) + if 'role' in end_data or 'multiplicity' in end_data: + end_details = [f" {end_data['element']}: {{"] + if 'role' in end_data: + end_details.append(f" role: {end_data['role']}") + if 'multiplicity' in end_data: + end_details.append(f" multiplicity: {end_data['multiplicity']}") + end_details.append(" }") + details.extend(end_details) + + if self.messages: + details.append(" messages: [") + for message in self.messages: + to_element = self.target['element'] if message['direction'] == 'target' else self.source['element'] + details.append(f" {{ name: {message['name']}, to_direction: {to_element} }}") + details.append(" ]") + + if details: + parts.append("{\n" + "\n".join(details) + "\n}") + + return " ".join(parts) + + def __getitem__(self, key): + return self.__dict__[key] + +def get_relation_arrow(relation_type: str) -> str: + """ + Returns the correct arrow based on the relation type or flow type using string containment. + + Parameters: + relation_type (str): The type of the relation (e.g., "ClassAggregation", "BPMNFlow_sequence"). + + Returns: + str: The arrow representation for the given relation type in Mermaid syntax. + """ + arrow_map = { + # Keys sorted manually by length in descending order to ensure correct matching when using endswith, e.g., when we have dataassociation, dataassociation should be checked before association + "interfacerequired": "--c", + "interfaceprovided": "--", + "dataassociation": "-->", + "generalization": "<|--", + "unidirectional": "-->", + "bidirectional": "<-->", + "association": "-->", + "inheritance": "<|--", + "composition": "*--", + "aggregation": "o--", + "realization": "..|>", + "dependency": "..>", + "sequence": "-->", + "message": "-->", + "include": "..>", + "message": "-->", + "extend": "-->", + "flow": "-->", + "link": "-->", + "arc": "-->", + } + + relation_type = relation_type.replace(" ", "").lower() + + for key, value in arrow_map.items(): + if relation_type.endswith(key): + return f"({relation_type}) {value}" + + return f"-- {relation_type} --" \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/uml_parser.py b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/uml_parser.py new file mode 100644 index 000000000..9142c8225 --- /dev/null +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/helpers/serializers/parser/uml_parser.py @@ -0,0 +1,91 @@ +from typing import Dict, Any, List +from string import ascii_uppercase + +from module_modeling_llm.helpers.serializers.parser.element import Element +from module_modeling_llm.helpers.serializers.parser.relation import Relation + + +class UMLParser: + """ + A parser for UML diagrams + + This class is responsible for parsing JSON data representing a Apollon UML diagram + and converting it into a mermaid like textual representation + """ + + def __init__(self, json_data: Dict[str, Any]): + self.data: Dict[str, Any] = json_data + self.title: str = self.data['type'] + self.elements: List[Element] = [] + self.relations: List[Relation] = [] + self.owners: Dict[str, List[str]] = {} + self._parse() + + def _parse(self) -> None: + name_count: Dict[str, int] = {} + referenced_ids: List[str] = [] + name_suffix_counters: Dict[str, int] = {} + + # Get all referenced attributes and methods + for element_data in self.data['elements'].values(): + referenced_ids.extend(element_data.get('attributes', [])) + referenced_ids.extend(element_data.get('methods', [])) + + # Count occurrences of each name + for element_data in self.data['elements'].values(): + name = element_data.get('name') + name_count[name] = name_count.get(name, 0) + 1 + name_suffix_counters[name] = 0 + + # Filter elements and ensure unique names for duplicates + # This filters out all Elements that are referenced by any other Element, as they are attributes or methods + for element_data in self.data['elements'].values(): + if element_data.get('id') not in referenced_ids: + name = element_data.get('name') + if name_count[name] > 1: + suffix_index = name_suffix_counters[name] + element_data['name'] = f"{name}{ascii_uppercase[suffix_index]}" + name_suffix_counters[name] += 1 + + element = Element(element_data, self.data['elements']) + self.elements.append(element) + + # Parse relations + for index, relation_data in enumerate(self.data['relationships'].values()): + relation = Relation(relation_data, self.data['elements'], index + 1) + self.relations.append(relation) + + # Get all owners and their elements + for element in self.elements: + ownerId = element.owner + if ownerId: + owner_element = next((el for el in self.elements if el.id == ownerId), None) + if owner_element: + ownerName = owner_element.name + if ownerName not in self.owners: + self.owners[ownerName] = [] + self.owners[ownerName].append(element.name) + + def to_apollon(self) -> str: + lines: List[str] = [f"UML Diagram Type: {self.title}", ""] + + if self.elements: + lines.append("@Elements:\n") + lines.extend(element.to_apollon() for element in self.elements) + + if self.relations: + lines.append("\n\n@Relations:\n") + lines.extend(relation.to_apollon() for relation in self.relations) + + if self.owners: + lines.append("\n\n@Owners:\n") + for owner, children in self.owners.items(): + lines.append(f"{owner}: {', '.join(children)}") + + return "\n".join(lines) + + def get_elements(self) -> List[Element]: + return self.elements + + def get_relations(self) -> List[Relation]: + return self.relations \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/apollon_format.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/apollon_format.py new file mode 100644 index 000000000..7dd4f5b77 --- /dev/null +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/apollon_format.py @@ -0,0 +1,97 @@ +apollon_format_description = """ +### Apollon Format Documentation + +The Apollon format is a textual representation of UML diagrams, designed to be easily understood and interpreted by both humans and language models (LLMs). It resembles the Mermaid format and allows for the description of various UML diagram types with a structured syntax. + +#### Diagram Types + +The Apollon format supports many different UML diagram types, including: + +- **ClassDiagram** +- **ObjectDiagram** +- **ActivityDiagram** +- **UseCaseDiagram** +- **CommunicationDiagram** +- **ComponentDiagram** +- **DeploymentDiagram** +- **PetriNet** +- **ReachabilityGraph** +- **SyntaxTree** +- **Flowchart** +- **BPMN** + +#### Format Structure + +The Apollon format is organized into three main categories: + +1. **Elements**: The basic components of the diagram (e.g., classes, objects, activities). +2. **Relations**: Connections between elements that depict relationships or interactions. +3. **Nested Elements**: Hierarchical structures or containment relationships. + +##### General Structure + +```apollon +UML Diagram Type: + +Elements: +[ElementType] + +Relations: +R: () + +Nested Elements: +: [] +``` + +##### Detailed Breakdown + +- **Elements**: + - Defined by their type and name. Optionally, attributes and methods can be listed. + - Example: + ```apollon + [Class] User { + attributes: + name + email + methods: + login() + logout() + } + ``` + +- **Relations**: + - Relations define the connections between two elements, capturing the nature of their interaction. Each relation can include optional attributes such as labels, roles, multiplicities, and messages to provide more detailed information. + - The `R` syntax serves as a unique identifier for each relation, ensuring that it can be easily referenced elsewhere + - Example (Basic): + ```apollon + R1: User --> Order + - Example (With label): + ```apollon + R1: User (Association) --> Order: places + ``` + - Example (With label, roles and multiplicities): + ```apollon + R2: User (Association) --> Order { + label: places + User: { + role: customer + multiplicity: 1 + } + Order: { + role: order + multiplicity: * + } + messages: [ + { name: "Place Order", to_direction: Order } + ] + } + ``` + +- **Nested Elements**: + - Used to represent elements contained within other elements. + - Example: + ```apollon + Package: [User, Order, Product] + ``` + +""" \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/generate_suggestions.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/generate_suggestions.py index c6b3ef0cb..644687828 100644 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/generate_suggestions.py +++ b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/generate_suggestions.py @@ -1,27 +1,113 @@ -system_message = """\ -You are an AI tutor for modeling exercise assessment at a prestigious university. +# Constants for prompt components +ROLE = "You are an AI tutor for {submission_format} modeling exercise assessment at a prestigious university." -# Task -Create graded feedback suggestions for a student\'s modeling submission that a human tutor would accept. \ +GRADED_FEEDBACK_TASK = """ +Create graded feedback suggestions for a student's {submission_format} modeling submission that a human tutor would accept. Meaning, the feedback you provide should be applicable to the submission with little to no modification. +""" + +NON_GRADED_FEEDBACK_TASK = """ +Your task is to modify given feedback. This feedback was originally created for tutors to help them grade student submissions. We want to use this same feedback for students before the due date of the assignment. However we need to make sure that the feedback does not give away the solution. Your task is to modify the feedback so that it is appropriate for students. + +Follow these guidelines when filtering and rewriting the feedback: + +- Avoid revealing specific solutions or correct answers. +- Focus on providing general guidance and encouragement. +- Highlight areas that need improvement without giving away the solution. +- Maintain a positive and constructive tone. +- Provide hints or suggestions rather than direct corrections. +- Keep the feedback concise and to the point. +- Ensure that the feedback is still relevant and helpful for the student's learning process. -# Style -1. Constructive, 2. Specific, 3. Balanced, 4. Clear and Concise, 5. Actionable, 6. Educational, 7. Contextual +Keep the original strucute, just change the title and description of the feedback -# Submission format remarks -{submission_format_remarks} +For example, original feedback: +"title": "Missing Start Event" +"description": "The process is missing the start event" +... -# Problem statement +Filtered and rewritten feedback: +"title": "Process Initiation" +"description": "Consider how your process begins. Is there a clear starting point? Review the standard elements used to indicate the commencement of a process flow." +... + +Remember, the goal is to guide the student towards improvement without providing a complete solution or grading information in the feedback. Also keep the original structure of the feedback. Just modify the title and description values. +""" + +STYLE = """ + +1. Constructive +2. Specific +3. Balanced +4. Clear and Concise +5. Actionable +6. Educational +7. Contextual +""" + +PROBLEM_STATEMENT = """ + {problem_statement} +""" + +EXERCISE_GRADING_INSTRUCTIONS = """ + -# Grading instructions -{grading_instructions} Max points: {max_points}, bonus points: {bonus_points}\ + +Instructions: +{grading_instructions} """ -human_message = """\ -Student\'s submission to grade: -\"\"\" +GRADING_INSTRUCTIONS = """ +Important: +Make sure to provide detailed feedback for each criterion. Always try to be as specific as possible. +Also make sure your feedback adds up to the correct number of points. If there are n points available and everything is correct, then the feedback should add up to n points. +Deeply think about the diagram and what the student potentially missed, misunderstood or mixed up. + +Example: +- Element 1: 1 point +- Element 2: 1 point +- Element 3: 0 points +- Element 4: 1 point +- Element 5: 1 point +- Relation 1: 1 point +- Relation 2: 1 point +... +""" + +UML_DIAGRAM_FORMAT = """ + +The submission uses the following UML diagram format: +{uml_diagram_format} +""" + +FORMAT_INSTRUCTIONS = """ + +{format_instructions} +""" + +OFFICIAL_EXAMPLE_SOLUTION = """ + +{example_solution} +""" + +STUDENT_SUBMISSION = """ + {submission} -\"\"\"\ """ + +OUTPUT_FORMAT = """ +Please return the feedback in the correct json format. +""" + +GRADED_FEEDBACK_OUTPUT = """ + +{original_feedback} +""" + +graded_feedback_system_message = f"{ROLE}\n\n{GRADED_FEEDBACK_TASK}\n\n{STYLE}\n\n{PROBLEM_STATEMENT}\n\n{EXERCISE_GRADING_INSTRUCTIONS}\n\n{OFFICIAL_EXAMPLE_SOLUTION}\n\n{GRADING_INSTRUCTIONS}\n\n{UML_DIAGRAM_FORMAT}\n\n{FORMAT_INSTRUCTIONS}" +graded_feedback_human_message = f"{STUDENT_SUBMISSION}\n\n{OUTPUT_FORMAT}" + +filter_feedback_system_message = f"{NON_GRADED_FEEDBACK_TASK}\n\n{FORMAT_INSTRUCTIONS}" +filter_feedback_human_message = f"{GRADED_FEEDBACK_OUTPUT}\n\n{OUTPUT_FORMAT}" \ No newline at end of file diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/__init__.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/bpmn.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/bpmn.py deleted file mode 100644 index a810226e8..000000000 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/bpmn.py +++ /dev/null @@ -1,2 +0,0 @@ -submission_format_remarks = """The submissions are provided as BPMN 2.0 XML and need to match the following problem -statement.""" diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/general.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/general.py deleted file mode 100644 index a6cfbe202..000000000 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/general.py +++ /dev/null @@ -1 +0,0 @@ -submission_format_remarks = """The submissions need to match the following problem statement.""" diff --git a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/submission_format_remarks.py b/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/submission_format_remarks.py deleted file mode 100644 index 068739eb7..000000000 --- a/modules/modeling/module_modeling_llm/module_modeling_llm/prompts/submission_format/submission_format_remarks.py +++ /dev/null @@ -1,33 +0,0 @@ -from module_modeling_llm.helpers.models.diagram_types import DiagramType -from module_modeling_llm.prompts.submission_format.bpmn import \ - submission_format_remarks as bpmn_submission_format_remarks -from module_modeling_llm.prompts.submission_format.general import \ - submission_format_remarks as general_submission_format_remarks - - -def get_submission_format_remarks(diagram_type: DiagramType) -> str: - match diagram_type: - case DiagramType.CLASS_DIAGRAM: - return general_submission_format_remarks - case DiagramType.OBJECT_DIAGRAM: - return general_submission_format_remarks - case DiagramType.ACTIVITY_DIAGRAM: - return general_submission_format_remarks - case DiagramType.USE_CASE_DIAGRAM: - return general_submission_format_remarks - case DiagramType.COMMUNICATION_DIAGRAM: - return general_submission_format_remarks - case DiagramType.COMPONENT_DIAGRAM: - return general_submission_format_remarks - case DiagramType.DEPLOYMENT_DIAGRAM: - return general_submission_format_remarks - case DiagramType.PETRI_NET: - return general_submission_format_remarks - case DiagramType.REACHABILITY_GRAPH: - return general_submission_format_remarks - case DiagramType.SYNTAX_TREE: - return general_submission_format_remarks - case DiagramType.FLOWCHART: - return general_submission_format_remarks - case DiagramType.BPMN: - return bpmn_submission_format_remarks diff --git a/modules/modeling/module_modeling_llm/poetry.lock b/modules/modeling/module_modeling_llm/poetry.lock index fe0cb96b1..b498f3731 100644 --- a/modules/modeling/module_modeling_llm/poetry.lock +++ b/modules/modeling/module_modeling_llm/poetry.lock @@ -138,13 +138,13 @@ frozenlist = ">=1.1.0" [[package]] name = "anyio" -version = "3.7.1" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"}, - {file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"}, + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, ] [package.dependencies] @@ -152,9 +152,9 @@ idna = ">=2.8" sniffio = ">=1.1" [package.extras] -doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"] -test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] 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