mirror of
https://github.com/hanzoai/insights.git
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zeekay
1b367e2b62
The tree was a debranded snapshot of upstream taken around 2026-02-20, and upstream has moved about five months since. This replaces it with upstream master, mechanically re-derived: bin/debrand does the rename, and the pipeline and squashed migrations are restored from the shipped tree. Not a merge. The previous import replaced the tree wholesale in a single commit, so there is no shared content history to merge against. Reproducing the rename on the new tree is what actually converges. What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md, and insights/migrations. The squashed initial migration is load-bearing — the live database records it by name, so upstream's ~918 individual migrations cannot come back. Nine products are dropped as unreferenced. Twenty-one more that we do not run are kept anyway because the core app still imports them; unpicking those is a refactor and does not belong in a rebase. This base does not build yet, and the reasons are not in the rename: upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against our 4.2, and the dependency manifests still name upstream packages that we republish under our own names. Those are decisions, not renames.
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
"""
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Facade API for experiments product.
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This module provides the public interface for creating and managing experiments
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using framework-free DTOs, wrapping the existing ExperimentService.
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"""
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from rest_framework.exceptions import ValidationError
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from insights.models.team import Team
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from insights.models.user import User
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from products.experiments.backend.experiment_service import ExperimentService
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from products.experiments.backend.models.experiment import Experiment as ExperimentModel
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from .contracts import CreateExperimentInput, Experiment
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def create_experiment(*, team: Team, user: User, input_dto: CreateExperimentInput) -> Experiment:
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"""
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Create a new experiment.
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Transactional safety is provided by ExperimentService.create_experiment.
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Args:
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team: Team creating the experiment
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user: User creating the experiment
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input_dto: Experiment creation input with all configuration
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Returns:
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Experiment DTO
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Raises:
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ValidationError: If validation fails in service layer
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"""
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# Load holdout if ID provided
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from products.experiments.backend.models.experiment import ExperimentHoldout
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holdout = None
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if input_dto.holdout_id is not None:
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try:
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holdout = ExperimentHoldout.objects.get(id=input_dto.holdout_id, team_id=team.id)
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except ExperimentHoldout.DoesNotExist:
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raise ValidationError(f"Holdout with id {input_dto.holdout_id} does not exist for this team")
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# Convert tuple to list for ordering fields (DTO uses tuple for immutability)
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primary_metrics_ordered_uuids = list(input_dto.metrics_ordering) if input_dto.metrics_ordering else None
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secondary_metrics_ordered_uuids = (
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list(input_dto.secondary_metrics_ordering) if input_dto.secondary_metrics_ordering else None
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)
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# Call existing service (already @transaction.atomic)
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service = ExperimentService(team=team, user=user)
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experiment_model = service.create_experiment(
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name=input_dto.name,
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feature_flag_key=input_dto.feature_flag_key,
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description=input_dto.description,
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type=input_dto.type,
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parameters=input_dto.parameters,
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feature_flag_config=input_dto.feature_flag_config,
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running_time_calculation=input_dto.running_time_calculation,
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excluded_variants=input_dto.excluded_variants,
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metrics=input_dto.metrics,
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metrics_secondary=input_dto.metrics_secondary,
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secondary_metrics=input_dto.secondary_metrics,
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stats_config=input_dto.stats_config,
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exposure_criteria=input_dto.exposure_criteria,
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holdout=holdout,
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saved_metrics_ids=input_dto.saved_metrics_ids,
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start_date=input_dto.start_date,
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end_date=input_dto.end_date,
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primary_metrics_ordered_uuids=primary_metrics_ordered_uuids,
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secondary_metrics_ordered_uuids=secondary_metrics_ordered_uuids,
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create_in_folder=input_dto.create_in_folder,
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filters=input_dto.filters,
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scheduling_config=input_dto.scheduling_config,
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only_count_matured_users=input_dto.only_count_matured_users,
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archived=input_dto.archived,
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deleted=input_dto.deleted,
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conclusion=input_dto.conclusion,
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conclusion_comment=input_dto.conclusion_comment,
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repository=input_dto.repository,
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serializer_context=input_dto.serializer_context,
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allow_unknown_events=input_dto.allow_unknown_events,
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)
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# Convert model to DTO
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return _experiment_model_to_dto(experiment_model)
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def _experiment_model_to_dto(experiment: ExperimentModel) -> Experiment:
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"""Convert Django model to DTO."""
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return Experiment(
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id=experiment.id,
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name=experiment.name,
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description=experiment.description or None,
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feature_flag_id=experiment.feature_flag_id,
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feature_flag_key=experiment.feature_flag.key,
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is_draft=experiment.start_date is None,
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start_date=experiment.start_date,
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end_date=experiment.end_date,
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created_at=experiment.created_at,
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updated_at=experiment.updated_at,
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)
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