User experiment migration tool

apps/experiments/management/commands/migrate_nonpipeline_to_pipeline_experiments.py

@@ -1,12 +1,9 @@
-from uuid import uuid4
-
 from django.core.management.base import BaseCommand
 from django.db import transaction
 from django.db.models import Q
 
+from apps.experiments.helper import convert_non_pipeline_experiment_to_pipeline
 from apps.experiments.models import Experiment
-from apps.pipelines.models import Pipeline
-from apps.pipelines.nodes.nodes import AssistantNode, LLMResponseWithPrompt
 from apps.teams.models import Flag
 
 
@@ -104,7 +101,7 @@ def handle(self, *args, **options):
         for experiment in experiments_to_convert:
             try:
                 with transaction.atomic():
-                    self._convert_experiment(experiment)
+                    convert_non_pipeline_experiment_to_pipeline(experiment)
                     converted_count += 1
                     self.stdout.write(self.style.SUCCESS(f"Success: {experiment.name}"))
             except Exception as e:
@@ -122,79 +119,8 @@ def _get_experiment_type(self, experiment):
             return "LLM"
         else:
             return "Unknown"
-
-    def _convert_experiment(self, experiment):
-        if experiment.assistant:
-            pipeline = self._create_assistant_pipeline(experiment)
-        elif experiment.llm_provider:
-            pipeline = self._create_llm_pipeline(experiment)
-        else:
-            raise ValueError(f"Unknown experiment type for experiment {experiment.id}")
-
-        experiment.pipeline = pipeline
-        experiment.assistant = None
-        experiment.llm_provider = None
-        experiment.llm_provider_model = None
-
         experiment.save()
 
     def _get_chatbots_flag_team_ids(self):
         chatbots_flag = Flag.objects.get(name="flag_chatbots")
         return list(chatbots_flag.teams.values_list("id", flat=True))
-
-    def _create_pipeline_with_node(self, experiment, node_type, node_label, node_params):
-        """Create a pipeline with start -> custom_node -> end structure."""
-        pipeline_name = f"{experiment.name} Pipeline"
-        middle_node_config = {
-            "id": str(uuid4()),
-            "type": "pipelineNode",
-            "position": {"x": 400, "y": 200},
-            "data": {"type": node_type, "label": node_label, "params": node_params},
-        }
-
-        return Pipeline._create_pipeline_with_nodes(
-            team=experiment.team, name=pipeline_name, middle_nodes_config=middle_node_config
-        )
-
-    def _create_llm_pipeline(self, experiment):
-        """Create a start -> LLMResponseWithPrompt -> end nodes pipeline for an LLM experiment."""
-        llm_params = {
-            "name": "llm",
-            "llm_provider_id": experiment.llm_provider.id,
-            "llm_provider_model_id": experiment.llm_provider_model.id,
-            "llm_temperature": experiment.temperature,
-            "history_type": "global",
-            "history_name": None,
-            "history_mode": "summarize",
-            "user_max_token_limit": experiment.llm_provider_model.max_token_limit,
-            "max_history_length": 10,
-            "source_material_id": experiment.source_material.id if experiment.source_material else None,
-            "prompt": experiment.prompt_text or "",
-            "tools": list(experiment.tools) if experiment.tools else [],
-            "custom_actions": [
-                op.get_model_id(False)
-                for op in experiment.custom_action_operations.select_related("custom_action").all()
-            ],
-            "built_in_tools": [],
-            "tool_config": {},
-        }
-
-        return self._create_pipeline_with_node(
-            experiment=experiment, node_type=LLMResponseWithPrompt.__name__, node_label="LLM", node_params=llm_params
-        )
-
-    def _create_assistant_pipeline(self, experiment):
-        """Createii a start -> AssistantNode -> end nodes pipeline for an assistant experiment."""
-        assistant_params = {
-            "name": "assistant",
-            "assistant_id": str(experiment.assistant.id),
-            "citations_enabled": experiment.citations_enabled,
-            "input_formatter": experiment.input_formatter or "",
-        }
-
-        return self._create_pipeline_with_node(
-            experiment=experiment,
-            node_type=AssistantNode.__name__,
-            node_label="OpenAI Assistant",
-            node_params=assistant_params,
-        )

apps/experiments/tasks.py

@@ -3,7 +3,6 @@
 
 from celery.app import shared_task
 from django.core.files.base import ContentFile
-from django.core.management import call_command
 from django.utils import timezone
 from field_audit.models import AuditAction
 from langchain_core.messages import AIMessage, HumanMessage
@@ -154,18 +153,3 @@ def _convert_prompt_builder_history(messages_history):
         elif message["author"] == "Assistant":
             history.append(AIMessage(content=message["message"]))
     return history
-
-
-@shared_task(bind=True, base=TaskbadgerTask)
-def migrate_experiment_to_pipeline_task(self, experiment_id: int) -> dict:
-    """
-    Migrate a single assistant or llm experiment to pipeline experiment.
-    """
-    try:
-        experiment = Experiment.objects.get(id=experiment_id)
-        call_command("migrate_nonpipeline_to_pipeline_experiments", experiment_id=experiment_id, skip_confirmation=True)
-
-        return {"success": True, "experiment_id": experiment_id, "experiment_name": experiment.name}
-    except Exception as e:
-        logger.exception(f"Failed to migrate experiment {experiment_id}: {e}")
-        raise

apps/experiments/views/experiment.py

@@ -11,7 +11,7 @@
 from celery_progress.backend import Progress
 from django.conf import settings
 from django.contrib import messages
-from django.contrib.auth.decorators import permission_required
+from django.contrib.auth.decorators import login_required, permission_required
 from django.contrib.auth.mixins import PermissionRequiredMixin
 from django.core.exceptions import PermissionDenied, ValidationError
 from django.db import transaction
@@ -87,7 +87,6 @@
     async_create_experiment_version,
     async_export_chat,
     get_response_for_webchat_task,
-    migrate_experiment_to_pipeline_task,
 )
 from apps.experiments.views.prompt import PROMPT_DATA_SESSION_KEY
 from apps.files.forms import get_file_formset
@@ -1504,15 +1503,20 @@ def get_release_status_badge(request, team_slug: str, experiment_id: int):
     return render(request, "experiments/components/unreleased_badge.html", context)
 
 
+@login_required
+@permission_required(("experiments.change_experiment", "pipelines.add_pipeline"))
 def migrate_experiment_view(request, team_slug, experiment_id):
+    from apps.pipelines.helper import convert_non_pipeline_experiment_to_pipeline
+
     experiment = get_object_or_404(Experiment, id=experiment_id, team__slug=team_slug)
     failed_url = reverse(
         "experiments:single_experiment_home",
         kwargs={"team_slug": team_slug, "experiment_id": experiment_id},
     )
     try:
-        task = migrate_experiment_to_pipeline_task.delay(experiment_id)
-        task.get(timeout=60)
+        with transaction.atomic():
+            experiment = Experiment.objects.get(id=experiment_id)
+            convert_non_pipeline_experiment_to_pipeline(experiment)
         messages.success(request, f'Successfully migrated experiment "{experiment.name}" to chatbot!')
         return redirect("chatbots:single_chatbot_home", team_slug=team_slug, experiment_id=experiment_id)
     except Exception as e:

apps/pipelines/helper.py

@@ -35,3 +35,87 @@ def duplicate_pipeline_with_new_ids(pipeline_data):
         edge["target"] = new_target_id
 
     return new_data, old_to_new_node_ids
+
+
+# TODO: function is temporary and can be deleted after the exp -> chatbot transition is complete
+def convert_non_pipeline_experiment_to_pipeline(experiment):
+    if experiment.assistant:
+        pipeline = _create_assistant_pipeline(experiment)
+    elif experiment.llm_provider:
+        pipeline = _create_llm_pipeline(experiment)
+    else:
+        raise ValueError(f"Unknown experiment type for experiment {experiment.id}")
+
+    experiment.pipeline = pipeline
+    experiment.assistant = None
+    experiment.llm_provider = None
+    experiment.llm_provider_model = None
+    experiment.save()
+
+
+# TODO: function is temporary and can be deleted after the exp -> chatbot transition is complete
+def _create_pipeline_with_node(experiment, node_type, node_label, node_params):
+    from .models import Pipeline
+
+    """Create a pipeline with start -> custom_node -> end structure."""
+    pipeline_name = f"{experiment.name} Pipeline"
+    middle_node_config = {
+        "id": str(uuid4()),
+        "type": "pipelineNode",
+        "position": {"x": 400, "y": 200},
+        "data": {"type": node_type, "label": node_label, "params": node_params},
+    }
+
+    return Pipeline._create_pipeline_with_nodes(
+        team=experiment.team, name=pipeline_name, middle_nodes_config=middle_node_config
+    )
+
+
+# TODO: function is temporary and can be deleted after the exp -> chatbot transition is complete
+def _create_llm_pipeline(experiment):
+    from apps.pipelines.nodes.nodes import LLMResponseWithPrompt
+
+    """Create a start -> LLMResponseWithPrompt -> end nodes pipeline for an LLM experiment."""
+    llm_params = {
+        "name": "llm",
+        "llm_provider_id": experiment.llm_provider.id,
+        "llm_provider_model_id": experiment.llm_provider_model.id,
+        "llm_temperature": experiment.temperature,
+        "history_type": "global",
+        "history_name": None,
+        "history_mode": "summarize",
+        "user_max_token_limit": experiment.llm_provider_model.max_token_limit,
+        "max_history_length": 10,
+        "source_material_id": experiment.source_material.id if experiment.source_material else None,
+        "prompt": experiment.prompt_text or "",
+        "tools": list(experiment.tools) if experiment.tools else [],
+        "custom_actions": [
+            op.get_model_id(False) for op in experiment.custom_action_operations.select_related("custom_action").all()
+        ],
+        "built_in_tools": [],
+        "tool_config": {},
+    }
+
+    return _create_pipeline_with_node(
+        experiment=experiment, node_type=LLMResponseWithPrompt.__name__, node_label="LLM", node_params=llm_params
+    )
+
+
+# TODO: function is temporary and can be deleted after the exp -> chatbot transition is complete
+def _create_assistant_pipeline(experiment):
+    from apps.pipelines.nodes.nodes import AssistantNode
+
+    """Create a start -> AssistantNode -> end nodes pipeline for an assistant experiment."""
+    assistant_params = {
+        "name": "assistant",
+        "assistant_id": str(experiment.assistant.id),
+        "citations_enabled": experiment.citations_enabled,
+        "input_formatter": experiment.input_formatter or "",
+    }
+
+    return _create_pipeline_with_node(
+        experiment=experiment,
+        node_type=AssistantNode.__name__,
+        node_label="OpenAI Assistant",
+        node_params=assistant_params,
+    )