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plugin system

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Aljaz Ceru
2025-08-24 17:46:15 +02:00
parent 5fdab97f7f
commit d1c59265d7
132 changed files with 4246 additions and 2007 deletions
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157
backend/modules/workflow/main.py
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@@ -21,6 +21,7 @@ from pydantic import BaseModel, Field
from fastapi import APIRouter, HTTPException, Depends
from sqlalchemy.orm import Session
from sqlalchemy import select
from app.core.security import get_current_user
from app.core.logging import get_logger
from app.services.llm.service import llm_service
from app.services.llm.models import ChatRequest as LLMChatRequest, ChatMessage as LLMChatMessage
@@ -28,6 +29,7 @@ from app.services.llm.exceptions import LLMError, ProviderError, SecurityError
from app.services.base_module import Permission
from app.db.database import SessionLocal
from app.models.workflow import WorkflowDefinition as DBWorkflowDefinition, WorkflowExecution as DBWorkflowExecution
from app.services.workflow_execution_service import WorkflowExecutionService
# Import protocols for type hints and dependency injection
from ..protocols import ChatbotServiceProtocol
@@ -235,33 +237,76 @@ class WorkflowExecution(BaseModel):
class WorkflowEngine:
"""Core workflow execution engine"""
"""Core workflow execution engine with user context tracking"""
def __init__(self, chatbot_service: Optional[ChatbotServiceProtocol] = None):
def __init__(self, chatbot_service: Optional[ChatbotServiceProtocol] = None, execution_service: Optional[WorkflowExecutionService] = None):
self.chatbot_service = chatbot_service
self.execution_service = execution_service
self.executions: Dict[str, WorkflowExecution] = {}
self.workflows: Dict[str, WorkflowDefinition] = {}
async def execute_workflow(self, workflow: WorkflowDefinition,
input_data: Dict[str, Any] = None) -> WorkflowExecution:
"""Execute a workflow definition"""
input_data: Dict[str, Any] = None,
user_context: Optional[Dict[str, Any]] = None) -> WorkflowExecution:
"""Execute a workflow definition with proper user context tracking"""
# Create user context if not provided
if not user_context:
user_context = {"user_id": "system", "username": "System", "session_id": str(uuid.uuid4())}
# Create execution record in database if service is available
db_execution = None
if self.execution_service:
try:
db_execution = await self.execution_service.create_execution_record(
workflow_id=workflow.id,
user_context=user_context,
execution_params=input_data
)
# Start the execution
await self.execution_service.start_execution(
db_execution.id,
workflow_context={"workflow_name": workflow.name}
)
except Exception as e:
logger.error(f"Failed to create database execution record: {e}")
# Create in-memory execution for backward compatibility
execution = WorkflowExecution(
workflow_id=workflow.id,
status=WorkflowStatus.RUNNING,
started_at=datetime.utcnow()
)
# Initialize context
# Use database execution ID if available
if db_execution:
execution.id = db_execution.id
# Initialize context with user information
context = WorkflowContext(
workflow_id=workflow.id,
execution_id=execution.id,
variables={**workflow.variables, **(input_data or {})},
variables={
**workflow.variables,
**(input_data or {}),
# Add user context to variables for step access
"_user_id": user_context.get("user_id", "system"),
"_username": user_context.get("username", "System"),
"_session_id": user_context.get("session_id")
},
results={},
metadata={},
metadata={
"user_context": user_context,
"execution_started_by": user_context.get("username", "System")
},
step_history=[]
)
try:
logger.info(f"Starting workflow execution {execution.id} for workflow {workflow.name} by user {user_context.get('username', 'System')}")
# Execute steps
await self._execute_steps(workflow.steps, context)
@@ -269,11 +314,31 @@ class WorkflowEngine:
execution.results = context.results
execution.completed_at = datetime.utcnow()
# Update database execution record if available
if self.execution_service and db_execution:
await self.execution_service.complete_execution(
db_execution.id,
context.results,
context.step_history
)
logger.info(f"Completed workflow execution {execution.id} successfully")
except Exception as e:
logger.error(f"Workflow execution failed: {e}")
error_message = str(e)
logger.error(f"Workflow execution {execution.id} failed: {error_message}")
execution.status = WorkflowStatus.FAILED
execution.error = str(e)
execution.error = error_message
execution.completed_at = datetime.utcnow()
# Update database execution record if available
if self.execution_service and db_execution:
await self.execution_service.fail_execution(
db_execution.id,
error_message,
context.step_history
)
self.executions[execution.id] = execution
return execution
@@ -339,7 +404,7 @@ class WorkflowEngine:
raise
async def _execute_llm_step(self, step: WorkflowStep, context: WorkflowContext):
"""Execute an LLM call step"""
"""Execute an LLM call step with proper user context"""
llm_step = LLMCallStep(**step.dict())
# Template message content with context variables
@@ -348,11 +413,15 @@ class WorkflowEngine:
# Convert messages to LLM service format
llm_messages = [LLMChatMessage(role=msg["role"], content=msg["content"]) for msg in messages]
# Create LLM service request
# Get user context from workflow metadata
user_context = context.metadata.get("user_context", {})
user_id = user_context.get("user_id", "system")
# Create LLM service request with proper user context
llm_request = LLMChatRequest(
model=llm_step.model,
messages=llm_messages,
user_id="workflow_user",
user_id=str(user_id), # Use actual user ID from context
api_key_id=0, # Workflow module uses internal service
**{k: v for k, v in llm_step.parameters.items() if k in ['temperature', 'max_tokens', 'top_p', 'frequency_penalty', 'presence_penalty', 'stop']}
)
@@ -365,7 +434,7 @@ class WorkflowEngine:
context.variables[llm_step.output_variable] = result
context.results[step.id] = result
logger.info(f"LLM step {step.id} completed")
logger.info(f"LLM step {step.id} completed for user {user_context.get('username', user_id)}")
async def _execute_conditional_step(self, step: WorkflowStep, context: WorkflowContext):
"""Execute a conditional step"""
@@ -473,12 +542,14 @@ class WorkflowEngine:
context=chatbot_context
)
# Make the chatbot call using the service protocol
# NOTE: DB session dependency should be injected via WorkflowEngine constructor
# for proper chatbot database operations (conversation persistence, etc.)
# Make the chatbot call using the service protocol with proper user context
# Get user context from workflow metadata
user_context = context.metadata.get("user_context", {})
user_id = user_context.get("user_id", "system")
response = await self.chatbot_service.chat_completion(
request=chat_request,
user_id="workflow_system", # Identifier for workflow-initiated chats
user_id=str(user_id), # Use actual user ID from context
db=None # Database session needed for conversation persistence
)
@@ -647,7 +718,7 @@ class WorkflowEngine:
llm_request = LLMChatRequest(
model=step.model,
messages=llm_messages,
user_id="workflow_system",
user_id=str(variables.get("_user_id", "system")),
api_key_id=0,
temperature=step.temperature,
max_tokens=step.max_tokens
@@ -674,7 +745,7 @@ class WorkflowEngine:
response = await self.litellm_client.create_chat_completion(
model=step.model,
messages=messages,
user_id="workflow_system",
user_id=str(variables.get("_user_id", "system")),
api_key_id="workflow",
temperature=step.temperature,
max_tokens=step.max_tokens
@@ -708,7 +779,7 @@ class WorkflowEngine:
llm_request = LLMChatRequest(
model=step.model,
messages=llm_messages,
user_id="workflow_system",
user_id=str(variables.get("_user_id", "system")),
api_key_id=0,
temperature=step.temperature,
max_tokens=step.max_tokens
@@ -731,7 +802,7 @@ class WorkflowEngine:
llm_request = LLMChatRequest(
model=step.model,
messages=llm_messages,
user_id="workflow_system",
user_id=str(variables.get("_user_id", "system")),
api_key_id=0,
temperature=step.temperature,
max_tokens=step.max_tokens
@@ -937,8 +1008,19 @@ class WorkflowModule:
if config:
self.config = config
# Initialize the workflow engine
self.engine = WorkflowEngine(LiteLLMClient(), chatbot_service=self.chatbot_service)
# Initialize the workflow engine with execution service
# Create execution service if database is available
execution_service = None
try:
from app.db.database import async_session_factory
# Create an async session for the execution service
async_db = async_session_factory()
execution_service = WorkflowExecutionService(async_db)
logger.info("Workflow execution service initialized successfully")
except Exception as e:
logger.warning(f"Failed to initialize execution service: {e}")
self.engine = WorkflowEngine(chatbot_service=self.chatbot_service, execution_service=execution_service)
self.setup_routes()
self.initialized = True
@@ -948,19 +1030,36 @@ class WorkflowModule:
"""Setup workflow API routes"""
@self.router.post("/execute")
async def execute_workflow(workflow_def: WorkflowDefinition,
input_data: Optional[Dict[str, Any]] = None):
"""Execute a workflow"""
async def execute_workflow(
workflow_def: WorkflowDefinition,
input_data: Optional[Dict[str, Any]] = None,
current_user: Dict[str, Any] = Depends(get_current_user)
):
"""Execute a workflow with proper user context"""
if not self.initialized or not self.engine:
raise HTTPException(status_code=503, detail="Workflow module not initialized")
try:
execution = await self.engine.execute_workflow(workflow_def, input_data)
# Create user context from authenticated user
user_context = {
"user_id": str(current_user.get("id", "system")),
"username": current_user.get("username") or current_user.get("email", "Unknown User"),
"session_id": str(uuid.uuid4())
}
# Execute workflow with user context
execution = await self.engine.execute_workflow(
workflow_def,
input_data,
user_context=user_context
)
return {
"execution_id": execution.id,
"status": execution.status,
"results": execution.results if execution.status == WorkflowStatus.COMPLETED else None,
"error": execution.error
"error": execution.error,
"executed_by": user_context.get("username", "Unknown")
}
except Exception as e:
logger.error(f"Workflow execution failed: {e}")
@@ -1409,7 +1508,7 @@ class WorkflowModule:
variables=variables,
workflow_metadata=workflow_metadata,
timeout=timeout,
created_by="system", # TODO: Get from user context
created_by="system", # Note: This method needs user context parameter to track creator properly
is_active=True
)