GT AI OS Community v2.0.33 - Add NVIDIA NIM and Nemotron agents
- Updated python_coding_microproject.csv to use NVIDIA NIM Kimi K2 - Updated kali_linux_shell_simulator.csv to use NVIDIA NIM Kimi K2 - Made more general-purpose (flexible targets, expanded tools) - Added nemotron-mini-agent.csv for fast local inference via Ollama - Added nemotron-agent.csv for advanced reasoning via Ollama - Added wiki page: Projects for NVIDIA NIMs and Nemotron
This commit is contained in:
HackWeasel
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apps/tenant-backend/app/models/workflow.py
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603
apps/tenant-backend/app/models/workflow.py
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"""
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Workflow Models for GT 2.0 Tenant Backend - Service-Based Architecture
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Pydantic models for workflow entities using the PostgreSQL + PGVector backend.
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Stores workflow definitions, executions, triggers, and chat sessions.
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Perfect tenant isolation - each tenant has separate workflow data.
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"""
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from datetime import datetime
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from typing import List, Optional, Dict, Any
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from enum import Enum
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from pydantic import Field, ConfigDict
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from app.models.base import BaseServiceModel, BaseCreateModel, BaseUpdateModel, BaseResponseModel
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class WorkflowStatus(str, Enum):
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"""Workflow status enumeration"""
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DRAFT = "draft"
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ACTIVE = "active"
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PAUSED = "paused"
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ARCHIVED = "archived"
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class TriggerType(str, Enum):
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"""Trigger type enumeration"""
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MANUAL = "manual"
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WEBHOOK = "webhook"
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CRON = "cron"
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EVENT = "event"
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API = "api"
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class InteractionMode(str, Enum):
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"""Interaction mode enumeration"""
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CHAT = "chat"
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BUTTON = "button"
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FORM = "form"
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DASHBOARD = "dashboard"
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API = "api"
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class ExecutionStatus(str, Enum):
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"""Execution status enumeration"""
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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CANCELLED = "cancelled"
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class Workflow(BaseServiceModel):
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"""
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Workflow model for GT 2.0 service-based architecture.
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Represents an agentic workflow with nodes, triggers, and execution logic.
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Supports chat interfaces, form inputs, API endpoints, and dashboard views.
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"""
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# Basic workflow properties
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tenant_id: str = Field(..., description="Tenant domain identifier")
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user_id: str = Field(..., description="User who owns this workflow")
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name: str = Field(..., min_length=1, max_length=200, description="Workflow name")
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description: Optional[str] = Field(None, max_length=1000, description="Workflow description")
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# Workflow definition as JSON structure
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definition: Dict[str, Any] = Field(..., description="Nodes, edges, and configuration")
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# Triggers and interaction modes
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triggers: List[Dict[str, Any]] = Field(default_factory=list, description="Webhook, cron, event triggers")
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interaction_modes: List[InteractionMode] = Field(default_factory=list, description="UI interaction modes")
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# Resource references - ensuring user owns all resources
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agent_ids: List[str] = Field(default_factory=list, description="Referenced agents")
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api_key_ids: List[str] = Field(default_factory=list, description="Referenced API keys")
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webhook_ids: List[str] = Field(default_factory=list, description="Referenced webhooks")
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dataset_ids: List[str] = Field(default_factory=list, description="Referenced datasets")
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integration_ids: List[str] = Field(default_factory=list, description="Referenced integrations")
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# Workflow configuration
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config: Dict[str, Any] = Field(default_factory=dict, description="Runtime configuration")
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timeout_seconds: int = Field(default=300, ge=1, le=3600, description="Execution timeout (5 min default)")
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max_retries: int = Field(default=3, ge=0, le=10, description="Maximum retry attempts")
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# Status and metadata
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status: WorkflowStatus = Field(default=WorkflowStatus.DRAFT, description="Workflow status")
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execution_count: int = Field(default=0, description="Total execution count")
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last_executed: Optional[datetime] = Field(None, description="Last execution timestamp")
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# Analytics
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total_tokens_used: int = Field(default=0, description="Total tokens consumed")
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total_cost_cents: int = Field(default=0, description="Total cost in cents")
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average_execution_time_ms: Optional[int] = Field(None, description="Average execution time")
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# Model configuration
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model_config = ConfigDict(
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protected_namespaces=(),
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json_encoders={
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datetime: lambda v: v.isoformat() if v else None
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}
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)
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@classmethod
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def get_table_name(cls) -> str:
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"""Get the database table name"""
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return "workflows"
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def activate(self) -> None:
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"""Activate the workflow"""
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self.status = WorkflowStatus.ACTIVE
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self.update_timestamp()
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def pause(self) -> None:
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"""Pause the workflow"""
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self.status = WorkflowStatus.PAUSED
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self.update_timestamp()
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def archive(self) -> None:
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"""Archive the workflow"""
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self.status = WorkflowStatus.ARCHIVED
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self.update_timestamp()
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def update_execution_stats(self, tokens_used: int, cost_cents: int, execution_time_ms: int) -> None:
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"""Update execution statistics"""
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self.execution_count += 1
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self.total_tokens_used += tokens_used
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self.total_cost_cents += cost_cents
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self.last_executed = datetime.utcnow()
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# Update rolling average execution time
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if self.average_execution_time_ms is None:
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self.average_execution_time_ms = execution_time_ms
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else:
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# Simple moving average
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self.average_execution_time_ms = int(
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(self.average_execution_time_ms * (self.execution_count - 1) + execution_time_ms) / self.execution_count
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)
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self.update_timestamp()
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class WorkflowExecution(BaseServiceModel):
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"""
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Workflow execution model for tracking individual workflow runs.
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Stores execution state, progress, timing, and resource usage.
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"""
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# Core execution properties
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workflow_id: str = Field(..., description="Parent workflow ID")
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user_id: str = Field(..., description="User who triggered execution")
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tenant_id: str = Field(..., description="Tenant domain identifier")
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# Execution state
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status: ExecutionStatus = Field(default=ExecutionStatus.PENDING, description="Execution status")
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current_node_id: Optional[str] = Field(None, description="Currently executing node")
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progress_percentage: int = Field(default=0, ge=0, le=100, description="Execution progress")
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# Data and context
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input_data: Dict[str, Any] = Field(default_factory=dict, description="Execution input data")
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output_data: Dict[str, Any] = Field(default_factory=dict, description="Execution output data")
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execution_trace: List[Dict[str, Any]] = Field(default_factory=list, description="Step-by-step log")
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error_details: Optional[str] = Field(None, description="Error details if failed")
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# Timing and performance
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started_at: datetime = Field(default_factory=datetime.utcnow, description="Execution start time")
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completed_at: Optional[datetime] = Field(None, description="Execution completion time")
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duration_ms: Optional[int] = Field(None, description="Execution duration in milliseconds")
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# Resource usage
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tokens_used: int = Field(default=0, description="Tokens consumed")
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cost_cents: int = Field(default=0, description="Cost in cents")
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tool_calls_count: int = Field(default=0, description="Number of tool calls made")
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# Trigger information
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trigger_type: Optional[TriggerType] = Field(None, description="How execution was triggered")
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trigger_data: Dict[str, Any] = Field(default_factory=dict, description="Trigger-specific data")
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trigger_source: Optional[str] = Field(None, description="Source identifier for trigger")
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# Session information for chat mode
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session_id: Optional[str] = Field(None, description="Chat session ID if applicable")
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interaction_mode: Optional[InteractionMode] = Field(None, description="User interaction mode")
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# Model configuration
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model_config = ConfigDict(
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protected_namespaces=(),
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json_encoders={
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datetime: lambda v: v.isoformat() if v else None
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}
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)
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@classmethod
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def get_table_name(cls) -> str:
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"""Get the database table name"""
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return "workflow_executions"
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def mark_running(self, current_node_id: str) -> None:
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"""Mark execution as running"""
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self.status = ExecutionStatus.RUNNING
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self.current_node_id = current_node_id
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self.update_timestamp()
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def mark_completed(self, output_data: Dict[str, Any]) -> None:
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"""Mark execution as completed"""
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self.status = ExecutionStatus.COMPLETED
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self.completed_at = datetime.utcnow()
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self.output_data = output_data
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self.progress_percentage = 100
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if self.started_at:
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self.duration_ms = int((self.completed_at - self.started_at).total_seconds() * 1000)
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self.update_timestamp()
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def mark_failed(self, error_details: str) -> None:
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"""Mark execution as failed"""
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self.status = ExecutionStatus.FAILED
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self.completed_at = datetime.utcnow()
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self.error_details = error_details
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if self.started_at:
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self.duration_ms = int((self.completed_at - self.started_at).total_seconds() * 1000)
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self.update_timestamp()
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def add_trace_entry(self, node_id: str, action: str, data: Dict[str, Any]) -> None:
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"""Add entry to execution trace"""
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trace_entry = {
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"timestamp": datetime.utcnow().isoformat(),
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"node_id": node_id,
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"action": action,
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"data": data
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}
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self.execution_trace.append(trace_entry)
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class WorkflowTrigger(BaseServiceModel):
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"""
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Workflow trigger model for automated workflow execution.
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Supports webhook, cron, event, and API triggers.
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"""
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# Core trigger properties
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workflow_id: str = Field(..., description="Parent workflow ID")
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user_id: str = Field(..., description="User who owns this trigger")
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tenant_id: str = Field(..., description="Tenant domain identifier")
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# Trigger configuration
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trigger_type: TriggerType = Field(..., description="Type of trigger")
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trigger_config: Dict[str, Any] = Field(..., description="Trigger-specific configuration")
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# Webhook-specific fields
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webhook_url: Optional[str] = Field(None, description="Generated webhook URL")
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webhook_secret: Optional[str] = Field(None, max_length=128, description="Webhook signature secret")
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# Cron-specific fields
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cron_schedule: Optional[str] = Field(None, max_length=100, description="Cron expression")
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timezone: str = Field(default="UTC", max_length=50, description="Timezone for cron schedule")
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# Event-specific fields
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event_source: Optional[str] = Field(None, max_length=100, description="Event source system")
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event_filters: Dict[str, Any] = Field(default_factory=dict, description="Event filtering criteria")
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# Status and metadata
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is_active: bool = Field(default=True, description="Whether trigger is active")
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trigger_count: int = Field(default=0, description="Number of times triggered")
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last_triggered: Optional[datetime] = Field(None, description="Last trigger timestamp")
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# Model configuration
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model_config = ConfigDict(
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protected_namespaces=(),
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json_encoders={
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datetime: lambda v: v.isoformat() if v else None
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}
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)
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@classmethod
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def get_table_name(cls) -> str:
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"""Get the database table name"""
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return "workflow_triggers"
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def activate(self) -> None:
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"""Activate the trigger"""
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self.is_active = True
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self.update_timestamp()
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def deactivate(self) -> None:
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"""Deactivate the trigger"""
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self.is_active = False
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self.update_timestamp()
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def record_trigger(self) -> None:
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"""Record a trigger event"""
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self.trigger_count += 1
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self.last_triggered = datetime.utcnow()
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self.update_timestamp()
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class WorkflowSession(BaseServiceModel):
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"""
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Workflow session model for chat-based workflow interactions.
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Manages conversational state for workflow chat interfaces.
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"""
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# Core session properties
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workflow_id: str = Field(..., description="Parent workflow ID")
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user_id: str = Field(..., description="User participating in session")
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tenant_id: str = Field(..., description="Tenant domain identifier")
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# Session configuration
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session_type: str = Field(default="chat", max_length=50, description="Session type")
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session_state: Dict[str, Any] = Field(default_factory=dict, description="Current conversation state")
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# Chat history
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message_count: int = Field(default=0, description="Number of messages in session")
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last_message_at: Optional[datetime] = Field(None, description="Last message timestamp")
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# Status
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is_active: bool = Field(default=True, description="Whether session is active")
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expires_at: Optional[datetime] = Field(None, description="Session expiration time")
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# Model configuration
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model_config = ConfigDict(
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protected_namespaces=(),
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json_encoders={
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datetime: lambda v: v.isoformat() if v else None
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}
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)
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@classmethod
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def get_table_name(cls) -> str:
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"""Get the database table name"""
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return "workflow_sessions"
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def add_message(self) -> None:
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"""Record a new message in the session"""
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self.message_count += 1
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self.last_message_at = datetime.utcnow()
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self.update_timestamp()
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def close_session(self) -> None:
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"""Close the session"""
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self.is_active = False
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self.update_timestamp()
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class WorkflowMessage(BaseServiceModel):
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"""
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Workflow message model for chat session messages.
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Stores individual messages within workflow chat sessions.
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"""
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# Core message properties
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session_id: str = Field(..., description="Parent session ID")
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workflow_id: str = Field(..., description="Parent workflow ID")
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execution_id: Optional[str] = Field(None, description="Associated execution ID")
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user_id: str = Field(..., description="User who sent/received message")
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tenant_id: str = Field(..., description="Tenant domain identifier")
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# Message content
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role: str = Field(..., max_length=20, description="Message role (user, agent, system)")
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content: str = Field(..., description="Message content")
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message_type: str = Field(default="text", max_length=50, description="Message type")
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# Agent information for agent messages
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agent_id: Optional[str] = Field(None, description="Agent that generated this message")
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confidence_score: Optional[int] = Field(None, ge=0, le=100, description="Agent confidence (0-100)")
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# Additional data
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message_metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional message data")
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tokens_used: int = Field(default=0, description="Tokens consumed for this message")
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# Model configuration
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model_config = ConfigDict(
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protected_namespaces=(),
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json_encoders={
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datetime: lambda v: v.isoformat() if v else None
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}
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)
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@classmethod
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def get_table_name(cls) -> str:
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"""Get the database table name"""
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return "workflow_messages"
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# Create/Update/Response models for each entity
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class WorkflowCreate(BaseCreateModel):
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"""Model for creating new workflows"""
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tenant_id: str
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user_id: str
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name: str = Field(..., min_length=1, max_length=200)
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description: Optional[str] = Field(None, max_length=1000)
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definition: Dict[str, Any]
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triggers: List[Dict[str, Any]] = Field(default_factory=list)
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interaction_modes: List[InteractionMode] = Field(default_factory=list)
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agent_ids: List[str] = Field(default_factory=list)
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api_key_ids: List[str] = Field(default_factory=list)
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webhook_ids: List[str] = Field(default_factory=list)
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dataset_ids: List[str] = Field(default_factory=list)
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integration_ids: List[str] = Field(default_factory=list)
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config: Dict[str, Any] = Field(default_factory=dict)
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timeout_seconds: int = Field(default=300, ge=1, le=3600)
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max_retries: int = Field(default=3, ge=0, le=10)
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class WorkflowUpdate(BaseUpdateModel):
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"""Model for updating workflows"""
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name: Optional[str] = Field(None, min_length=1, max_length=200)
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description: Optional[str] = Field(None, max_length=1000)
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definition: Optional[Dict[str, Any]] = None
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triggers: Optional[List[Dict[str, Any]]] = None
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interaction_modes: Optional[List[InteractionMode]] = None
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config: Optional[Dict[str, Any]] = None
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timeout_seconds: Optional[int] = Field(None, ge=1, le=3600)
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max_retries: Optional[int] = Field(None, ge=0, le=10)
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status: Optional[WorkflowStatus] = None
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class WorkflowResponse(BaseResponseModel):
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"""Model for workflow API responses"""
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id: str
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tenant_id: str
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user_id: str
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name: str
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description: Optional[str]
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definition: Dict[str, Any]
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triggers: List[Dict[str, Any]]
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interaction_modes: List[InteractionMode]
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agent_ids: List[str]
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api_key_ids: List[str]
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webhook_ids: List[str]
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dataset_ids: List[str]
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integration_ids: List[str]
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config: Dict[str, Any]
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timeout_seconds: int
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max_retries: int
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status: WorkflowStatus
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execution_count: int
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last_executed: Optional[datetime]
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total_tokens_used: int
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total_cost_cents: int
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average_execution_time_ms: Optional[int]
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created_at: datetime
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updated_at: datetime
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class WorkflowExecutionCreate(BaseCreateModel):
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"""Model for creating new workflow executions"""
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workflow_id: str
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user_id: str
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tenant_id: str
|
||||
input_data: Dict[str, Any] = Field(default_factory=dict)
|
||||
trigger_type: Optional[TriggerType] = None
|
||||
trigger_data: Dict[str, Any] = Field(default_factory=dict)
|
||||
trigger_source: Optional[str] = None
|
||||
session_id: Optional[str] = None
|
||||
interaction_mode: Optional[InteractionMode] = None
|
||||
|
||||
|
||||
class WorkflowExecutionUpdate(BaseUpdateModel):
|
||||
"""Model for updating workflow executions"""
|
||||
status: Optional[ExecutionStatus] = None
|
||||
current_node_id: Optional[str] = None
|
||||
progress_percentage: Optional[int] = Field(None, ge=0, le=100)
|
||||
output_data: Optional[Dict[str, Any]] = None
|
||||
error_details: Optional[str] = None
|
||||
completed_at: Optional[datetime] = None
|
||||
tokens_used: Optional[int] = Field(None, ge=0)
|
||||
cost_cents: Optional[int] = Field(None, ge=0)
|
||||
tool_calls_count: Optional[int] = Field(None, ge=0)
|
||||
|
||||
|
||||
class WorkflowExecutionResponse(BaseResponseModel):
|
||||
"""Model for workflow execution API responses"""
|
||||
id: str
|
||||
workflow_id: str
|
||||
user_id: str
|
||||
tenant_id: str
|
||||
status: ExecutionStatus
|
||||
current_node_id: Optional[str]
|
||||
progress_percentage: int
|
||||
input_data: Dict[str, Any]
|
||||
output_data: Dict[str, Any]
|
||||
execution_trace: List[Dict[str, Any]]
|
||||
error_details: Optional[str]
|
||||
started_at: datetime
|
||||
completed_at: Optional[datetime]
|
||||
duration_ms: Optional[int]
|
||||
tokens_used: int
|
||||
cost_cents: int
|
||||
tool_calls_count: int
|
||||
trigger_type: Optional[TriggerType]
|
||||
trigger_data: Dict[str, Any]
|
||||
trigger_source: Optional[str]
|
||||
session_id: Optional[str]
|
||||
interaction_mode: Optional[InteractionMode]
|
||||
created_at: datetime
|
||||
updated_at: datetime
|
||||
|
||||
|
||||
# Node type definitions for workflow canvas
|
||||
WORKFLOW_NODE_TYPES = {
|
||||
"agent": {
|
||||
"name": "Agent",
|
||||
"description": "Execute an AI Agent with personality",
|
||||
"inputs": ["text", "context"],
|
||||
"outputs": ["response", "confidence"],
|
||||
"config_schema": {
|
||||
"agent_id": {"type": "string", "required": True},
|
||||
"confidence_threshold": {"type": "integer", "default": 70},
|
||||
"max_tokens": {"type": "integer", "default": 2000},
|
||||
"temperature": {"type": "number", "default": 0.7}
|
||||
}
|
||||
},
|
||||
"trigger": {
|
||||
"name": "Trigger",
|
||||
"description": "Start workflow execution",
|
||||
"inputs": [],
|
||||
"outputs": ["trigger_data"],
|
||||
"subtypes": ["webhook", "cron", "event", "manual", "api"],
|
||||
"config_schema": {
|
||||
"trigger_type": {"type": "string", "required": True}
|
||||
}
|
||||
},
|
||||
"integration": {
|
||||
"name": "Integration",
|
||||
"description": "Connect to external services",
|
||||
"inputs": ["data"],
|
||||
"outputs": ["response"],
|
||||
"subtypes": ["api", "database", "storage", "webhook"],
|
||||
"config_schema": {
|
||||
"integration_type": {"type": "string", "required": True},
|
||||
"api_key_id": {"type": "string"},
|
||||
"endpoint_url": {"type": "string"},
|
||||
"method": {"type": "string", "default": "GET"}
|
||||
}
|
||||
},
|
||||
"logic": {
|
||||
"name": "Logic",
|
||||
"description": "Control flow and data transformation",
|
||||
"inputs": ["data"],
|
||||
"outputs": ["result"],
|
||||
"subtypes": ["decision", "loop", "transform", "aggregate", "filter"],
|
||||
"config_schema": {
|
||||
"logic_type": {"type": "string", "required": True}
|
||||
}
|
||||
},
|
||||
"output": {
|
||||
"name": "Output",
|
||||
"description": "Send results to external systems",
|
||||
"inputs": ["data"],
|
||||
"outputs": [],
|
||||
"subtypes": ["webhook", "api", "email", "storage", "notification"],
|
||||
"config_schema": {
|
||||
"output_type": {"type": "string", "required": True}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# Interaction mode configurations
|
||||
INTERACTION_MODE_CONFIGS = {
|
||||
"chat": {
|
||||
"name": "Chat Interface",
|
||||
"description": "Conversational interaction with workflow",
|
||||
"supports_streaming": True,
|
||||
"supports_history": True,
|
||||
"ui_components": ["chat_input", "message_history", "agent_avatars"]
|
||||
},
|
||||
"button": {
|
||||
"name": "Button Trigger",
|
||||
"description": "Simple one-click workflow execution",
|
||||
"supports_streaming": False,
|
||||
"supports_history": False,
|
||||
"ui_components": ["trigger_button", "progress_indicator", "result_display"]
|
||||
},
|
||||
"form": {
|
||||
"name": "Form Input",
|
||||
"description": "Structured input with validation",
|
||||
"supports_streaming": False,
|
||||
"supports_history": True,
|
||||
"ui_components": ["dynamic_form", "validation", "submit_button"]
|
||||
},
|
||||
"dashboard": {
|
||||
"name": "Dashboard View",
|
||||
"description": "Overview of workflow status and metrics",
|
||||
"supports_streaming": True,
|
||||
"supports_history": True,
|
||||
"ui_components": ["metrics_cards", "execution_history", "status_indicators"]
|
||||
},
|
||||
"api": {
|
||||
"name": "API Endpoint",
|
||||
"description": "Programmatic access to workflow",
|
||||
"supports_streaming": True,
|
||||
"supports_history": False,
|
||||
"ui_components": []
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user