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

apps/tenant-backend/app/models/conversation.py

@@ -0,0 +1,148 @@
+"""
+Conversation Model for GT 2.0 Tenant Backend - Service-Based Architecture
+
+Pydantic models for conversation entities using the PostgreSQL + PGVector backend.
+Stores conversation metadata and settings for AI chat sessions.
+Perfect tenant isolation - each tenant has separate conversation data.
+"""
+
+from datetime import datetime
+from typing import List, Optional, Dict, Any
+from enum import Enum
+
+from pydantic import Field, ConfigDict
+from app.models.base import BaseServiceModel, BaseCreateModel, BaseUpdateModel, BaseResponseModel
+
+
+class ConversationStatus(str, Enum):
+    """Conversation status enumeration"""
+    ACTIVE = "active"
+    ARCHIVED = "archived"
+    DELETED = "deleted"
+
+
+class Conversation(BaseServiceModel):
+    """
+    Conversation model for GT 2.0 service-based architecture.
+    
+    Represents a chat session with an AI agent including metadata,
+    configuration, and usage statistics.
+    """
+    
+    # Core conversation properties
+    title: str = Field(..., min_length=1, max_length=200, description="Conversation title")
+    agent_id: Optional[str] = Field(None, description="Associated agent ID")
+    
+    # User information  
+    created_by: str = Field(..., description="User email or ID who created this")
+    user_name: Optional[str] = Field(None, max_length=100, description="User display name")
+    
+    # Configuration
+    system_prompt: Optional[str] = Field(None, description="Custom system prompt override")
+    model_id: str = Field(default="groq:llama3-70b-8192", description="AI model identifier")
+    configuration: Dict[str, Any] = Field(default_factory=dict, description="Model parameters and settings")
+    
+    # Status and metadata
+    status: ConversationStatus = Field(default=ConversationStatus.ACTIVE, description="Conversation status")
+    tags: List[str] = Field(default_factory=list, description="Conversation tags")
+    
+    # Statistics
+    message_count: int = Field(default=0, description="Number of messages in conversation")
+    total_tokens_used: int = Field(default=0, description="Total tokens used")
+    total_cost_cents: int = Field(default=0, description="Total cost in cents")
+    
+    # Timestamps
+    last_activity_at: Optional[datetime] = Field(None, description="Last activity timestamp")
+    
+    # Model configuration
+    model_config = ConfigDict(
+        protected_namespaces=(),
+        json_encoders={
+            datetime: lambda v: v.isoformat() if v else None
+        }
+    )
+    
+    @classmethod
+    def get_table_name(cls) -> str:
+        """Get the database table name"""
+        return "conversations"
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "Conversation":
+        """Create from dictionary"""
+        return cls(
+            agent_id=data.get("agent_id"),
+            title=data.get("title", ""),
+            system_prompt=data.get("system_prompt"),
+            model_id=data.get("model_id", "groq:llama3-70b-8192"),
+            created_by=data.get("created_by", ""),
+            user_name=data.get("user_name"),
+            configuration=data.get("configuration", {}),
+            tags=data.get("tags", []),
+        )
+    
+    def update_statistics(self, message_count: int, tokens_used: int, cost_cents: int) -> None:
+        """Update conversation statistics"""
+        self.message_count = message_count
+        self.total_tokens_used = tokens_used
+        self.total_cost_cents = cost_cents
+        self.last_activity_at = datetime.utcnow()
+        self.update_timestamp()
+    
+    def archive(self) -> None:
+        """Archive this conversation"""
+        self.status = ConversationStatus.ARCHIVED
+        self.update_timestamp()
+    
+    def delete(self) -> None:
+        """Mark conversation as deleted"""
+        self.status = ConversationStatus.DELETED
+        self.update_timestamp()
+
+
+class ConversationCreate(BaseCreateModel):
+    """Model for creating new conversations"""
+    title: str = Field(..., min_length=1, max_length=200)
+    agent_id: Optional[str] = None
+    created_by: str
+    user_name: Optional[str] = Field(None, max_length=100)
+    system_prompt: Optional[str] = None
+    model_id: str = Field(default="groq:llama3-70b-8192")
+    configuration: Dict[str, Any] = Field(default_factory=dict)
+    tags: List[str] = Field(default_factory=list)
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class ConversationUpdate(BaseUpdateModel):
+    """Model for updating conversations"""
+    title: Optional[str] = Field(None, min_length=1, max_length=200)
+    system_prompt: Optional[str] = None
+    model_id: Optional[str] = None
+    configuration: Optional[Dict[str, Any]] = None
+    status: Optional[ConversationStatus] = None
+    tags: Optional[List[str]] = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class ConversationResponse(BaseResponseModel):
+    """Model for conversation API responses"""
+    id: str
+    title: str
+    agent_id: Optional[str]
+    created_by: str
+    user_name: Optional[str]
+    system_prompt: Optional[str]
+    model_id: str
+    configuration: Dict[str, Any]
+    status: ConversationStatus
+    tags: List[str]
+    message_count: int
+    total_tokens_used: int
+    total_cost_cents: int
+    last_activity_at: Optional[datetime]
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(protected_namespaces=())