310491a557
- 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
81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
"""
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Conversation schemas for GT 2.0 Tenant Backend
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Pydantic models for conversation-related API request/response validation.
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"""
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from pydantic import BaseModel, Field, ConfigDict
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from typing import List, Optional, Dict, Any
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from datetime import datetime
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class ConversationCreate(BaseModel):
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"""Request to create a new conversation"""
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agent_id: str = Field(..., description="Agent UUID to chat with")
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title: Optional[str] = Field(None, description="Conversation title")
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initial_message: Optional[str] = Field(None, description="First message to send")
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class ConversationUpdate(BaseModel):
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"""Request to update a conversation"""
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title: Optional[str] = Field(None, description="New conversation title")
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system_prompt: Optional[str] = Field(None, description="Updated system prompt")
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class MessageCreate(BaseModel):
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"""Request to send a message"""
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content: str = Field(..., description="Message content")
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context_sources: Optional[List[str]] = Field(None, description="Context source IDs")
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metadata: Optional[Dict[str, Any]] = Field(None, description="Additional message metadata")
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class MessageResponse(BaseModel):
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"""Message response"""
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id: Optional[str] = Field(None, description="Message ID")
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message_id: Optional[str] = Field(None, description="Message ID (alternative)")
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content: Optional[str] = Field(None, description="Message content")
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role: Optional[str] = Field(None, description="Message role")
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tokens_used: Optional[int] = Field(None, description="Tokens consumed")
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model_used: Optional[str] = Field(None, description="Model used for generation")
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context_sources: Optional[List[str]] = Field(None, description="RAG context source documents")
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created_at: Optional[datetime] = Field(None, description="Creation timestamp")
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stream: Optional[bool] = Field(None, description="Whether response is streamed")
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stream_endpoint: Optional[str] = Field(None, description="Stream endpoint URL")
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model_config = ConfigDict(from_attributes=True, protected_namespaces=())
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class MessageListResponse(BaseModel):
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"""Response for listing messages"""
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messages: List[MessageResponse]
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conversation_id: int
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total: int
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class ConversationResponse(BaseModel):
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"""Conversation response"""
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id: int = Field(..., description="Conversation ID")
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title: str = Field(..., description="Conversation title")
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agent_id: str = Field(..., description="Agent ID")
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model_id: str = Field(..., description="Model identifier")
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system_prompt: Optional[str] = Field(None, description="System prompt")
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message_count: int = Field(0, description="Total message count")
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total_tokens: int = Field(0, description="Total tokens used")
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created_at: datetime = Field(..., description="Creation timestamp")
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updated_at: datetime = Field(..., description="Last update timestamp")
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messages: Optional[List[MessageResponse]] = Field(None, description="Conversation messages")
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model_config = ConfigDict(from_attributes=True, protected_namespaces=())
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class ConversationWithUnread(ConversationResponse):
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"""Conversation response with unread message count"""
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unread_count: int = Field(0, description="Number of unread messages")
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class ConversationListResponse(BaseModel):
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"""Response for listing conversations"""
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conversations: List[ConversationResponse]
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total: int
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limit: int
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offset: int |