1aa60dada2
Implements three-phase AI assistant feature: - Phase 0: RAG infrastructure with pgvector embeddings, Voyage AI integration, tree chunking service, and semantic search over team's flow library - Phase 1: In-session copilot panel during flow navigation with contextual AI help, current step awareness, and suggested related flows - Phase 2: Standalone AI chat page with persistent conversation history, pin/delete, and configurable retention policies (account-level) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
45 lines
1015 B
Python
45 lines
1015 B
Python
"""Pydantic schemas for the in-session copilot."""
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from typing import Optional, Any
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from uuid import UUID
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from datetime import datetime
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from pydantic import BaseModel, Field
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class SuggestedFlow(BaseModel):
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tree_id: UUID
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tree_name: str
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tree_type: str
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relevance_snippet: str
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class CopilotStartRequest(BaseModel):
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tree_id: UUID
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session_id: Optional[UUID] = None
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current_node_id: Optional[str] = None
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class CopilotStartResponse(BaseModel):
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conversation_id: UUID
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greeting: str
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class CopilotMessageRequest(BaseModel):
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message: str = Field(..., min_length=1, max_length=4000)
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current_node_id: Optional[str] = None
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class CopilotMessageResponse(BaseModel):
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content: str
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suggested_flows: list[SuggestedFlow] = []
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class CopilotConversationResponse(BaseModel):
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id: UUID
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tree_id: UUID
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messages: list[dict[str, Any]]
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current_node_id: Optional[str] = None
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message_count: int
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created_at: datetime
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model_config = {"from_attributes": True}
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