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>
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
"""Standalone AI assistant chat model.
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Persistent conversation history for general IT questions with RAG context.
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"""
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import uuid
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from datetime import datetime, timezone
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from typing import Optional, Any
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from sqlalchemy import String, DateTime, ForeignKey, Integer, Boolean
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy.dialects.postgresql import UUID, JSONB
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from app.core.database import Base
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class AssistantChat(Base):
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__tablename__ = "assistant_chats"
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id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
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)
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user_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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)
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account_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("accounts.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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)
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title: Mapped[str] = mapped_column(
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String(255), nullable=False, default="New Chat"
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)
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messages: Mapped[list[dict[str, Any]]] = mapped_column(
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JSONB, nullable=False, default=list
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)
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message_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0
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)
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total_input_tokens: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0
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)
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total_output_tokens: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0
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)
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pinned: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=False
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)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True),
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default=lambda: datetime.now(timezone.utc),
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onupdate=lambda: datetime.now(timezone.utc),
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)
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