0fb1ef33a0
* fix: increase assistant chat input height from 1 to 3 rows Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add Anthropic prompt caching to assistant chat Cache the static system prompt and conversation history prefix across turns, reducing input token costs by ~80% on multi-turn conversations. RAG context is intentionally uncached since it changes per query. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add Microsoft Learn MCP integration + refine assistant system prompt - Integrate Microsoft Learn MCP server via Anthropic's MCP connector for real-time documentation lookups (docs search, fetch, code samples) - Refine system prompt: clear persona, structured answer guidelines, when to use RAG flows vs Microsoft Learn, guardrails against fabrication - Add ENABLE_MCP_MICROSOFT_LEARN config toggle (default: True) - Fix bugs from prior edit: wrong MCP URL, broken indentation, undefined usage/token variables, NOT_GIVEN for disabled MCP params - Log MCP tool usage and cache performance Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: AI chat session conclusion + survey completion & management AI Assistant - Conclude Session: - 3-step modal: select outcome (resolved/escalated/paused), add notes, AI-generated summary - AI generates structured ticket notes from conversation transcript (PSA-ready format) - Copy to clipboard for pasting into ticketing systems - "Resume in New Chat" for paused sessions (pre-loads context into new chat) - Backend: POST /chats/{id}/conclude endpoint, conclusion_summary/outcome/concluded_at fields - Migration 048: add conclusion fields to assistant_chats Survey Completion Flow: - Email-to-self option after submission (branded HTML email with formatted responses) - Finish button navigates to /survey/thank-you page - Thank you page with close-window message and feedback email callout - Already-submitted state updated with same messaging - Backend: POST /survey/email-copy public endpoint Survey Admin Management: - Read/unread indicators (cyan dot, bold name, auto-mark on expand) - Unread count stat card - Per-row context menu: mark read/unread, archive/unarchive, delete - Bulk actions bar: select all, mark read/unread, archive, delete - Show Archived toggle to filter archived responses - Backend: 7 new admin endpoints (read, unread, archive, unarchive, delete, bulk) - Migration 049: add is_read, archived_at to survey_responses Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: initialize VerifyEmailPage state from token to avoid setState in effect Moves the no-token error case from useEffect into initial state to satisfy the react-hooks/set-state-in-effect ESLint rule. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
69 lines
2.2 KiB
Python
69 lines
2.2 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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conclusion_outcome: Mapped[Optional[str]] = mapped_column(
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String(20), nullable=True
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)
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conclusion_summary: Mapped[Optional[str]] = mapped_column(
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String, nullable=True
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)
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concluded_at: Mapped[Optional[datetime]] = mapped_column(
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DateTime(timezone=True), nullable=True
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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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