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>
71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
"""Pydantic schemas for standalone AI assistant chat."""
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from typing import Optional, Any, Literal
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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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from app.schemas.copilot import SuggestedFlow
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class ChatCreateRequest(BaseModel):
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"""Empty body — creates a new blank conversation."""
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pass
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class ChatMessageRequest(BaseModel):
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message: str = Field(..., min_length=1, max_length=8000)
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class ChatMessageResponse(BaseModel):
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content: str
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suggested_flows: list[SuggestedFlow] = []
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class ChatListResponse(BaseModel):
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id: UUID
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title: str
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message_count: int
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pinned: bool
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created_at: datetime
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updated_at: datetime
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model_config = {"from_attributes": True}
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class ChatDetailResponse(BaseModel):
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id: UUID
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title: str
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messages: list[dict[str, Any]]
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message_count: int
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pinned: bool
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created_at: datetime
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updated_at: datetime
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model_config = {"from_attributes": True}
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class ChatUpdateRequest(BaseModel):
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title: Optional[str] = Field(None, min_length=1, max_length=255)
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pinned: Optional[bool] = None
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class RetentionSettingsResponse(BaseModel):
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chat_retention_days: Optional[int]
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chat_retention_max_count: Optional[int]
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class RetentionSettingsUpdate(BaseModel):
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chat_retention_days: Optional[int] = Field(None, ge=1, le=365)
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chat_retention_max_count: Optional[int] = Field(None, ge=10, le=10000)
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class ConcludeChatRequest(BaseModel):
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outcome: Literal["resolved", "escalated", "paused"]
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notes: Optional[str] = Field(None, max_length=2000)
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class ConcludeChatResponse(BaseModel):
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summary: str
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outcome: str
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concluded_at: datetime
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