feat: AI chat conclusion + survey completion & management (#95)
* 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>
This commit was merged in pull request #95.
This commit is contained in:
chihlasm
@@ -228,6 +228,117 @@ def _auto_title(message: str) -> str:
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return title
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CONCLUSION_SYSTEM_PROMPT = """\
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You are a ticket documentation specialist for MSP (Managed Service Provider) teams. \
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Your job is to transform an AI troubleshooting conversation into clean, professional \
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ticket notes that can be pasted directly into a PSA/ticketing system (ConnectWise, \
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Autotask, HaloPSA, etc.).
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## Output Format
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Generate a structured summary using this exact format:
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**Subject:** [One-line summary of the issue]
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**Outcome:** {outcome_label}
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**Problem Description:**
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[2-3 sentence summary of the original problem]
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**Steps Taken:**
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1. [Step] — [Result/finding]
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2. [Step] — [Result/finding]
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(list all troubleshooting steps from the conversation)
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**Current Status:**
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[Where things stand now — what was resolved, what remains]
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{notes_section}
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**Key Findings:**
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- [Important discovery or configuration detail]
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- [Any relevant error codes, settings, or values identified]
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{resume_section}
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## Rules
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- Be concise but thorough — these notes will be read by another engineer
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- Include specific technical details (commands run, error messages, config values)
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- Use plain text formatting (no HTML) — bold with ** is fine
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- Do NOT include conversational filler, greetings, or meta-commentary
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- Extract ALL actionable steps from the conversation, in chronological order
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- If the conversation identified root cause, state it clearly
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"""
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async def generate_conclusion_summary(
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chat: "AssistantChat",
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outcome: str,
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notes: str | None = None,
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) -> str:
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"""Generate a ticket-ready summary from a concluded chat conversation."""
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outcome_labels = {
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"resolved": "Resolved",
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"escalated": "Escalated",
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"paused": "Paused — To Be Continued",
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}
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outcome_label = outcome_labels.get(outcome, outcome)
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notes_section = ""
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if notes:
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notes_section = f"\n**Engineer Notes:**\n{notes}\n"
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resume_section = ""
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if outcome == "paused":
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resume_section = (
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"\n**Next Steps (for resumption):**\n"
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"- [What needs to happen next]\n"
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"- [Any pending actions or follow-ups]\n"
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)
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elif outcome == "escalated":
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resume_section = (
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"\n**Escalation Details:**\n"
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"- [Reason for escalation]\n"
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"- [Recommended next steps for receiving team/tier]\n"
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)
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# Build the conversation transcript for the AI
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transcript_lines = []
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for msg in chat.messages:
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role_label = "ENGINEER" if msg["role"] == "user" else "AI ASSISTANT"
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transcript_lines.append(f"[{role_label}]: {msg['content']}")
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transcript = "\n\n".join(transcript_lines)
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prompt = (
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f"Outcome: {outcome_label}\n\n"
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f"{'Engineer Notes: ' + notes if notes else '(No additional notes)'}\n\n"
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f"--- CONVERSATION TRANSCRIPT ---\n\n{transcript}\n\n"
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f"--- END TRANSCRIPT ---\n\n"
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f"Generate the ticket notes now. Replace all placeholder brackets with actual content from the conversation. "
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f"The notes_section placeholder should be: {notes_section or '(omit this section)'}\n"
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f"The resume_section placeholder should be filled based on the conversation context."
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)
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system_with_vars = CONCLUSION_SYSTEM_PROMPT.replace(
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"{outcome_label}", outcome_label
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).replace(
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"{notes_section}", notes_section or ""
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).replace(
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"{resume_section}", resume_section
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)
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content, _, _ = await _call_ai(
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system_base=system_with_vars,
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rag_context="",
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history=[],
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new_message=prompt,
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max_tokens=2048,
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)
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return content
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async def create_chat(
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user_id: UUID,
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account_id: UUID,
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