refactor: remove dead assistant_chat system, consolidate image helpers
The old /assistant/chats/* CRUD endpoints and assistant_chat_service
chat functions were unused — the frontend exclusively uses
/ai-sessions/{id}/chat (unified_chat_service) for all chat operations.
Removed:
- Chat CRUD endpoints (create, list, get, send, delete, conclude)
- assistant_chat_service: create_chat, send_message,
generate_conclusion_summary, CONCLUSION_SYSTEM_PROMPT
- Frontend: assistantChatApi chat methods, dead types
(AssistantChat, AssistantChatMessage, ConcludeChatRequest, etc.)
Kept:
- /assistant/retention endpoints (used by ChatRetentionSettingsPage)
- Shared AI infrastructure (_call_ai, _call_anthropic_cached,
ASSISTANT_SYSTEM_PROMPT, _auto_title) — imported by unified_chat_service
Moved:
- fetch_upload_images + resize_image_for_vision → storage_service.py
(shared location, not tied to dead endpoint)
Also added "Image Analysis" section to system prompt so Claude knows
to describe attached screenshots.
-650 lines of dead code removed.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
chihlasm
@@ -1,7 +1,7 @@
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"""Standalone AI assistant chat service with RAG context.
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"""Shared AI chat infrastructure — system prompt, prompt caching, and AI calling.
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Provides persistent conversation history for general IT questions
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with semantic search over the team's flow library.
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Used by unified_chat_service (the active chat backend). The assistant_chat
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CRUD endpoints were removed — only retention settings remain on that router.
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Uses Anthropic prompt caching to reduce cost on multi-turn conversations:
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- The static system prompt is cached (ephemeral, 5-min TTL)
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@@ -13,14 +13,8 @@ for real-time documentation lookups (controlled by ENABLE_MCP_MICROSOFT_LEARN).
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"""
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import logging
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from typing import Any
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from uuid import UUID
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.config import settings
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from app.models.assistant_chat import AssistantChat
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from app.services.rag_service import search as rag_search, build_rag_context, extract_suggested_flows
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logger = logging.getLogger(__name__)
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@@ -74,6 +68,11 @@ You have access to Microsoft's official documentation via Microsoft Learn. Use i
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- No team flow covers the topic and vendor-specific detail would help
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Do NOT use Microsoft Learn for every question — only when official docs add real value.
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## Image Analysis
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When an image is attached, analyze it carefully. Screenshots of error messages, \
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config panels, event viewer logs, and network diagrams are common in MSP work. \
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Describe what you see and use the visual information to inform your troubleshooting advice.
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## Boundaries
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- Stay focused on IT infrastructure, systems administration, and MSP operations.
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- If a question is clearly outside your domain, say so briefly and redirect.
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@@ -273,199 +272,3 @@ def _auto_title(message: str) -> str:
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if len(message) > 100:
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title = title.rsplit(" ", 1)[0] + "..."
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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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db: AsyncSession,
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) -> AssistantChat:
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"""Create a new empty chat."""
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chat = AssistantChat(
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user_id=user_id,
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account_id=account_id,
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messages=[],
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)
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db.add(chat)
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await db.flush()
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return chat
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async def send_message(
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chat_id: UUID,
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user_id: UUID,
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account_id: UUID,
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message: str,
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db: AsyncSession,
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images: list[dict[str, Any]] | None = None,
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) -> tuple[str, list[dict[str, Any]], AssistantChat]:
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"""Send a user message and get AI response.
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Args:
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images: Optional list of {"media_type": str, "data": str (base64)}
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for vision content attached to this message.
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Returns (ai_content, suggested_flows, chat).
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"""
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result = await db.execute(
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select(AssistantChat).where(
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AssistantChat.id == chat_id,
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AssistantChat.user_id == user_id,
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)
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)
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chat = result.scalar_one_or_none()
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if not chat:
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raise ValueError("Chat not found")
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# Auto-title from first message
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if chat.message_count == 0:
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chat.title = _auto_title(message)
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# RAG search
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rag_results = await rag_search(
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query=message,
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account_id=account_id,
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db=db,
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limit=8,
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)
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rag_context = build_rag_context(rag_results)
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# Build messages for AI
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ai_messages: list[dict[str, Any]] = []
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for msg in chat.messages:
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if msg["role"] in ("user", "assistant"):
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ai_messages.append({"role": msg["role"], "content": msg["content"]})
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# Call AI with prompt caching (Anthropic) or generic provider
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ai_content, input_tokens, output_tokens = await _call_ai(
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system_base=ASSISTANT_SYSTEM_PROMPT,
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rag_context=rag_context,
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history=ai_messages,
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new_message=message,
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images=images,
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)
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# Update chat
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msgs = list(chat.messages)
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msgs.append({"role": "user", "content": message})
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msgs.append({"role": "assistant", "content": ai_content})
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chat.messages = msgs
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chat.message_count += 2
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chat.total_input_tokens += input_tokens
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chat.total_output_tokens += output_tokens
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suggested_flows = extract_suggested_flows(rag_results)
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return ai_content, suggested_flows, chat
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