8d6accaf60
- Profile settings, account transfer, delete/leave account flows - Email verification with JWT tokens and Resend integration - AI assistant/copilot fixes: markdown rendering, shared RAG helpers, token tracking, input refocus, model_validate usage - User guides hub + detail pages with 13 topic guides - Sidebar and top bar navigation for guides Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
"""Standalone AI assistant chat service with RAG context.
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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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"""
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import logging
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from typing import Optional, 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.ai_provider import get_ai_provider
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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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ASSISTANT_SYSTEM_PROMPT = """You are a Senior Systems and Network Engineer with 15+ years of experience working in Managed Service Provider (MSP) environments. You specialize in:
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- Windows Server, Active Directory, Group Policy, and Hybrid Identity (Entra ID)
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- Networking (TCP/IP, DNS, DHCP, VPN, firewall troubleshooting, Cisco/Fortinet)
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- Virtualization (VMware, Hyper-V) and cloud platforms (Azure, AWS, M365)
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- Endpoint management, RMM tools, and PSA platforms (ConnectWise, Datto, Kaseya)
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- PowerShell scripting and automation
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When answering:
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- Be direct and actionable — MSP engineers need fast, practical answers
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- Include specific commands, paths, and config values when relevant
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- Mention potential risks or gotchas before suggesting changes
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- If a relevant troubleshooting flow exists in the team's library, reference it
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- Keep responses concise but thorough — prefer bullet points and code blocks
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- Format code with proper markdown code blocks
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"""
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def _auto_title(message: str) -> str:
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"""Generate a short title from the first user message."""
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title = message.strip()[:100]
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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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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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) -> tuple[str, list[dict[str, Any]], AssistantChat]:
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"""Send a user message and get AI response.
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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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# Build system prompt
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system_prompt = ASSISTANT_SYSTEM_PROMPT + build_rag_context(rag_results)
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# Build messages for AI
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ai_messages = []
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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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ai_messages.append({"role": "user", "content": message})
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# Call AI
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provider = get_ai_provider()
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ai_content, input_tokens, output_tokens = await provider.generate_text(
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system_prompt=system_prompt,
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messages=ai_messages,
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max_tokens=4096,
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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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