feat: add ResolutionOutputGenerator with three-output generation

Adds ResolutionOutputGenerator service that generates PSA ticket notes,
knowledge base article draft, and client summary on session resolve, plus
integration tests for generate_all and edit_output.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/services/resolution_output_generator.py

@@ -0,0 +1,118 @@
+"""Resolution output generator — three deliverables on session resolve."""
+import logging
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.ai_session import AISession
+from app.models.session_resolution_output import SessionResolutionOutput
+from app.services.assistant_chat_service import _call_ai
+
+logger = logging.getLogger(__name__)
+
+RESOLUTION_MODEL = "claude-sonnet-4-6"
+
+
+class ResolutionOutputGenerator:
+    def __init__(self, db: AsyncSession):
+        self.db = db
+
+    async def generate_all(self, session_id: UUID) -> list[SessionResolutionOutput]:
+        result = await self.db.execute(
+            select(AISession).where(AISession.id == session_id)
+        )
+        session = result.scalar_one_or_none()
+        if not session:
+            raise ValueError(f"Session {session_id} not found")
+
+        context = self._build_session_context(session)
+
+        outputs = []
+        for output_type, prompt in [
+            ("psa_ticket_notes", self._psa_notes_prompt(context)),
+            ("knowledge_base", self._kb_article_prompt(context)),
+            ("client_summary", self._client_summary_prompt(context)),
+        ]:
+            content, _, _ = await _call_ai(
+                system_base="You are a technical documentation assistant for MSP teams.",
+                rag_context="",
+                history=[],
+                new_message=prompt,
+                max_tokens=2048,
+            )
+
+            output = SessionResolutionOutput(
+                session_id=session_id,
+                output_type=output_type,
+                generated_content=content,
+                status="draft",
+                generated_by_model=RESOLUTION_MODEL,
+            )
+            self.db.add(output)
+            outputs.append(output)
+
+        await self.db.flush()
+        return outputs
+
+    async def edit_output(self, output_id: UUID, edited_content: str) -> SessionResolutionOutput:
+        result = await self.db.execute(
+            select(SessionResolutionOutput).where(SessionResolutionOutput.id == output_id)
+        )
+        output = result.scalar_one_or_none()
+        if not output:
+            raise ValueError(f"Output {output_id} not found")
+        output.edited_content = edited_content
+        await self.db.flush()
+        return output
+
+    async def push_output(self, output_id: UUID, destination: str) -> SessionResolutionOutput:
+        result = await self.db.execute(
+            select(SessionResolutionOutput).where(SessionResolutionOutput.id == output_id)
+        )
+        output = result.scalar_one_or_none()
+        if not output:
+            raise ValueError(f"Output {output_id} not found")
+
+        from datetime import datetime, timezone
+        output.status = "pushed"
+        output.pushed_to = destination
+        output.pushed_at = datetime.now(timezone.utc)
+        await self.db.flush()
+        return output
+
+    def _build_session_context(self, session: AISession) -> str:
+        parts = [
+            f"Problem: {session.problem_summary or 'Unknown'}",
+            f"Domain: {session.problem_domain or 'Unknown'}",
+            f"Resolution: {session.resolution_summary or 'Not specified'}",
+            f"Steps taken: {session.step_count}",
+        ]
+        msgs = session.conversation_messages or []
+        if msgs:
+            parts.append("\nConversation highlights:")
+            for msg in msgs[-10:]:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")[:200]
+                parts.append(f"  [{role}]: {content}")
+        return "\n".join(parts)
+
+    def _psa_notes_prompt(self, context: str) -> str:
+        return (
+            f"Generate professional PSA ticket notes for this resolved troubleshooting session.\n"
+            f"Format as structured markdown with: Problem, Diagnostic Steps, Resolution, Recommendations.\n\n{context}"
+        )
+
+    def _kb_article_prompt(self, context: str) -> str:
+        return (
+            f"Generate a knowledge base article draft from this resolved session.\n"
+            f"Include: Symptoms, Root Cause, Resolution Steps, Things to Rule Out First.\n\n{context}"
+        )
+
+    def _client_summary_prompt(self, context: str) -> str:
+        return (
+            f"Generate a non-technical summary for the end user/client.\n"
+            f"Explain what was wrong and what was done to fix it in plain language.\n"
+            f"No jargon. 2-3 paragraphs max.\n\n{context}"
+        )