feat: AI marker system prompt fixes, TaskLane activation, and FlowPilot updates
- Fix system prompt to ensure [QUESTIONS]/[ACTIONS] markers in AI responses - Add format reminder injection to user messages for marker compliance - Wire TaskLane activation in prefill and resume paths - Add ActionCardGroup component for structured question/action rendering - Update FlowPilot session and step card components - Update ai-session schemas and types for marker data Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
chihlasm
@@ -4,7 +4,9 @@ Replaces assistant_chat_service for new chat sessions. Messages are stored
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in ai_sessions.conversation_messages JSONB. Reuses the same AI calling
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infrastructure and system prompt from assistant_chat_service.
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"""
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import json
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import logging
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import re
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from typing import Any
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from uuid import UUID
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@@ -22,6 +24,129 @@ from app.services.rag_service import search as rag_search, build_rag_context, ex
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logger = logging.getLogger(__name__)
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def _parse_fork_marker(ai_content: str) -> tuple[str, dict[str, Any] | None]:
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"""Extract [FORK]...[/FORK] JSON from AI response.
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Returns (cleaned_content, fork_data_or_None).
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The fork marker is stripped from the display text.
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"""
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match = re.search(r'\[FORK\]\s*([\s\S]*?)\s*\[/FORK\]', ai_content)
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if not match:
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return ai_content, None
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try:
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raw = match.group(1).strip()
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# Strip markdown fences if AI wrapped it
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if raw.startswith("```"):
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raw = re.sub(r'^```(?:json)?\s*', '', raw)
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raw = re.sub(r'\s*```$', '', raw)
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fork_data = json.loads(raw)
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except (json.JSONDecodeError, ValueError) as e:
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logger.warning("Failed to parse [FORK] marker: %s", e)
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return ai_content, None
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# Validate structure
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if not isinstance(fork_data, dict) or "options" not in fork_data:
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logger.warning("Invalid [FORK] data — missing 'options'")
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return ai_content, None
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options = fork_data["options"]
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if not isinstance(options, list) or len(options) < 2:
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logger.warning("Invalid [FORK] data — need at least 2 options")
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return ai_content, None
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# Strip the marker from display text
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cleaned = ai_content[:match.start()] + ai_content[match.end():]
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cleaned = cleaned.strip()
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return cleaned, fork_data
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def _parse_actions_marker(ai_content: str) -> tuple[str, list[dict[str, Any]] | None]:
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"""Extract [ACTIONS]...[/ACTIONS] JSON from AI response.
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Returns (cleaned_content, actions_list_or_None).
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The actions marker is stripped from the display text.
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"""
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match = re.search(r'\[ACTIONS\]\s*([\s\S]*?)\s*\[/ACTIONS\]', ai_content)
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if not match:
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return ai_content, None
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try:
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raw = match.group(1).strip()
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if raw.startswith("```"):
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raw = re.sub(r'^```(?:json)?\s*', '', raw)
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raw = re.sub(r'\s*```$', '', raw)
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actions = json.loads(raw)
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except (json.JSONDecodeError, ValueError) as e:
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logger.warning("Failed to parse [ACTIONS] marker: %s", e)
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return ai_content, None
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if not isinstance(actions, list) or len(actions) == 0:
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logger.warning("Invalid [ACTIONS] data — need at least 1 action")
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return ai_content, None
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# Validate each action has at minimum a label
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valid_actions = []
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for a in actions:
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if isinstance(a, dict) and a.get("label"):
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valid_actions.append({
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"label": a["label"],
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"command": a.get("command"),
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"description": a.get("description", ""),
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})
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if not valid_actions:
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return ai_content, None
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cleaned = ai_content[:match.start()] + ai_content[match.end():]
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cleaned = cleaned.strip()
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return cleaned, valid_actions
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def _parse_questions_marker(ai_content: str) -> tuple[str, list[dict[str, Any]] | None]:
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"""Extract [QUESTIONS]...[/QUESTIONS] JSON from AI response.
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Returns (cleaned_content, questions_list_or_None).
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The questions marker is stripped from the display text.
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"""
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match = re.search(r'\[QUESTIONS\]\s*([\s\S]*?)\s*\[/QUESTIONS\]', ai_content)
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if not match:
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return ai_content, None
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try:
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raw = match.group(1).strip()
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if raw.startswith("```"):
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raw = re.sub(r'^```(?:json)?\s*', '', raw)
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raw = re.sub(r'\s*```$', '', raw)
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questions = json.loads(raw)
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except (json.JSONDecodeError, ValueError) as e:
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logger.warning("Failed to parse [QUESTIONS] marker: %s", e)
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return ai_content, None
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if not isinstance(questions, list) or len(questions) == 0:
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logger.warning("Invalid [QUESTIONS] data — need at least 1 question")
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return ai_content, None
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# Validate each question has at minimum a text field
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valid_questions = []
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for q in questions:
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if isinstance(q, dict) and q.get("text"):
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valid_questions.append({
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"text": q["text"],
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"context": q.get("context", ""),
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})
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if not valid_questions:
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return ai_content, None
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cleaned = ai_content[:match.start()] + ai_content[match.end():]
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cleaned = cleaned.strip()
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return cleaned, valid_questions
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async def create_chat_session(
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user_id: UUID,
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account_id: UUID,
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@@ -58,14 +183,14 @@ async def send_chat_message(
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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]], AISession]:
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) -> tuple[str, list[dict[str, Any]], AISession, dict[str, Any] | None, list[dict[str, Any]] | None, list[dict[str, Any]] | None]:
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"""Send a message in a chat session 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, session).
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Returns (ai_content, suggested_flows, session, fork_metadata, actions_data, questions_data).
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"""
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result = await db.execute(
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select(AISession).where(
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@@ -124,10 +249,47 @@ async def send_chat_message(
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if session.status == "paused":
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session.status = "active"
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# Check for fork, actions, and questions markers in branch response too
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branch_display, branch_fork_data = _parse_fork_marker(ai_content)
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branch_display, branch_actions_data = _parse_actions_marker(branch_display)
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branch_display, branch_questions_data = _parse_questions_marker(branch_display)
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if branch_display != ai_content:
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# Store stripped content in branch history
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msgs[-1] = {"role": "assistant", "content": branch_display}
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branch.conversation_messages = msgs
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branch_fork_metadata = None
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if branch_fork_data:
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try:
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fork_point, new_branches = await manager.create_fork(
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session_id=session.id,
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parent_branch_id=branch.id,
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trigger_step_id=None,
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fork_reason=branch_fork_data.get("fork_reason", ""),
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options=[
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{"label": o["label"], "description": o.get("description", "")}
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for o in branch_fork_data["options"]
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],
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)
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first_branch = new_branches[0]
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await manager.switch_branch(session.id, first_branch.id)
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branch_fork_metadata = {
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"fork_point_id": str(fork_point.id),
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"fork_reason": branch_fork_data.get("fork_reason", ""),
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"branches": [
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{"branch_id": str(b.id), "label": b.label}
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for b in new_branches
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],
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"active_branch_id": str(first_branch.id),
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}
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await db.flush()
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except Exception:
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logger.exception("Failed to create fork within branch for session %s", session.id)
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suggested_flows = extract_suggested_flows(
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await rag_search(query=message, account_id=account_id, db=db, limit=8)
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)
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return ai_content, suggested_flows, session
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return branch_display, suggested_flows, session, branch_fork_metadata, branch_actions_data, branch_questions_data
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# Auto-title from first message if still default
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if session.step_count == 0 and message.strip():
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@@ -161,10 +323,27 @@ async def send_chat_message(
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images=images,
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)
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# Append messages to conversation_messages
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# Check for fork marker in AI response
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display_content, fork_data = _parse_fork_marker(ai_content)
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# Check for actions marker in AI response
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display_content, actions_data = _parse_actions_marker(display_content)
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# Check for questions marker in AI response
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display_content, questions_data = _parse_questions_marker(display_content)
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logger.info(
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"Marker parsing results — actions: %s, questions: %s, fork: %s, raw_length: %d, display_length: %d",
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bool(actions_data), bool(questions_data), bool(fork_data),
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len(ai_content), len(display_content),
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)
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# Store DISPLAY content (markers stripped) in conversation_messages.
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# The format reminder in the user message + system prompt final reminder
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# are sufficient to keep the AI emitting markers on subsequent turns.
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msgs = list(session.conversation_messages or [])
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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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msgs.append({"role": "assistant", "content": display_content})
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session.conversation_messages = msgs
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session.step_count += 2 # message count for display
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session.total_input_tokens += input_tokens
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@@ -174,6 +353,46 @@ async def send_chat_message(
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if session.status == "paused":
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session.status = "active"
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# If fork was detected, create branches
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fork_metadata = None
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if fork_data:
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try:
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from app.services.branch_manager import BranchManager
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mgr = BranchManager(db)
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# Create root branch if this is the first fork
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if not session.is_branching:
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await mgr.create_root_branch(session.id)
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fork_point, new_branches = await mgr.create_fork(
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session_id=session.id,
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parent_branch_id=session.active_branch_id,
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trigger_step_id=None,
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fork_reason=fork_data.get("fork_reason", ""),
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options=[
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{"label": o["label"], "description": o.get("description", "")}
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for o in fork_data["options"]
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],
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)
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# Don't auto-switch — conversation continues on current branch.
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# Branches appear in sidebar. User switches when ready.
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fork_metadata = {
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"fork_point_id": str(fork_point.id),
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"fork_reason": fork_data.get("fork_reason", ""),
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"branches": [
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{"branch_id": str(b.id), "label": b.label}
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for b in new_branches
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],
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"active_branch_id": str(session.active_branch_id) if session.active_branch_id else None,
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}
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await db.flush()
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logger.info("Created fork with %d branches for session %s", len(new_branches), session_id)
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except Exception:
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logger.exception("Failed to create fork for session %s", session_id)
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# Fork failed but chat message still sent — don't break the response
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suggested_flows = extract_suggested_flows(rag_results)
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return ai_content, suggested_flows, session
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return display_content, suggested_flows, session, fork_metadata, actions_data, questions_data
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