fix tools calls
This commit is contained in:
Roberto
@@ -16,7 +16,7 @@ from app.agents.note_agent import NOTE_TOOLS
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from app.agents.project_agent import PROJECT_TOOLS
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from app.agents.task_agent import TASK_TOOLS
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from app.agents.timeline_agent import TIMELINE_TOOLS
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from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
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from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
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from app.core.llm import get_agent_llm, model_for_agent
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from app.core.memory_middleware import MemoryMiddleware
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from app.core.ws_context import clear_tool_result_collector, execute_on_client, set_tool_result_collector
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@@ -56,36 +56,89 @@ def _language_instruction(context: dict[str, Any]) -> str:
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)
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def _datetime_context_injection(context: dict[str, Any]) -> str:
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"""Build a system-prompt paragraph with current timestamp, user timezone, and format prefs."""
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"""Build a comprehensive DATE CONTEXT block with pre-computed ms-epoch boundaries for common ranges."""
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fp = context.get("format_prefs")
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if not fp or not isinstance(fp, dict):
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return ""
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try:
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from zoneinfo import ZoneInfo
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from datetime import datetime as _dt, timezone as _utc
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from datetime import datetime as _dt, timezone as _utc, timedelta as _td
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tz_name: str = str(fp.get("timezone") or "UTC")
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now_iso: str = str(fp.get("now_iso") or "")
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date_fmt: str = str(fp.get("date_format") or "dd/MM/yyyy")
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time_fmt: str = str(fp.get("time_format") or "24h")
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tz = ZoneInfo(tz_name)
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if now_iso:
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now_utc = _dt.fromisoformat(now_iso.replace("Z", "+00:00"))
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else:
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now_utc = _dt.now(_utc.utc)
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tz = ZoneInfo(tz_name)
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now_ms = int(now_utc.timestamp() * 1000)
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now_local = now_utc.astimezone(tz)
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today_local = now_local.strftime("%Y-%m-%d")
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weekday_local = now_local.strftime("%A")
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now_local_str = now_local.strftime("%Y-%m-%d %H:%M")
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weekday_str = now_local.strftime("%A")
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y, m, d = now_local.year, now_local.month, now_local.day
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def _day(year: int, month: int, day: int) -> tuple[int, int]:
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s = _dt(year, month, day, tzinfo=tz)
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e = s + _td(days=1)
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return int(s.timestamp() * 1000), int(e.timestamp() * 1000) - 1
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def _between(start: "_dt", end_excl: "_dt") -> tuple[int, int]:
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return int(start.timestamp() * 1000), int(end_excl.timestamp() * 1000) - 1
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today_s, today_e = _day(y, m, d)
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yd = now_local - _td(days=1)
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yesterday_s, yesterday_e = _day(yd.year, yd.month, yd.day)
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tm = now_local + _td(days=1)
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tomorrow_s, tomorrow_e = _day(tm.year, tm.month, tm.day)
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# ISO week (Mon–Sun)
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monday = _dt(y, m, d, tzinfo=tz) - _td(days=now_local.weekday())
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last_monday = monday - _td(weeks=1)
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next_monday = monday + _td(weeks=1)
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this_week_s, this_week_e = _between(monday, next_monday)
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last_week_s, last_week_e = _between(last_monday, monday)
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next_week_s, next_week_e = _between(next_monday, next_monday + _td(weeks=1))
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# Calendar months
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this_m_start = _dt(y, m, 1, tzinfo=tz)
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next_m_start = _dt(y + (m // 12), m % 12 + 1, 1, tzinfo=tz)
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last_m_start = _dt(y - (1 if m == 1 else 0), 12 if m == 1 else m - 1, 1, tzinfo=tz)
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next2_m = next_m_start.month % 12 + 1
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next2_y = next_m_start.year + (1 if next_m_start.month == 12 else 0)
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next2_m_start = _dt(next2_y, next2_m, 1, tzinfo=tz)
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this_month_s, this_month_e = _between(this_m_start, next_m_start)
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last_month_s, last_month_e = _between(last_m_start, this_m_start)
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next_month_s, next_month_e = _between(next_m_start, next2_m_start)
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# Calendar years
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this_yr_s, this_yr_e = _between(_dt(y, 1, 1, tzinfo=tz), _dt(y + 1, 1, 1, tzinfo=tz))
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last_yr_s, last_yr_e = _between(_dt(y - 1, 1, 1, tzinfo=tz), _dt(y, 1, 1, tzinfo=tz))
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sunday = monday + _td(days=6)
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last_sunday = last_monday + _td(days=6)
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next_sunday = next_monday + _td(days=6)
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return (
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f"\n\nCurrent instant: {now_utc.isoformat()}. "
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f"User local date: {today_local} ({weekday_local}). "
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f"Timezone: {tz_name}. "
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f"Display preference: dateFormat={date_fmt}, timeFormat={time_fmt}. "
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f"When calling tools with date fields, always pass integer Unix milliseconds (ms since epoch, UTC). "
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f"When calling list_tasks_due_today or list_timelines_today, always pass user_timezone=\"{tz_name}\". "
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f"When presenting dates to the user in chat, format using the display preference above."
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f"\n\nDATE CONTEXT (timezone: {tz_name}, dateFormat: {date_fmt}, timeFormat: {time_fmt})\n"
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f"now_local: {now_local_str} ({weekday_str})\n"
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f"now_ms: {now_ms}\n\n"
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f"today [{today_s}, {today_e}] {y:04d}-{m:02d}-{d:02d}\n"
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f"tomorrow [{tomorrow_s}, {tomorrow_e}] {tm.strftime('%Y-%m-%d')}\n"
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f"yesterday [{yesterday_s}, {yesterday_e}] {yd.strftime('%Y-%m-%d')}\n"
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f"this_week [{this_week_s}, {this_week_e}] {monday.strftime('%Y-%m-%d')} → {sunday.strftime('%Y-%m-%d')} (Mon–Sun)\n"
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f"last_week [{last_week_s}, {last_week_e}] {last_monday.strftime('%Y-%m-%d')} → {last_sunday.strftime('%Y-%m-%d')}\n"
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f"next_week [{next_week_s}, {next_week_e}] {next_monday.strftime('%Y-%m-%d')} → {next_sunday.strftime('%Y-%m-%d')}\n"
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f"this_month [{this_month_s}, {this_month_e}] {y:04d}-{m:02d}\n"
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f"last_month [{last_month_s}, {last_month_e}] {last_m_start.strftime('%Y-%m')}\n"
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f"next_month [{next_month_s}, {next_month_e}] {next_m_start.strftime('%Y-%m')}\n"
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f"this_year [{this_yr_s}, {this_yr_e}] {y:04d}\n"
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f"last_year [{last_yr_s}, {last_yr_e}] {y - 1:04d}\n\n"
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f"When calling list_tasks_due_today or list_timelines_today, always pass user_timezone=\"{tz_name}\".\n"
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f"When presenting dates, format using dateFormat={date_fmt} and timeFormat={time_fmt}."
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)
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except Exception:
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return ""
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@@ -123,27 +176,75 @@ def _relational_memory_injection(context: dict[str, Any]) -> str:
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return section
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_HOME_SYSTEM_PROMPT = (
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"You are the home assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
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"Always use tools for factual data retrieval before answering. "
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"When the user asks to remember, forget, or update what you know about them, use memory tools. "
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"If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
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"Return markdown and use tags when relevant: <project>[ids]</project>, <task>[ids]</task>, "
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"<note>[ids]</note>, <timeline>[ids]</timeline>, <chart>{json}</chart>. "
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"When listing tasks or timelines, each id tag must be on its own line with no prefix/suffix text. "
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"Never put titles, priorities, or dates on the same line as <task> or <timeline> tags. "
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"For questions about upcoming timelines (e.g. 'prossimi eventi'), include only future items in the current month unless the user asks a different range. "
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"For upcoming tasks, after tag lines add a short recommendation based on due date and priority."
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)
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def _request_context_block(context: dict[str, Any]) -> str:
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"""Return a small block with per-request scope and resolved project context."""
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parts: list[str] = []
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scope = context.get("scope")
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if scope and isinstance(scope, dict):
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parts.append(f"scope: {json.dumps(scope, ensure_ascii=True)}")
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resolved = context.get("resolved_project_id")
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if resolved and isinstance(resolved, str):
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parts.append(f"resolved_project_id: {resolved}")
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return "\n".join(parts)
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_FLOATING_SYSTEM_PROMPT = (
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"You are the floating assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
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"Stay focused on the floating scope in context.scope and answer concisely. "
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"Return plain text only. Do not output XML/HTML-like tags such as <task>, <project>, <note>, <timeline>, or any bracketed id tag wrappers. "
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"Always use tools for factual data retrieval before answering. "
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"When the user asks to remember, forget, or update what you know about them, use memory tools. "
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"If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
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)
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_HOME_SYSTEM_PROMPT = """\
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You are the home assistant for adiuvAI with direct access to all tools: tasks, projects, notes, timelines, and memory tools.
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Always use tools for factual data retrieval before answering.
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When the user asks to remember, forget, or update what you know about them, use memory tools.
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# Output format
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Return markdown and use tags when relevant: <project>[ids]</project>, <task>[ids]</task>, <note>[ids]</note>, <timeline>[ids]</timeline>, <chart>{{json}}</chart>.
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When listing tasks or timelines, each id tag must be on its own line with no prefix/suffix text.
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Never put titles, priorities, or dates on the same line as <task> or <timeline> tags.
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For questions about upcoming timelines (e.g. 'prossimi eventi'), include only future items in the current month unless the user asks a different range.
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For upcoming tasks, after tag lines add a short recommendation based on due date and priority.
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# Date filtering
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{date_context}
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When filtering tasks/timelines/notes by date, take dueDateFrom / dueDateTo (ms epoch UTC) verbatim from the DATE CONTEXT boundary table above. Do NOT compute boundaries from now_ms yourself.
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For specific dates not listed, compute local-midnight in the user timezone and convert to UTC ms.
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For "today" / "tomorrow" queries, prefer list_tasks_due_today / list_timelines_today with user_timezone from DATE CONTEXT.
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# Language
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{language_instruction}
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# Known people & projects
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{relational_memory}
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# Behavioral hints
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{proactive_hints}
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# Request context
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{request_context}\
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"""
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_FLOATING_SYSTEM_PROMPT = """\
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You are the floating assistant for adiuvAI with direct access to all tools: tasks, projects, notes, timelines, and memory tools.
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Stay focused on the floating scope and answer concisely.
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Return plain text only. Do not output XML/HTML-like tags such as <task>, <project>, <note>, <timeline>, or any bracketed id tag wrappers.
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Always use tools for factual data retrieval before answering.
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When the user asks to remember, forget, or update what you know about them, use memory tools.
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# Date filtering
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{date_context}
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When filtering by date, take dueDateFrom / dueDateTo (ms epoch UTC) verbatim from the DATE CONTEXT boundary table above. Do NOT compute boundaries from now_ms yourself.
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For specific dates not listed, compute local-midnight in the user timezone and convert to UTC ms.
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# Language
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{language_instruction}
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# Known people & projects
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{relational_memory}
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# Behavioral hints
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{proactive_hints}
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# Request context
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{request_context}\
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"""
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_FLOATING_DOMAIN_CLASSIFIER_PROMPT = (
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"You are a strict domain classifier for websocket floating requests. "
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@@ -253,10 +354,18 @@ def _session_id_from_context(context: dict[str, Any]) -> str | None:
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return None
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def _context_for_model(context: dict[str, Any]) -> dict[str, Any]:
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sanitized = dict(context)
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sanitized.pop("_debug", None)
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return sanitized
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def _build_system_prompt(name: str, fallback: str, context: dict[str, Any]) -> tuple[str, Any]:
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"""Fetch Langfuse template and compile all per-request slots into one system prompt."""
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template, prompt_obj = get_prompt_or_fallback(name, fallback)
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text = compile_prompt(
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template, prompt_obj,
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date_context=_datetime_context_injection(context).strip(),
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language_instruction=_language_instruction(context).strip(),
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relational_memory=_relational_memory_injection(context).strip(),
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proactive_hints=_proactive_hints_injection(context).strip(),
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request_context=_request_context_block(context),
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)
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return text, prompt_obj
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_TAG_LINE_RE = re.compile(r"<(task|timeline)>\[[^\]]+\]</\1>")
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@@ -713,17 +822,11 @@ async def _run_single_agent(
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lf = get_langfuse()
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llm = get_agent_llm(agent_name)
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tools = _all_tools_for_user(user_id, trace_id)
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model_context = _context_for_model(context)
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logger.info("deep_agent: run_single_agent_start trace=%s user=%s", trace_id or "-", user_id)
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llm_with_tools = llm.bind_tools(tools)
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messages: list[Any] = [
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SystemMessage(content=system_prompt),
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HumanMessage(
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content=(
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f"User message:\n{message}\n\n"
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f"Context:\n{json.dumps({'context': model_context}, ensure_ascii=True)[:3500]}"
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)
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),
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HumanMessage(content=message),
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]
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tool_calls_count = 0
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@@ -843,17 +946,11 @@ async def _run_single_agent_stream(
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llm = get_agent_llm(agent_name)
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if tools is None:
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tools = _all_tools_for_user(user_id, trace_id)
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model_context = _context_for_model(context)
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logger.info("deep_agent: run_single_agent_stream_start trace=%s user=%s", trace_id or "-", user_id)
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llm_with_tools = llm.bind_tools(tools)
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messages: list[Any] = [
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SystemMessage(content=system_prompt),
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HumanMessage(
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content=(
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f"User message:\n{message}\n\n"
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f"Context:\n{json.dumps({'context': model_context}, ensure_ascii=True)[:3500]}"
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)
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),
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HumanMessage(content=message),
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]
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tool_calls_count = 0
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@@ -969,13 +1066,7 @@ async def _run_single_agent_stream(
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async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
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prepared_context = await _prepare_context(message, context)
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system_prompt, langfuse_prompt = get_prompt_or_fallback(
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"home_system", _HOME_SYSTEM_PROMPT
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)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _datetime_context_injection(context)
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system_prompt += _language_instruction(context)
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system_prompt, langfuse_prompt = _build_system_prompt("home_system", _HOME_SYSTEM_PROMPT, prepared_context)
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response = await _run_single_agent(
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user_id=user_id,
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system_prompt=system_prompt,
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@@ -990,13 +1081,7 @@ async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
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async def run_floating(user_id: str, message: str, context: dict[str, Any]) -> tuple[str, dict[str, str | None]]:
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prepared_context = await _prepare_context(message, context)
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domain = await _infer_floating_domain(message, prepared_context)
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system_prompt, langfuse_prompt = get_prompt_or_fallback(
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"floating_system", _FLOATING_SYSTEM_PROMPT
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)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _datetime_context_injection(context)
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system_prompt += _language_instruction(context)
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system_prompt, langfuse_prompt = _build_system_prompt("floating_system", _FLOATING_SYSTEM_PROMPT, prepared_context)
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response = await _run_single_agent(
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user_id=user_id,
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system_prompt=system_prompt,
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@@ -1017,13 +1102,7 @@ async def run_home_stream(
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context: dict[str, Any],
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) -> AsyncGenerator[tuple[str, Any], None]:
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prepared_context = await _prepare_context(message, context)
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system_prompt, langfuse_prompt = get_prompt_or_fallback(
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"home_system", _HOME_SYSTEM_PROMPT
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)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _datetime_context_injection(context)
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system_prompt += _language_instruction(context)
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system_prompt, langfuse_prompt = _build_system_prompt("home_system", _HOME_SYSTEM_PROMPT, prepared_context)
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text_chunks: list[str] = []
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async for event in _run_single_agent_stream(
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user_id=user_id,
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@@ -1053,13 +1132,7 @@ async def run_floating_stream(
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domain = await _infer_floating_domain(message, prepared_context)
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yield "floating_domain", domain
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system_prompt, langfuse_prompt = get_prompt_or_fallback(
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"floating_system", _FLOATING_SYSTEM_PROMPT
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)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _datetime_context_injection(context)
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system_prompt += _language_instruction(context)
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system_prompt, langfuse_prompt = _build_system_prompt("floating_system", _FLOATING_SYSTEM_PROMPT, prepared_context)
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sanitizer = _FloatingStreamSanitizer()
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emitted_sanitized = False
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raw_chunks: list[str] = []
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