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fix tools calls

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This commit is contained in:
Roberto
2026-04-27 09:15:08 +02:00
parent cb8f56d909
commit 6787e690ba
4 changed files with 659 additions and 120 deletions
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148
app/agents/task_agent.py
View File

@@ -26,32 +26,137 @@ def _is_uuid(value: str) -> bool:
async def list_tasks(
project_id: str = "",
status: str = "",
priority: str = "",
assignee: str = "",
search: str = "",
order_by: str = "",
order_dir: str = "",
due_date_from: int = -1,
due_date_to: int = -1,
created_at_from: int = -1,
created_at_to: int = -1,
completed_at_from: int = -1,
completed_at_to: int = -1,
is_ai_suggested: int = -1,
limit: int = 50,
offset: int = 0,
) -> str:
"""List tasks, optionally filtered by project_id, status (todo|in_progress|done),
a search string, or an order_by field name (dueDate|priority|createdAt)."""
"""List tasks with optional filters. Returns up to `limit` results (default 50).
project_id: UUID of the project to scope results to.
status: filter by status — todo | in_progress | done.
priority: filter by priority — high | medium | low.
assignee: substring to match against assignee names.
search: substring search across title and description.
order_by: sort field — dueDate | priority | createdAt | completedAt.
order_dir: asc (default) | desc.
due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
limit: max rows to return (default 50). Use with offset to paginate.
offset: skip first N rows (default 0).
Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
Tip — prefer count_tasks for "how many" questions to avoid listing rows.
Tip — for natural-language windows ("today", "tomorrow", "this week", "last month", etc.)
take due_date_from / due_date_to verbatim from the DATE CONTEXT block in the system prompt;
do not compute boundaries from the current UTC instant.
"""
normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
result = await execute_on_client(
action="select",
table="tasks",
filters={
"projectId": normalized_project_id or None,
"status": status or None,
"search": search or None,
"orderBy": order_by or None,
},
)
filters: dict[str, Any] = {
"projectId": normalized_project_id or None,
"status": status or None,
"priority": priority or None,
"search": search or None,
"orderBy": order_by or None,
"orderDir": order_dir or None,
"limit": limit,
"offset": offset,
}
if assignee:
filters["assignee"] = assignee
if due_date_from != -1:
filters["dueDateFrom"] = due_date_from
if due_date_to != -1:
filters["dueDateTo"] = due_date_to
if created_at_from != -1:
filters["createdAtFrom"] = created_at_from
if created_at_to != -1:
filters["createdAtTo"] = created_at_to
if completed_at_from != -1:
filters["completedAtFrom"] = completed_at_from
if completed_at_to != -1:
filters["completedAtTo"] = completed_at_to
if is_ai_suggested != -1:
filters["isAiSuggested"] = is_ai_suggested
result = await execute_on_client(action="select", table="tasks", filters=filters)
rows = result.get("rows", [])
if not rows:
return "No tasks found matching the given filters."
lines = [
f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, id: {r['id']})"
f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, "
f"dueDate: {r.get('dueDate')}, completedAt: {r.get('completedAt')}, id: {r['id']})"
for r in rows
]
return f"Found {len(rows)} task(s):\n" + "\n".join(lines)
@tool
async def count_tasks(
project_id: str = "",
status: str = "",
priority: str = "",
assignee: str = "",
search: str = "",
due_date_from: int = -1,
due_date_to: int = -1,
created_at_from: int = -1,
created_at_to: int = -1,
completed_at_from: int = -1,
completed_at_to: int = -1,
is_ai_suggested: int = -1,
) -> str:
"""Count tasks matching the given filters without returning rows.
Use this instead of list_tasks for "how many" questions — it is much cheaper.
Same filter parameters as list_tasks (no limit/offset/order_by needed).
due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
Tip — for natural-language windows take due_date_from / due_date_to from the DATE CONTEXT block;
do not compute boundaries from the current UTC instant.
"""
normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
filters: dict[str, Any] = {
"projectId": normalized_project_id or None,
"status": status or None,
"priority": priority or None,
"search": search or None,
}
if assignee:
filters["assignee"] = assignee
if due_date_from != -1:
filters["dueDateFrom"] = due_date_from
if due_date_to != -1:
filters["dueDateTo"] = due_date_to
if created_at_from != -1:
filters["createdAtFrom"] = created_at_from
if created_at_to != -1:
filters["createdAtTo"] = created_at_to
if completed_at_from != -1:
filters["completedAtFrom"] = completed_at_from
if completed_at_to != -1:
filters["completedAtTo"] = completed_at_to
if is_ai_suggested != -1:
filters["isAiSuggested"] = is_ai_suggested
result = await execute_on_client(action="count", table="tasks", filters=filters)
return f"Task count: {result.get('count', 0)}"
@tool
async def create_task(
title: str,
@@ -72,6 +177,8 @@ async def create_task(
due_date: Unix timestamp in milliseconds; 0 means no due date
project_id: optional UUID of the parent project
is_ai_suggested: 1 if proactively suggested, 0 if user-requested
completedAt is set automatically when status is 'done'.
"""
result = await execute_on_client(
action="insert",
@@ -108,6 +215,10 @@ async def update_task(
"""Update fields on an existing task. Only pass fields you want to change.
task_id: the task's UUID (required)
due_date: -1 means unchanged; 0 clears the due date; any positive value sets it
completedAt is managed automatically:
- setting status to 'done' records the current timestamp
- changing status away from 'done' clears completedAt
"""
updates: dict[str, Any] = {}
if title:
@@ -141,11 +252,12 @@ async def delete_task(task_id: str) -> str:
@tool
async def list_tasks_due_today(user_timezone: str = "UTC") -> str:
async def list_tasks_due_today(user_timezone: str = "UTC", include_done: bool = False) -> str:
"""List all tasks whose due date falls on today's date.
user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
Always pass the user's timezone so 'today' is computed in their local time.
include_done: set True to also include already-completed tasks due today (default False).
"""
try:
from zoneinfo import ZoneInfo
@@ -156,10 +268,13 @@ async def list_tasks_due_today(user_timezone: str = "UTC") -> str:
start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
start_ms = int(start_dt.timestamp() * 1000)
end_ms = start_ms + 86_400_000 - 1
filters: dict[str, Any] = {"dueDateFrom": start_ms, "dueDateTo": end_ms}
if not include_done:
filters["status"] = "todo"
result = await execute_on_client(
action="select",
table="tasks",
filters={"dueDateFrom": start_ms, "dueDateTo": end_ms},
filters=filters,
)
rows = result.get("rows", [])
if not rows:
@@ -203,7 +318,6 @@ async def add_task_comment(task_id: str, author: str, content: str) -> str:
)
row = result.get("row", {})
row_author = row.get("author", author)
# Electron payloads can vary (taskId vs task_id). Fall back to input task_id.
row_task_id = row.get("taskId") or row.get("task_id") or task_id
row_comment_id = row.get("id", "unknown")
return f"Comment added by {row_author} on task {row_task_id} (comment id: {row_comment_id})."
@@ -221,6 +335,7 @@ async def delete_task_comment(comment_id: str) -> str:
TASK_TOOLS: list[Any] = [
list_tasks,
count_tasks,
create_task,
update_task,
delete_task,
@@ -232,6 +347,7 @@ TASK_TOOLS: list[Any] = [
TASK_READ_TOOLS: list[Any] = [
list_tasks,
count_tasks,
list_tasks_due_today,
list_task_comments,
]

179
app/agents/timeline_agent.py
View File

@@ -20,19 +20,127 @@ def _is_uuid(value: str) -> bool:
@tool
async def list_timelines(project_id: str = "") -> str:
"""List timelines. Provide project_id to scope to a specific project."""
async def list_timelines(
project_id: str = "",
type: str = "",
is_completed: int = -1,
is_ai_suggested: int = -1,
order_by: str = "",
order_dir: str = "",
date_from: int = -1,
date_to: int = -1,
created_at_from: int = -1,
created_at_to: int = -1,
completed_at_from: int = -1,
completed_at_to: int = -1,
limit: int = 50,
offset: int = 0,
) -> str:
"""List timeline events (milestones, checkpoints, activities) with optional filters.
project_id: UUID to scope results to a specific project.
type: filter by event type — milestone | checkpoint | activity.
is_completed: 0 = incomplete only, 1 = completed only, -1 = any (default).
is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
order_by: sort field — date (default) | createdAt | completedAt.
order_dir: asc (default) | desc.
date_from / date_to: ms epoch range for the event date. Use -1 to omit.
created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
limit: max rows to return (default 50). Use with offset to paginate.
offset: skip first N rows (default 0).
Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
Tip — prefer count_timelines for "how many" questions to avoid listing rows.
Tip — for natural-language windows ("today", "this week", "last month", etc.)
take date_from / date_to verbatim from the DATE CONTEXT block in the system prompt;
do not compute boundaries from the current UTC instant.
"""
normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
result = await execute_on_client(
action="select",
table="timelines",
filters={"projectId": normalized_project_id or None},
)
filters: dict[str, Any] = {
"projectId": normalized_project_id or None,
"orderBy": order_by or None,
"orderDir": order_dir or None,
"limit": limit,
"offset": offset,
}
if type:
filters["type"] = type
if is_completed != -1:
filters["isCompleted"] = is_completed
if is_ai_suggested != -1:
filters["isAiSuggested"] = is_ai_suggested
if date_from != -1:
filters["dateFrom"] = date_from
if date_to != -1:
filters["dateTo"] = date_to
if created_at_from != -1:
filters["createdAtFrom"] = created_at_from
if created_at_to != -1:
filters["createdAtTo"] = created_at_to
if completed_at_from != -1:
filters["completedAtFrom"] = completed_at_from
if completed_at_to != -1:
filters["completedAtTo"] = completed_at_to
result = await execute_on_client(action="select", table="timelines", filters=filters)
rows = result.get("rows", [])
if not rows:
return "No timelines found."
lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
return f"Found {len(rows)} timeline(s):\n" + "\n".join(lines)
return "No timeline events found."
lines = [
f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
f"completed: {bool(r.get('isCompleted'))}, completedAt: {r.get('completedAt')}, id: {r['id']})"
for r in rows
]
return f"Found {len(rows)} timeline event(s):\n" + "\n".join(lines)
@tool
async def count_timelines(
project_id: str = "",
type: str = "",
is_completed: int = -1,
is_ai_suggested: int = -1,
date_from: int = -1,
date_to: int = -1,
created_at_from: int = -1,
created_at_to: int = -1,
completed_at_from: int = -1,
completed_at_to: int = -1,
) -> str:
"""Count timeline events matching the given filters without returning rows.
Use this instead of list_timelines for "how many" questions — it is much cheaper.
Same filter parameters as list_timelines (no limit/offset/order_by needed).
date_from / date_to: ms epoch range for the event date. Use -1 to omit.
completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
Tip — for natural-language windows take date_from / date_to from the DATE CONTEXT block;
do not compute boundaries from the current UTC instant.
"""
normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
filters: dict[str, Any] = {"projectId": normalized_project_id or None}
if type:
filters["type"] = type
if is_completed != -1:
filters["isCompleted"] = is_completed
if is_ai_suggested != -1:
filters["isAiSuggested"] = is_ai_suggested
if date_from != -1:
filters["dateFrom"] = date_from
if date_to != -1:
filters["dateTo"] = date_to
if created_at_from != -1:
filters["createdAtFrom"] = created_at_from
if created_at_to != -1:
filters["createdAtTo"] = created_at_to
if completed_at_from != -1:
filters["completedAtFrom"] = completed_at_from
if completed_at_to != -1:
filters["completedAtTo"] = completed_at_to
result = await execute_on_client(action="count", table="timelines", filters=filters)
return f"Timeline event count: {result.get('count', 0)}"
@tool
@@ -40,13 +148,19 @@ async def create_timeline(
project_id: str,
title: str,
date: int,
type: str = "milestone",
is_completed: int = 0,
is_ai_suggested: int = 0,
) -> str:
"""Create a project timeline (milestone).
"""Create a project timeline event.
project_id: REQUIRED UUID of the parent project
title: descriptive name for the milestone
date: Unix timestamp in milliseconds
title: descriptive name for the event
date: Unix timestamp in milliseconds for the event date
type: milestone (default) | checkpoint | activity
is_completed: 1 if already completed, 0 if not (default 0)
is_ai_suggested: 1 if proactively suggested, 0 if user-requested
completedAt is set automatically when is_completed is 1.
"""
result = await execute_on_client(
action="insert",
@@ -55,11 +169,13 @@ async def create_timeline(
"projectId": project_id,
"title": title,
"date": date,
"type": type,
"isCompleted": is_completed,
"isAiSuggested": is_ai_suggested,
},
)
row = result["row"]
return f"Timeline created: '{row['title']}' (id: {row['id']}, date: {row['date']})"
return f"Timeline event created: '{row['title']}' (id: {row['id']}, date: {row['date']}, type: {row.get('type')})"
@tool
@@ -67,38 +183,47 @@ async def update_timeline(
timeline_id: str,
title: str = "",
date: int = -1,
is_completed: int = -1,
) -> str:
"""Update a timeline. Only pass fields that should change.
timeline_id: UUID of the timeline (required)
"""Update a timeline event. Only pass fields that should change.
timeline_id: UUID of the event (required)
date: -1 means unchanged; any other value sets the new date (ms timestamp)
is_completed: 0 = mark incomplete, 1 = mark complete, -1 = unchanged
completedAt is managed automatically:
- setting is_completed to 1 records the current timestamp
- setting is_completed to 0 clears completedAt
"""
updates: dict[str, Any] = {}
if title:
updates["title"] = title
if date != -1:
updates["date"] = date
if is_completed != -1:
updates["isCompleted"] = is_completed
result = await execute_on_client(
action="update",
table="timelines",
data={"id": timeline_id, "updates": updates},
)
row = result["row"]
return f"Timeline updated: '{row['title']}' (id: {row['id']})"
return f"Timeline event updated: '{row['title']}' (id: {row['id']})"
@tool
async def delete_timeline(timeline_id: str) -> str:
"""Delete a timeline permanently by its UUID."""
"""Delete a timeline event permanently by its UUID."""
await execute_on_client(action="delete", table="timelines", data={"id": timeline_id})
return f"Timeline {timeline_id} deleted."
return f"Timeline event {timeline_id} deleted."
@tool
async def list_timelines_today(user_timezone: str = "UTC") -> str:
"""List all timeline events (milestones) whose date falls on today.
async def list_timelines_today(user_timezone: str = "UTC", include_completed: bool = True) -> str:
"""List all timeline events whose date falls on today.
user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
Always pass the user's timezone so 'today' is computed in their local time.
include_completed: set False to exclude already-completed events (default True).
"""
try:
from zoneinfo import ZoneInfo
@@ -109,20 +234,27 @@ async def list_timelines_today(user_timezone: str = "UTC") -> str:
start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
start_ms = int(start_dt.timestamp() * 1000)
end_ms = start_ms + 86_400_000 - 1
filters: dict[str, Any] = {"dateFrom": start_ms, "dateTo": end_ms}
if not include_completed:
filters["isCompleted"] = 0
result = await execute_on_client(
action="select",
table="timelines",
filters={"dateFrom": start_ms, "dateTo": end_ms},
filters=filters,
)
rows = result.get("rows", [])
if not rows:
return "No timeline events today."
lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
lines = [
f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, completed: {bool(r.get('isCompleted'))}, id: {r['id']})"
for r in rows
]
return f"Timeline events today ({len(rows)}):\n" + "\n".join(lines)
TIMELINE_TOOLS: list[Any] = [
list_timelines,
count_timelines,
list_timelines_today,
create_timeline,
update_timeline,
@@ -131,5 +263,6 @@ TIMELINE_TOOLS: list[Any] = [
TIMELINE_READ_TOOLS: list[Any] = [
list_timelines,
count_timelines,
list_timelines_today,
]

231
app/core/deep_agent.py
View File

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