mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-08-09 12:40:59 +00:00
Junyan Qin
393 lines
14 KiB
Python
393 lines
14 KiB
Python
from __future__ import annotations
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import datetime
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from types import SimpleNamespace
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import pytest
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import sqlalchemy
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from sqlalchemy.ext.asyncio import create_async_engine
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from langbot.pkg.api.http.authz import WorkspaceRequiredError
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from langbot.pkg.api.http.context import ExecutionContext
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from langbot.pkg.api.http.service.monitoring import MonitoringService
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from langbot.pkg.entity.persistence.base import Base
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from langbot.pkg.entity.persistence.monitoring import MonitoringLLMCall, MonitoringMessage
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from langbot.pkg.entity.persistence.workspace import Workspace
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from langbot.pkg.persistence.mgr import PersistenceManager
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pytestmark = pytest.mark.asyncio
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WORKSPACE_A = '00000000-0000-0000-0000-00000000000a'
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WORKSPACE_B = '00000000-0000-0000-0000-00000000000b'
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def _context(workspace_uuid: str) -> ExecutionContext:
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return ExecutionContext(
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instance_uuid='instance',
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workspace_uuid=workspace_uuid,
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placement_generation=3,
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bot_uuid='same-bot',
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pipeline_uuid='same-pipeline',
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)
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class _PersistenceManager:
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def __init__(self, engine):
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self.engine = engine
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async def execute_async(self, *args, **kwargs):
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async with self.engine.connect() as connection:
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result = await connection.execute(*args, **kwargs)
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await connection.commit()
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return result
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def get_db_engine(self):
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return self.engine
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@staticmethod
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def serialize_model(model, data, masked_columns=None):
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return {
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column.name: (
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getattr(data, column.name).isoformat()
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if isinstance(getattr(data, column.name), datetime.datetime)
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else getattr(data, column.name)
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)
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for column in model.__table__.columns
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if column.name not in (masked_columns or [])
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}
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@pytest.fixture
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async def service(tmp_path):
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engine = create_async_engine(f'sqlite+aiosqlite:///{tmp_path / "monitoring.db"}')
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async with engine.begin() as connection:
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await connection.run_sync(Base.metadata.create_all)
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await connection.execute(
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sqlalchemy.insert(Workspace),
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[
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{
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'uuid': WORKSPACE_A,
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'instance_uuid': 'instance',
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'name': 'A',
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'slug': 'a',
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'source': 'cloud_projection',
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},
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{
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'uuid': WORKSPACE_B,
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'instance_uuid': 'instance',
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'name': 'B',
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'slug': 'b',
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'source': 'cloud_projection',
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},
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],
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)
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application = SimpleNamespace(
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persistence_mgr=_PersistenceManager(engine),
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instance_config=SimpleNamespace(data={'database': {'use': 'sqlite'}}),
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)
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yield MonitoringService(application)
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await engine.dispose()
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async def _record_message(service, context, content):
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return await service.record_message(
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context,
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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message_content=content,
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session_id='same-session',
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)
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async def test_monitoring_write_without_execution_context_fails_closed(service):
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with pytest.raises(WorkspaceRequiredError):
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await _record_message(service, None, 'unscoped')
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async def test_same_session_and_resource_ids_do_not_collide(service):
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context_a = _context(WORKSPACE_A)
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context_b = _context(WORKSPACE_B)
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message_a = await _record_message(service, context_a, 'tenant-a')
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message_b = await _record_message(service, context_b, 'tenant-b')
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await service.record_session_start(
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context_a,
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session_id='same-session',
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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)
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await service.record_session_start(
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context_b,
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session_id='same-session',
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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)
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messages_a, total_a = await service.get_messages(context_a)
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messages_b, total_b = await service.get_messages(context_b)
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assert total_a == total_b == 1
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assert messages_a[0]['message_content'] == 'tenant-a'
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assert messages_b[0]['message_content'] == 'tenant-b'
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assert (await service.get_message_details(context_b, message_a))['found'] is False
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assert (await service.get_message_details(context_a, message_b))['found'] is False
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async def test_tool_call_inherits_context_from_connection_message_row(service):
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context = _context(WORKSPACE_A)
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message_id = await _record_message(service, context, 'tool context')
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await service.record_tool_call(
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context,
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tool_name='search',
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tool_source='native',
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duration=12,
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message_id=message_id,
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)
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tool_calls, total = await service.get_tool_calls(context)
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assert total == 1
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assert tool_calls[0]['bot_id'] == 'same-bot'
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assert tool_calls[0]['pipeline_id'] == 'same-pipeline'
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assert tool_calls[0]['session_id'] == 'same-session'
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assert tool_calls[0]['message_id'] == message_id
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async def test_feedback_upsert_and_cancel_are_workspace_scoped(service):
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context_a = _context(WORKSPACE_A)
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context_b = _context(WORKSPACE_B)
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await service.record_feedback(context_a, feedback_id='same-feedback', feedback_type=1)
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await service.record_feedback(context_b, feedback_id='same-feedback', feedback_type=2)
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stats_a = await service.get_feedback_stats(context_a)
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stats_b = await service.get_feedback_stats(context_b)
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assert stats_a['total_likes'] == 1
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assert stats_a['total_dislikes'] == 0
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assert stats_b['total_likes'] == 0
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assert stats_b['total_dislikes'] == 1
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await service.record_feedback(context_a, feedback_id='same-feedback', feedback_type=3)
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assert (await service.get_feedback_stats(context_a))['total_feedback'] == 0
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assert (await service.get_feedback_stats(context_b))['total_feedback'] == 1
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async def test_monitoring_queries_and_detail_views_are_strictly_bounded(service):
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context = _context(WORKSPACE_A)
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service.ap.instance_config.data['monitoring'] = {
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'query_limits': {
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'page_rows': 2,
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'export_rows': 2,
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'detail_rows': 2,
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'timeseries_buckets': 2,
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'max_offset': 10,
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}
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}
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await service.record_session_start(
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context,
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session_id='same-session',
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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)
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message_ids = [await _record_message(service, context, f'message-{index}') for index in range(4)]
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for index in range(3):
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await service.record_llm_call(
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context,
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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session_id='same-session',
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model_name='model',
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input_tokens=1,
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output_tokens=2,
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duration=10,
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message_id=message_ids[0],
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)
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await service.record_tool_call(
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context,
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tool_name=f'tool-{index}',
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tool_source='native',
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duration=5,
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session_id='same-session',
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message_id=message_ids[0],
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)
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await service.record_error(
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context,
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bot_id='same-bot',
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bot_name='Same Bot',
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pipeline_id='same-pipeline',
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pipeline_name='Same Pipeline',
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error_type='Failure',
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error_message=f'error-{index}',
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session_id='same-session',
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message_id=message_ids[0],
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)
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page, total = await service.get_messages(context, limit=100000, offset=-5)
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exported = await service.export_messages(context, limit=100000)
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session_detail = await service.get_session_analysis(context, 'same-session')
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message_detail = await service.get_message_details(context, message_ids[0])
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assert total == 4
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assert len(page) == 2
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assert len(exported) == 2
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assert session_detail['message_stats']['total'] == 4
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assert session_detail['llm_stats']['total_calls'] == 3
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assert session_detail['tool_stats']['total_calls'] == 3
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assert len(session_detail['tool_calls']) == 2
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assert len(session_detail['errors']) == 2
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assert session_detail['detail_truncated'] == {
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'tool_calls': True,
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'errors': True,
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}
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assert message_detail['llm_stats']['total_calls'] == 3
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assert len(message_detail['llm_calls']) == 2
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assert len(message_detail['errors']) == 2
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assert message_detail['detail_truncated'] == {
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'llm_calls': True,
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'errors': True,
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}
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service.ap.instance_config.data['monitoring']['query_limits'] = {
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'page_rows': 999999,
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'export_rows': 999999,
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'detail_rows': 999999,
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'timeseries_buckets': 999999,
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'max_offset': 99999999,
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}
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assert service.normalize_page_window(999999, 99999999) == (5000, 10000000)
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assert service.normalize_export_limit(999999) == 50000
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assert service._detail_limit() == 10000
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assert service._timeseries_bucket_limit() == 10000
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async def test_token_statistics_aggregate_and_limit_groups_in_database(service):
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context = _context(WORKSPACE_A)
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service.ap.instance_config.data['monitoring'] = {
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'query_limits': {
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'page_rows': 1,
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'timeseries_buckets': 2,
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}
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}
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first_hour = datetime.datetime(2026, 7, 28, 10, 0)
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rows = [
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{
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'id': f'llm-{index}',
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'workspace_uuid': WORKSPACE_A,
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'timestamp': first_hour + datetime.timedelta(hours=hour, minutes=index),
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'model_name': model,
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'input_tokens': input_tokens,
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'output_tokens': output_tokens,
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'total_tokens': input_tokens + output_tokens,
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'duration': 100,
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'cost': 0.01,
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'status': 'success',
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'bot_id': 'same-bot',
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'bot_name': 'Same Bot',
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'pipeline_id': 'same-pipeline',
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'pipeline_name': 'Same Pipeline',
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'session_id': 'same-session',
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}
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for index, (hour, model, input_tokens, output_tokens) in enumerate(
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[
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(0, 'small-model', 1, 2),
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(1, 'large-model', 3, 4),
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(2, 'large-model', 5, 6),
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(2, 'large-model', 7, 8),
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]
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)
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]
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await service.ap.persistence_mgr.execute_async(sqlalchemy.insert(MonitoringLLMCall), rows)
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stats = await service.get_token_statistics(context, bucket='hour')
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assert stats['summary']['total_calls'] == 4
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assert stats['summary']['total_tokens'] == 36
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assert stats['by_model_truncated'] is True
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assert [model['model_name'] for model in stats['by_model']] == ['large-model']
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assert stats['timeseries_truncated'] is True
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assert stats['timeseries'] == [
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{
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'bucket': '2026-07-28 11:00',
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'input_tokens': 3,
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'output_tokens': 4,
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'total_tokens': 7,
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'calls': 1,
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},
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{
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'bucket': '2026-07-28 12:00',
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'input_tokens': 12,
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'output_tokens': 14,
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'total_tokens': 26,
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'calls': 2,
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},
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]
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async def test_cleanup_commits_sqlite_delete_before_vacuum(tmp_path):
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engine = create_async_engine(
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f'sqlite+aiosqlite:///{tmp_path / "monitoring-cleanup.db"}',
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connect_args={'timeout': 0.1},
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)
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application = SimpleNamespace(
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instance_config=SimpleNamespace(data={'database': {'use': 'sqlite'}}),
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)
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manager = PersistenceManager(application)
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manager.db = SimpleNamespace(get_engine=lambda: engine)
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application.persistence_mgr = manager
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try:
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async with engine.begin() as connection:
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await connection.run_sync(Base.metadata.create_all)
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await connection.execute(
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sqlalchemy.insert(Workspace).values(
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uuid=WORKSPACE_A,
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instance_uuid='instance',
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name='A',
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slug='a',
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source='cloud_projection',
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)
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)
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await connection.execute(
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sqlalchemy.insert(MonitoringMessage),
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[
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{
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'id': f'expired-message-{index}',
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'workspace_uuid': WORKSPACE_A,
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'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
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- datetime.timedelta(days=30),
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'bot_id': 'bot',
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'bot_name': 'Bot',
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'pipeline_id': 'pipeline',
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'pipeline_name': 'Pipeline',
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'message_content': 'expired',
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'session_id': 'session',
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'status': 'success',
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'level': 'info',
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}
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for index in range(5)
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],
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)
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deleted = await MonitoringService(application).cleanup_expired_records(
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_context(WORKSPACE_A),
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retention_days=1,
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batch_size=2,
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max_batches_per_table=1,
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)
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assert deleted['monitoring_messages'] == 2
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async with engine.connect() as connection:
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remaining = await connection.scalar(
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sqlalchemy.select(sqlalchemy.func.count()).select_from(MonitoringMessage)
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)
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assert remaining == 3
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finally:
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await engine.dispose()
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