From 5d9f6ec7635e2095de8e9ecb3448b8d517f21d60 Mon Sep 17 00:00:00 2001 From: Guanchao Wang Date: Mon, 26 Jan 2026 21:08:23 +0800 Subject: [PATCH] Feat/monitor (#1928) * feat: add monitor * feat: fix tab * feat: work * feat: not reliable monitor * feat: enhance monitoring page layout with integrated filters and refresh button * feat: add support for runner recording * feat: add jump button & alignment * feat: new * fix: not show query variables in local agent * fix: pnpm lint and python ruff check * fix: ruff fromat * chore: remove unnecessary migration * style: optimize monitoring page layout and fix sticky filter issues - Enhanced metric cards with gradient backgrounds and hover effects - Increased traffic chart height from 200px to 300px - Adjusted grid layout and spacing for better visual appeal - Fixed sticky filter area to properly cover parent padding without transparent gaps - Used negative margins and positioning to eliminate scrolling artifacts - Matched padding/margins with other pages (pipelines, bots) for consistency - Removed duplicate title/subtitle from page content - Added cursor-pointer styling to tab triggers - Removed border between tab list and tab content Co-Authored-By: Claude Sonnet 4.5 * fix: apply prettier formatting to monitoring components - Fixed indentation and spacing in MetricCard.tsx - Fixed formatting in TrafficChart.tsx - Applied prettier formatting to page.tsx Co-Authored-By: Claude Sonnet 4.5 * feat: update HomeSidebar to trigger action on child selection and localize monitoring titles * refactor: streamline LLM and embedding invocation methods * feat: add embedding model monitor * fix: database version * chore: simplify pnpm-lock.yaml formatting --------- Co-authored-by: Junyan Qin Co-authored-by: Claude Sonnet 4.5 --- .../api/http/controller/groups/monitoring.py | 325 ++ src/langbot/pkg/api/http/service/model.py | 4 +- .../pkg/api/http/service/monitoring.py | 796 +++ src/langbot/pkg/core/app.py | 3 + src/langbot/pkg/core/stages/build_app.py | 4 + .../pkg/entity/persistence/monitoring.py | 105 + src/langbot/pkg/pipeline/monitoring_helper.py | 270 + src/langbot/pkg/pipeline/pipelinemgr.py | 110 +- src/langbot/pkg/plugin/handler.py | 2 +- .../pkg/provider/modelmgr/requester.py | 217 +- .../provider/modelmgr/requesters/chatcmpl.py | 52 +- .../pkg/provider/runners/localagent.py | 8 +- .../pkg/rag/knowledge/services/embedder.py | 4 +- .../pkg/rag/knowledge/services/retriever.py | 5 +- web/package.json | 1 + web/pnpm-lock.yaml | 4398 +++++------------ .../components/bot-log/view/BotLogCard.tsx | 98 +- .../bot-log/view/BotLogListComponent.tsx | 13 +- .../components/home-sidebar/HomeSidebar.tsx | 1 + .../home-sidebar/sidbarConfigList.tsx | 20 + .../components/MessageContentRenderer.tsx | 230 + .../components/MessageDetailsCard.tsx | 292 ++ .../components/filters/MonitoringFilters.tsx | 209 + .../components/overview-cards/MetricCard.tsx | 93 + .../overview-cards/OverviewCards.tsx | 135 + .../overview-cards/TrafficChart.tsx | 263 + .../monitoring/hooks/useMonitoringData.ts | 352 ++ .../monitoring/hooks/useMonitoringFilters.ts | 65 + web/src/app/home/monitoring/page.tsx | 817 +++ .../app/home/monitoring/types/monitoring.ts | 180 + .../app/home/monitoring/utils/dateUtils.ts | 99 + .../home/pipelines/PipelineDetailDialog.tsx | 60 +- web/src/app/infra/http/BackendClient.ts | 146 + web/src/i18n/locales/en-US.ts | 138 + web/src/i18n/locales/ja-JP.ts | 117 + web/src/i18n/locales/zh-Hans.ts | 140 +- web/src/i18n/locales/zh-Hant.ts | 116 + 37 files changed, 6706 insertions(+), 3182 deletions(-) create mode 100644 src/langbot/pkg/api/http/controller/groups/monitoring.py create mode 100644 src/langbot/pkg/api/http/service/monitoring.py create mode 100644 src/langbot/pkg/entity/persistence/monitoring.py create mode 100644 src/langbot/pkg/pipeline/monitoring_helper.py create mode 100644 web/src/app/home/monitoring/components/MessageContentRenderer.tsx create mode 100644 web/src/app/home/monitoring/components/MessageDetailsCard.tsx create mode 100644 web/src/app/home/monitoring/components/filters/MonitoringFilters.tsx create mode 100644 web/src/app/home/monitoring/components/overview-cards/MetricCard.tsx create mode 100644 web/src/app/home/monitoring/components/overview-cards/OverviewCards.tsx create mode 100644 web/src/app/home/monitoring/components/overview-cards/TrafficChart.tsx create mode 100644 web/src/app/home/monitoring/hooks/useMonitoringData.ts create mode 100644 web/src/app/home/monitoring/hooks/useMonitoringFilters.ts create mode 100644 web/src/app/home/monitoring/page.tsx create mode 100644 web/src/app/home/monitoring/types/monitoring.ts create mode 100644 web/src/app/home/monitoring/utils/dateUtils.ts diff --git a/src/langbot/pkg/api/http/controller/groups/monitoring.py b/src/langbot/pkg/api/http/controller/groups/monitoring.py new file mode 100644 index 00000000..a1964a4b --- /dev/null +++ b/src/langbot/pkg/api/http/controller/groups/monitoring.py @@ -0,0 +1,325 @@ +from __future__ import annotations + +import datetime +import quart + +from .. import group + + +def parse_iso_datetime(datetime_str: str | None) -> datetime.datetime | None: + """Parse ISO 8601 datetime string, handling 'Z' suffix for UTC timezone""" + if not datetime_str: + return None + # Replace 'Z' with '+00:00' for Python 3.10 compatibility + if datetime_str.endswith('Z'): + datetime_str = datetime_str[:-1] + '+00:00' + dt = datetime.datetime.fromisoformat(datetime_str) + # Convert to UTC and remove timezone info to match database storage (which stores UTC as naive datetime) + if dt.tzinfo is not None: + # Convert to UTC and remove timezone info + dt = dt.astimezone(datetime.timezone.utc).replace(tzinfo=None) + return dt + + +@group.group_class('monitoring', '/api/v1/monitoring') +class MonitoringRouterGroup(group.RouterGroup): + async def initialize(self) -> None: + @self.route('/overview', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_overview() -> str: + """Get overview metrics""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + metrics = await self.ap.monitoring_service.get_overview_metrics( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + ) + + return self.success(data=metrics) + + @self.route('/messages', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_messages() -> str: + """Get message logs""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + limit = int(quart.request.args.get('limit', 100)) + offset = int(quart.request.args.get('offset', 0)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + messages, total = await self.ap.monitoring_service.get_messages( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=offset, + ) + + return self.success( + data={ + 'messages': messages, + 'total': total, + 'limit': limit, + 'offset': offset, + } + ) + + @self.route('/llm-calls', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_llm_calls() -> str: + """Get LLM call records""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + limit = int(quart.request.args.get('limit', 100)) + offset = int(quart.request.args.get('offset', 0)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + llm_calls, total = await self.ap.monitoring_service.get_llm_calls( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=offset, + ) + + return self.success( + data={ + 'llm_calls': llm_calls, + 'total': total, + 'limit': limit, + 'offset': offset, + } + ) + + @self.route('/embedding-calls', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_embedding_calls() -> str: + """Get embedding call records""" + # Parse query parameters + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + knowledge_base_id = quart.request.args.get('knowledgeBaseId') + limit = int(quart.request.args.get('limit', 100)) + offset = int(quart.request.args.get('offset', 0)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + embedding_calls, total = await self.ap.monitoring_service.get_embedding_calls( + start_time=start_time, + end_time=end_time, + knowledge_base_id=knowledge_base_id if knowledge_base_id else None, + limit=limit, + offset=offset, + ) + + return self.success( + data={ + 'embedding_calls': embedding_calls, + 'total': total, + 'limit': limit, + 'offset': offset, + } + ) + + @self.route('/sessions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_sessions() -> str: + """Get session information""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + is_active_str = quart.request.args.get('isActive') + limit = int(quart.request.args.get('limit', 100)) + offset = int(quart.request.args.get('offset', 0)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + # Parse is_active + is_active = None + if is_active_str: + is_active = is_active_str.lower() == 'true' + + sessions, total = await self.ap.monitoring_service.get_sessions( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + is_active=is_active, + limit=limit, + offset=offset, + ) + + return self.success( + data={ + 'sessions': sessions, + 'total': total, + 'limit': limit, + 'offset': offset, + } + ) + + @self.route('/errors', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_errors() -> str: + """Get error logs""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + limit = int(quart.request.args.get('limit', 100)) + offset = int(quart.request.args.get('offset', 0)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + errors, total = await self.ap.monitoring_service.get_errors( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=offset, + ) + + return self.success( + data={ + 'errors': errors, + 'total': total, + 'limit': limit, + 'offset': offset, + } + ) + + @self.route('/data', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_all_data() -> str: + """Get all monitoring data in a single request""" + # Parse query parameters + bot_ids = quart.request.args.getlist('botId') + pipeline_ids = quart.request.args.getlist('pipelineId') + start_time_str = quart.request.args.get('startTime') + end_time_str = quart.request.args.get('endTime') + limit = int(quart.request.args.get('limit', 50)) + + # Parse datetime + start_time = parse_iso_datetime(start_time_str) + end_time = parse_iso_datetime(end_time_str) + + # Get overview metrics + overview = await self.ap.monitoring_service.get_overview_metrics( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + ) + + # Get messages + messages, messages_total = await self.ap.monitoring_service.get_messages( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=0, + ) + + # Get LLM calls + llm_calls, llm_calls_total = await self.ap.monitoring_service.get_llm_calls( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=0, + ) + + # Get sessions + sessions, sessions_total = await self.ap.monitoring_service.get_sessions( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + is_active=None, + limit=limit, + offset=0, + ) + + # Get errors + errors, errors_total = await self.ap.monitoring_service.get_errors( + bot_ids=bot_ids if bot_ids else None, + pipeline_ids=pipeline_ids if pipeline_ids else None, + start_time=start_time, + end_time=end_time, + limit=limit, + offset=0, + ) + + # Get embedding calls + embedding_calls, embedding_calls_total = await self.ap.monitoring_service.get_embedding_calls( + start_time=start_time, + end_time=end_time, + limit=limit, + offset=0, + ) + + return self.success( + data={ + 'overview': overview, + 'messages': messages, + 'llmCalls': llm_calls, + 'embeddingCalls': embedding_calls, + 'sessions': sessions, + 'errors': errors, + 'totalCount': { + 'messages': messages_total, + 'llmCalls': llm_calls_total, + 'embeddingCalls': embedding_calls_total, + 'sessions': sessions_total, + 'errors': errors_total, + }, + } + ) + + @self.route('/sessions//analysis', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_session_analysis(session_id: str) -> str: + """Get detailed analysis for a specific session""" + analysis = await self.ap.monitoring_service.get_session_analysis(session_id) + + # Always return success with the analysis data + # The frontend will handle the 'found: false' case + return self.success(data=analysis) + + @self.route('/messages//details', methods=['GET'], auth_type=group.AuthType.USER_TOKEN) + async def get_message_details(message_id: str) -> str: + """Get detailed information for a specific message""" + details = await self.ap.monitoring_service.get_message_details(message_id) + + if not details.get('found'): + return self.error(message=f'Message {message_id} not found', code=404) + + return self.success(data=details) diff --git a/src/langbot/pkg/api/http/service/model.py b/src/langbot/pkg/api/http/service/model.py index d6250ff6..e3184de4 100644 --- a/src/langbot/pkg/api/http/service/model.py +++ b/src/langbot/pkg/api/http/service/model.py @@ -192,7 +192,7 @@ class LLMModelsService: runtime_llm_model = await self.ap.model_mgr.init_temporary_runtime_llm_model(model_data) extra_args = model_data.get('extra_args', {}) - await runtime_llm_model.provider.requester.invoke_llm( + await runtime_llm_model.provider.invoke_llm( query=None, model=runtime_llm_model, messages=[provider_message.Message(role='user', content='Hello, world! Please just reply a "Hello".')], @@ -354,7 +354,7 @@ class EmbeddingModelsService: else: runtime_embedding_model = await self.ap.model_mgr.init_temporary_runtime_embedding_model(model_data) - await runtime_embedding_model.provider.requester.invoke_embedding( + await runtime_embedding_model.provider.invoke_embedding( model=runtime_embedding_model, input_text=['Hello, world!'], extra_args={}, diff --git a/src/langbot/pkg/api/http/service/monitoring.py b/src/langbot/pkg/api/http/service/monitoring.py new file mode 100644 index 00000000..abd3e510 --- /dev/null +++ b/src/langbot/pkg/api/http/service/monitoring.py @@ -0,0 +1,796 @@ +from __future__ import annotations + +import uuid +import datetime +import sqlalchemy + +from ....core import app +from ....entity.persistence import monitoring as persistence_monitoring + + +class MonitoringService: + """Monitoring service""" + + ap: app.Application + + def __init__(self, ap: app.Application) -> None: + self.ap = ap + + # ========== Recording Methods ========== + + async def record_message( + self, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + message_content: str, + session_id: str, + status: str = 'success', + level: str = 'info', + platform: str | None = None, + user_id: str | None = None, + runner_name: str | None = None, + variables: str | None = None, + ) -> str: + """Record a message""" + message_id = str(uuid.uuid4()) + message_data = { + 'id': message_id, + 'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'bot_id': bot_id, + 'bot_name': bot_name, + 'pipeline_id': pipeline_id, + 'pipeline_name': pipeline_name, + 'message_content': message_content, + 'session_id': session_id, + 'status': status, + 'level': level, + 'platform': platform, + 'user_id': user_id, + 'runner_name': runner_name, + 'variables': variables, + } + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.insert(persistence_monitoring.MonitoringMessage).values(message_data) + ) + + return message_id + + async def record_llm_call( + self, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + session_id: str, + model_name: str, + input_tokens: int, + output_tokens: int, + duration: int, + status: str = 'success', + cost: float | None = None, + error_message: str | None = None, + message_id: str | None = None, + ) -> str: + """Record an LLM call""" + call_id = str(uuid.uuid4()) + call_data = { + 'id': call_id, + 'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'model_name': model_name, + 'input_tokens': input_tokens, + 'output_tokens': output_tokens, + 'total_tokens': input_tokens + output_tokens, + 'duration': duration, + 'cost': cost, + 'status': status, + 'bot_id': bot_id, + 'bot_name': bot_name, + 'pipeline_id': pipeline_id, + 'pipeline_name': pipeline_name, + 'session_id': session_id, + 'error_message': error_message, + 'message_id': message_id, + } + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.insert(persistence_monitoring.MonitoringLLMCall).values(call_data) + ) + + return call_id + + async def record_embedding_call( + self, + model_name: str, + prompt_tokens: int, + total_tokens: int, + duration: int, + input_count: int, + status: str = 'success', + error_message: str | None = None, + knowledge_base_id: str | None = None, + query_text: str | None = None, + session_id: str | None = None, + message_id: str | None = None, + call_type: str | None = None, + ) -> str: + """Record an embedding call""" + call_id = str(uuid.uuid4()) + call_data = { + 'id': call_id, + 'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'model_name': model_name, + 'prompt_tokens': prompt_tokens, + 'total_tokens': total_tokens, + 'duration': duration, + 'input_count': input_count, + 'status': status, + 'error_message': error_message, + 'knowledge_base_id': knowledge_base_id, + 'query_text': query_text, + 'session_id': session_id, + 'message_id': message_id, + 'call_type': call_type, + } + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.insert(persistence_monitoring.MonitoringEmbeddingCall).values(call_data) + ) + + return call_id + + async def record_session_start( + self, + session_id: str, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + platform: str | None = None, + user_id: str | None = None, + ) -> None: + """Record a new session""" + session_data = { + 'session_id': session_id, + 'bot_id': bot_id, + 'bot_name': bot_name, + 'pipeline_id': pipeline_id, + 'pipeline_name': pipeline_name, + 'message_count': 0, + 'start_time': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'last_activity': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'is_active': True, + 'platform': platform, + 'user_id': user_id, + } + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.insert(persistence_monitoring.MonitoringSession).values(session_data) + ) + + async def update_session_activity( + self, + session_id: str, + pipeline_id: str | None = None, + pipeline_name: str | None = None, + ) -> bool: + """Update session last activity time and increment message count. + + Also updates pipeline info if the bot's pipeline has changed. + + Returns: + True if session was found and updated, False if session doesn't exist. + """ + update_values = { + 'last_activity': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'message_count': persistence_monitoring.MonitoringSession.message_count + 1, + } + + # Update pipeline info if provided (handles pipeline switch) + if pipeline_id is not None: + update_values['pipeline_id'] = pipeline_id + if pipeline_name is not None: + update_values['pipeline_name'] = pipeline_name + + result = await self.ap.persistence_mgr.execute_async( + sqlalchemy.update(persistence_monitoring.MonitoringSession) + .where(persistence_monitoring.MonitoringSession.session_id == session_id) + .values(update_values) + ) + # Check if any rows were updated + return result.rowcount > 0 + + async def record_error( + self, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + error_type: str, + error_message: str, + session_id: str | None = None, + stack_trace: str | None = None, + message_id: str | None = None, + ) -> str: + """Record an error""" + error_id = str(uuid.uuid4()) + error_data = { + 'id': error_id, + 'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), + 'error_type': error_type, + 'error_message': error_message, + 'bot_id': bot_id, + 'bot_name': bot_name, + 'pipeline_id': pipeline_id, + 'pipeline_name': pipeline_name, + 'session_id': session_id, + 'stack_trace': stack_trace, + 'message_id': message_id, + } + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.insert(persistence_monitoring.MonitoringError).values(error_data) + ) + + return error_id + + async def update_message_status( + self, + message_id: str, + status: str, + level: str | None = None, + variables: str | None = None, + ) -> None: + """Update message status and optionally variables""" + update_values = {'status': status} + if level is not None: + update_values['level'] = level + if variables is not None: + update_values['variables'] = variables + + await self.ap.persistence_mgr.execute_async( + sqlalchemy.update(persistence_monitoring.MonitoringMessage) + .where(persistence_monitoring.MonitoringMessage.id == message_id) + .values(update_values) + ) + + # ========== Query Methods ========== + + async def get_overview_metrics( + self, + bot_ids: list[str] | None = None, + pipeline_ids: list[str] | None = None, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + ) -> dict: + """Get overview metrics""" + # Build base query conditions + message_conditions = [] + llm_conditions = [] + embedding_conditions = [] + session_conditions = [] + + if bot_ids: + message_conditions.append(persistence_monitoring.MonitoringMessage.bot_id.in_(bot_ids)) + llm_conditions.append(persistence_monitoring.MonitoringLLMCall.bot_id.in_(bot_ids)) + session_conditions.append(persistence_monitoring.MonitoringSession.bot_id.in_(bot_ids)) + + if pipeline_ids: + message_conditions.append(persistence_monitoring.MonitoringMessage.pipeline_id.in_(pipeline_ids)) + llm_conditions.append(persistence_monitoring.MonitoringLLMCall.pipeline_id.in_(pipeline_ids)) + session_conditions.append(persistence_monitoring.MonitoringSession.pipeline_id.in_(pipeline_ids)) + + if start_time: + message_conditions.append(persistence_monitoring.MonitoringMessage.timestamp >= start_time) + llm_conditions.append(persistence_monitoring.MonitoringLLMCall.timestamp >= start_time) + embedding_conditions.append(persistence_monitoring.MonitoringEmbeddingCall.timestamp >= start_time) + session_conditions.append(persistence_monitoring.MonitoringSession.start_time >= start_time) + + if end_time: + message_conditions.append(persistence_monitoring.MonitoringMessage.timestamp <= end_time) + llm_conditions.append(persistence_monitoring.MonitoringLLMCall.timestamp <= end_time) + embedding_conditions.append(persistence_monitoring.MonitoringEmbeddingCall.timestamp <= end_time) + session_conditions.append(persistence_monitoring.MonitoringSession.start_time <= end_time) + + # Total messages + message_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringMessage.id)) + if message_conditions: + message_query = message_query.where(sqlalchemy.and_(*message_conditions)) + + total_messages_result = await self.ap.persistence_mgr.execute_async(message_query) + total_messages = total_messages_result.scalar() or 0 + + # Total LLM calls + llm_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringLLMCall.id)) + if llm_conditions: + llm_query = llm_query.where(sqlalchemy.and_(*llm_conditions)) + + llm_calls_result = await self.ap.persistence_mgr.execute_async(llm_query) + llm_calls = llm_calls_result.scalar() or 0 + + # Total Embedding calls + embedding_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringEmbeddingCall.id)) + if embedding_conditions: + embedding_query = embedding_query.where(sqlalchemy.and_(*embedding_conditions)) + + embedding_calls_result = await self.ap.persistence_mgr.execute_async(embedding_query) + embedding_calls = embedding_calls_result.scalar() or 0 + + # Total model calls (LLM + Embedding) + model_calls = llm_calls + embedding_calls + + # Success rate (based on messages) + success_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringMessage.id)).where( + persistence_monitoring.MonitoringMessage.status == 'success' + ) + if message_conditions: + success_query = success_query.where(sqlalchemy.and_(*message_conditions)) + + success_result = await self.ap.persistence_mgr.execute_async(success_query) + success_count = success_result.scalar() or 0 + success_rate = (success_count / total_messages * 100) if total_messages > 0 else 100 + + # Active sessions + active_session_query = sqlalchemy.select( + sqlalchemy.func.count(persistence_monitoring.MonitoringSession.session_id) + ).where(persistence_monitoring.MonitoringSession.is_active == True) + if session_conditions: + active_session_query = active_session_query.where(sqlalchemy.and_(*session_conditions)) + + active_sessions_result = await self.ap.persistence_mgr.execute_async(active_session_query) + active_sessions = active_sessions_result.scalar() or 0 + + return { + 'total_messages': total_messages, + 'llm_calls': llm_calls, + 'embedding_calls': embedding_calls, + 'model_calls': model_calls, + 'success_rate': round(success_rate, 2), + 'active_sessions': active_sessions, + } + + async def get_messages( + self, + bot_ids: list[str] | None = None, + pipeline_ids: list[str] | None = None, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + limit: int = 100, + offset: int = 0, + ) -> tuple[list[dict], int]: + """Get messages with filters""" + conditions = [] + + if bot_ids: + conditions.append(persistence_monitoring.MonitoringMessage.bot_id.in_(bot_ids)) + if pipeline_ids: + conditions.append(persistence_monitoring.MonitoringMessage.pipeline_id.in_(pipeline_ids)) + if start_time: + conditions.append(persistence_monitoring.MonitoringMessage.timestamp >= start_time) + if end_time: + conditions.append(persistence_monitoring.MonitoringMessage.timestamp <= end_time) + + # Get total count + count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringMessage.id)) + if conditions: + count_query = count_query.where(sqlalchemy.and_(*conditions)) + + count_result = await self.ap.persistence_mgr.execute_async(count_query) + total = count_result.scalar() or 0 + + # Get messages + query = sqlalchemy.select(persistence_monitoring.MonitoringMessage).order_by( + persistence_monitoring.MonitoringMessage.timestamp.desc() + ) + if conditions: + query = query.where(sqlalchemy.and_(*conditions)) + + query = query.limit(limit).offset(offset) + + result = await self.ap.persistence_mgr.execute_async(query) + messages_rows = result.all() + + serialized = [] + for row in messages_rows: + # Extract model instance from Row (SQLAlchemy returns Row objects) + msg = row[0] if isinstance(row, tuple) else row + serialized_msg = self.ap.persistence_mgr.serialize_model(persistence_monitoring.MonitoringMessage, msg) + serialized.append(serialized_msg) + + return (serialized, total) + + async def get_llm_calls( + self, + bot_ids: list[str] | None = None, + pipeline_ids: list[str] | None = None, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + limit: int = 100, + offset: int = 0, + ) -> tuple[list[dict], int]: + """Get LLM calls with filters""" + conditions = [] + + if bot_ids: + conditions.append(persistence_monitoring.MonitoringLLMCall.bot_id.in_(bot_ids)) + if pipeline_ids: + conditions.append(persistence_monitoring.MonitoringLLMCall.pipeline_id.in_(pipeline_ids)) + if start_time: + conditions.append(persistence_monitoring.MonitoringLLMCall.timestamp >= start_time) + if end_time: + conditions.append(persistence_monitoring.MonitoringLLMCall.timestamp <= end_time) + + # Get total count + count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringLLMCall.id)) + if conditions: + count_query = count_query.where(sqlalchemy.and_(*conditions)) + + count_result = await self.ap.persistence_mgr.execute_async(count_query) + total = count_result.scalar() or 0 + + # Get LLM calls + query = sqlalchemy.select(persistence_monitoring.MonitoringLLMCall).order_by( + persistence_monitoring.MonitoringLLMCall.timestamp.desc() + ) + if conditions: + query = query.where(sqlalchemy.and_(*conditions)) + + query = query.limit(limit).offset(offset) + + result = await self.ap.persistence_mgr.execute_async(query) + llm_calls_rows = result.all() + + return ( + [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringLLMCall, row[0] if isinstance(row, tuple) else row + ) + for row in llm_calls_rows + ], + total, + ) + + async def get_embedding_calls( + self, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + knowledge_base_id: str | None = None, + limit: int = 100, + offset: int = 0, + ) -> tuple[list[dict], int]: + """Get embedding calls with filters""" + conditions = [] + + if start_time: + conditions.append(persistence_monitoring.MonitoringEmbeddingCall.timestamp >= start_time) + if end_time: + conditions.append(persistence_monitoring.MonitoringEmbeddingCall.timestamp <= end_time) + if knowledge_base_id: + conditions.append(persistence_monitoring.MonitoringEmbeddingCall.knowledge_base_id == knowledge_base_id) + + # Get total count + count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringEmbeddingCall.id)) + if conditions: + count_query = count_query.where(sqlalchemy.and_(*conditions)) + + count_result = await self.ap.persistence_mgr.execute_async(count_query) + total = count_result.scalar() or 0 + + # Get embedding calls + query = sqlalchemy.select(persistence_monitoring.MonitoringEmbeddingCall).order_by( + persistence_monitoring.MonitoringEmbeddingCall.timestamp.desc() + ) + if conditions: + query = query.where(sqlalchemy.and_(*conditions)) + + query = query.limit(limit).offset(offset) + + result = await self.ap.persistence_mgr.execute_async(query) + embedding_calls_rows = result.all() + + return ( + [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringEmbeddingCall, row[0] if isinstance(row, tuple) else row + ) + for row in embedding_calls_rows + ], + total, + ) + + async def get_sessions( + self, + bot_ids: list[str] | None = None, + pipeline_ids: list[str] | None = None, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + is_active: bool | None = None, + limit: int = 100, + offset: int = 0, + ) -> tuple[list[dict], int]: + """Get sessions with filters""" + conditions = [] + + if bot_ids: + conditions.append(persistence_monitoring.MonitoringSession.bot_id.in_(bot_ids)) + if pipeline_ids: + conditions.append(persistence_monitoring.MonitoringSession.pipeline_id.in_(pipeline_ids)) + if start_time: + conditions.append(persistence_monitoring.MonitoringSession.start_time >= start_time) + if end_time: + conditions.append(persistence_monitoring.MonitoringSession.start_time <= end_time) + if is_active is not None: + conditions.append(persistence_monitoring.MonitoringSession.is_active == is_active) + + # Get total count + count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringSession.session_id)) + if conditions: + count_query = count_query.where(sqlalchemy.and_(*conditions)) + + count_result = await self.ap.persistence_mgr.execute_async(count_query) + total = count_result.scalar() or 0 + + # Get sessions + query = sqlalchemy.select(persistence_monitoring.MonitoringSession).order_by( + persistence_monitoring.MonitoringSession.last_activity.desc() + ) + if conditions: + query = query.where(sqlalchemy.and_(*conditions)) + + query = query.limit(limit).offset(offset) + + result = await self.ap.persistence_mgr.execute_async(query) + sessions_rows = result.all() + + return ( + [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringSession, row[0] if isinstance(row, tuple) else row + ) + for row in sessions_rows + ], + total, + ) + + async def get_errors( + self, + bot_ids: list[str] | None = None, + pipeline_ids: list[str] | None = None, + start_time: datetime.datetime | None = None, + end_time: datetime.datetime | None = None, + limit: int = 100, + offset: int = 0, + ) -> tuple[list[dict], int]: + """Get errors with filters""" + conditions = [] + + if bot_ids: + conditions.append(persistence_monitoring.MonitoringError.bot_id.in_(bot_ids)) + if pipeline_ids: + conditions.append(persistence_monitoring.MonitoringError.pipeline_id.in_(pipeline_ids)) + if start_time: + conditions.append(persistence_monitoring.MonitoringError.timestamp >= start_time) + if end_time: + conditions.append(persistence_monitoring.MonitoringError.timestamp <= end_time) + + # Get total count + count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringError.id)) + if conditions: + count_query = count_query.where(sqlalchemy.and_(*conditions)) + + count_result = await self.ap.persistence_mgr.execute_async(count_query) + total = count_result.scalar() or 0 + + # Get errors + query = sqlalchemy.select(persistence_monitoring.MonitoringError).order_by( + persistence_monitoring.MonitoringError.timestamp.desc() + ) + if conditions: + query = query.where(sqlalchemy.and_(*conditions)) + + query = query.limit(limit).offset(offset) + + result = await self.ap.persistence_mgr.execute_async(query) + errors_rows = result.all() + + return ( + [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringError, row[0] if isinstance(row, tuple) else row + ) + for row in errors_rows + ], + total, + ) + + async def get_session_analysis( + self, + session_id: str, + ) -> dict: + """Get detailed analysis for a specific session""" + # Get session info + session_query = sqlalchemy.select(persistence_monitoring.MonitoringSession).where( + persistence_monitoring.MonitoringSession.session_id == session_id + ) + session_result = await self.ap.persistence_mgr.execute_async(session_query) + session_row = session_result.first() + + if not session_row: + return { + 'session_id': session_id, + 'found': False, + } + + session = session_row[0] if isinstance(session_row, tuple) else session_row + + # Get messages for this session + messages_query = ( + sqlalchemy.select(persistence_monitoring.MonitoringMessage) + .where(persistence_monitoring.MonitoringMessage.session_id == session_id) + .order_by(persistence_monitoring.MonitoringMessage.timestamp.asc()) + ) + messages_result = await self.ap.persistence_mgr.execute_async(messages_query) + messages_rows = messages_result.all() + + # Count messages by status + success_messages = 0 + error_messages = 0 + pending_messages = 0 + for row in messages_rows: + msg = row[0] if isinstance(row, tuple) else row + if msg.status == 'success': + success_messages += 1 + elif msg.status == 'error': + error_messages += 1 + elif msg.status == 'pending': + pending_messages += 1 + + # Get LLM calls for this session + llm_query = sqlalchemy.select(persistence_monitoring.MonitoringLLMCall).where( + persistence_monitoring.MonitoringLLMCall.session_id == session_id + ) + llm_result = await self.ap.persistence_mgr.execute_async(llm_query) + llm_rows = llm_result.all() + + # Calculate LLM statistics + total_llm_calls = len(llm_rows) + total_input_tokens = 0 + total_output_tokens = 0 + total_tokens = 0 + total_duration = 0 + success_llm_calls = 0 + error_llm_calls = 0 + + for row in llm_rows: + llm_call = row[0] if isinstance(row, tuple) else row + total_input_tokens += llm_call.input_tokens + total_output_tokens += llm_call.output_tokens + total_tokens += llm_call.total_tokens + total_duration += llm_call.duration + if llm_call.status == 'success': + success_llm_calls += 1 + else: + error_llm_calls += 1 + + # Get errors for this session + error_query = ( + sqlalchemy.select(persistence_monitoring.MonitoringError) + .where(persistence_monitoring.MonitoringError.session_id == session_id) + .order_by(persistence_monitoring.MonitoringError.timestamp.desc()) + ) + error_result = await self.ap.persistence_mgr.execute_async(error_query) + error_rows = error_result.all() + + errors = [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringError, row[0] if isinstance(row, tuple) else row + ) + for row in error_rows + ] + + # Calculate session duration + if messages_rows: + first_msg = messages_rows[0][0] if isinstance(messages_rows[0], tuple) else messages_rows[0] + last_msg = messages_rows[-1][0] if isinstance(messages_rows[-1], tuple) else messages_rows[-1] + session_duration_seconds = int((last_msg.timestamp - first_msg.timestamp).total_seconds()) + else: + session_duration_seconds = 0 + + return { + 'session_id': session_id, + 'found': True, + 'session': self.ap.persistence_mgr.serialize_model(persistence_monitoring.MonitoringSession, session), + 'message_stats': { + 'total': len(messages_rows), + 'success': success_messages, + 'error': error_messages, + 'pending': pending_messages, + }, + 'llm_stats': { + 'total_calls': total_llm_calls, + 'success_calls': success_llm_calls, + 'error_calls': error_llm_calls, + 'total_input_tokens': total_input_tokens, + 'total_output_tokens': total_output_tokens, + 'total_tokens': total_tokens, + 'average_duration_ms': int(total_duration / total_llm_calls) if total_llm_calls > 0 else 0, + }, + 'errors': errors, + 'session_duration_seconds': session_duration_seconds, + } + + async def get_message_details( + self, + message_id: str, + ) -> dict: + """Get detailed information for a specific message including associated LLM calls and errors""" + # Get message info + message_query = sqlalchemy.select(persistence_monitoring.MonitoringMessage).where( + persistence_monitoring.MonitoringMessage.id == message_id + ) + message_result = await self.ap.persistence_mgr.execute_async(message_query) + message_row = message_result.first() + + if not message_row: + return { + 'message_id': message_id, + 'found': False, + } + + message = message_row[0] if isinstance(message_row, tuple) else message_row + + # Get LLM calls for this message + llm_query = ( + sqlalchemy.select(persistence_monitoring.MonitoringLLMCall) + .where(persistence_monitoring.MonitoringLLMCall.message_id == message_id) + .order_by(persistence_monitoring.MonitoringLLMCall.timestamp.asc()) + ) + llm_result = await self.ap.persistence_mgr.execute_async(llm_query) + llm_rows = llm_result.all() + + llm_calls = [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringLLMCall, row[0] if isinstance(row, tuple) else row + ) + for row in llm_rows + ] + + # Calculate LLM statistics + total_input_tokens = sum(call.input_tokens for call in llm_rows) + total_output_tokens = sum(call.output_tokens for call in llm_rows) + total_tokens = sum(call.total_tokens for call in llm_rows) + total_duration = sum(call.duration for call in llm_rows) + + # Get errors for this message + error_query = ( + sqlalchemy.select(persistence_monitoring.MonitoringError) + .where(persistence_monitoring.MonitoringError.message_id == message_id) + .order_by(persistence_monitoring.MonitoringError.timestamp.asc()) + ) + error_result = await self.ap.persistence_mgr.execute_async(error_query) + error_rows = error_result.all() + + errors = [ + self.ap.persistence_mgr.serialize_model( + persistence_monitoring.MonitoringError, row[0] if isinstance(row, tuple) else row + ) + for row in error_rows + ] + + return { + 'message_id': message_id, + 'found': True, + 'message': self.ap.persistence_mgr.serialize_model(persistence_monitoring.MonitoringMessage, message), + 'llm_calls': llm_calls, + 'llm_stats': { + 'total_calls': len(llm_rows), + 'total_input_tokens': total_input_tokens, + 'total_output_tokens': total_output_tokens, + 'total_tokens': total_tokens, + 'total_duration_ms': total_duration, + 'average_duration_ms': int(total_duration / len(llm_rows)) if len(llm_rows) > 0 else 0, + }, + 'errors': errors, + } diff --git a/src/langbot/pkg/core/app.py b/src/langbot/pkg/core/app.py index c23d114f..62f8e7ae 100644 --- a/src/langbot/pkg/core/app.py +++ b/src/langbot/pkg/core/app.py @@ -29,6 +29,7 @@ from ..api.http.service import mcp as mcp_service from ..api.http.service import apikey as apikey_service from ..api.http.service import webhook as webhook_service from ..api.http.service import external_kb as external_kb_service +from ..api.http.service import monitoring as monitoring_service from ..discover import engine as discover_engine from ..storage import mgr as storagemgr from ..utils import logcache @@ -143,6 +144,8 @@ class Application: telemetry: telemetry_module.TelemetryManager = None + monitoring_service: monitoring_service.MonitoringService = None + def __init__(self): pass diff --git a/src/langbot/pkg/core/stages/build_app.py b/src/langbot/pkg/core/stages/build_app.py index 791b5a9e..6f84f299 100644 --- a/src/langbot/pkg/core/stages/build_app.py +++ b/src/langbot/pkg/core/stages/build_app.py @@ -26,6 +26,7 @@ from ...api.http.service import mcp as mcp_service from ...api.http.service import apikey as apikey_service from ...api.http.service import webhook as webhook_service from ...api.http.service import external_kb as external_kb_service +from ...api.http.service import monitoring as monitoring_service from ...discover import engine as discover_engine from ...storage import mgr as storagemgr from ...utils import logcache @@ -149,6 +150,9 @@ class BuildAppStage(stage.BootingStage): await http_ctrl.initialize() ap.http_ctrl = http_ctrl + monitoring_service_inst = monitoring_service.MonitoringService(ap) + ap.monitoring_service = monitoring_service_inst + async def runtime_disconnect_callback(connector: plugin_connector.PluginRuntimeConnector) -> None: await asyncio.sleep(3) await plugin_connector_inst.initialize() diff --git a/src/langbot/pkg/entity/persistence/monitoring.py b/src/langbot/pkg/entity/persistence/monitoring.py new file mode 100644 index 00000000..62121de6 --- /dev/null +++ b/src/langbot/pkg/entity/persistence/monitoring.py @@ -0,0 +1,105 @@ +import sqlalchemy + +from .base import Base + + +class MonitoringMessage(Base): + """Monitoring message records""" + + __tablename__ = 'monitoring_messages' + + id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True) + timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + message_content = sqlalchemy.Column(sqlalchemy.Text, nullable=False) + session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error, pending + level = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # info, warning, error, debug + platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) + user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) + runner_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # Runner name for this query + variables = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # Query variables as JSON string + + +class MonitoringLLMCall(Base): + """LLM call records""" + + __tablename__ = 'monitoring_llm_calls' + + id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True) + timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + input_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) + output_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) + total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) + duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # milliseconds + cost = sqlalchemy.Column(sqlalchemy.Float, nullable=True) + status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error + bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True) + message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) # Associated message ID + + +class MonitoringSession(Base): + """Session tracking records""" + + __tablename__ = 'monitoring_sessions' + + session_id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True) + bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + message_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0) + start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + last_activity = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + is_active = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True, index=True) + platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) + user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) + + +class MonitoringError(Base): + """Error log records""" + + __tablename__ = 'monitoring_errors' + + id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True) + timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + error_type = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=False) + bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True) + pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) + stack_trace = sqlalchemy.Column(sqlalchemy.Text, nullable=True) + message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) # Associated message ID + + +class MonitoringEmbeddingCall(Base): + """Embedding call records""" + + __tablename__ = 'monitoring_embedding_calls' + + id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True) + timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True) + model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False) + prompt_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) + total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) + duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # milliseconds + input_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # Number of input texts + status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error + error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True) + # Optional context fields + knowledge_base_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) + query_text = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # For retrieval calls + session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) + message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) + call_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True) # embedding, retrieve diff --git a/src/langbot/pkg/pipeline/monitoring_helper.py b/src/langbot/pkg/pipeline/monitoring_helper.py new file mode 100644 index 00000000..e5289934 --- /dev/null +++ b/src/langbot/pkg/pipeline/monitoring_helper.py @@ -0,0 +1,270 @@ +""" +Monitoring helper for recording events during pipeline execution. +This module provides convenient methods to record monitoring data +without cluttering the main pipeline code. +""" + +from __future__ import annotations + +import traceback +import typing +import time +import json + +if typing.TYPE_CHECKING: + from ..core import app + import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query + + +class MonitoringHelper: + """Helper class for monitoring operations""" + + @staticmethod + async def record_query_start( + ap: app.Application, + query: pipeline_query.Query, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + runner_name: str | None = None, + ) -> str: + """Record the start of query processing, returns message_id""" + try: + # Check if session exists, if not, record session start + session_id = f'{query.launcher_type}_{query.launcher_id}' + + # Try to record message + # Use JSON serialization to preserve message chain structure (including image URLs, etc.) + if hasattr(query, 'message_chain') and hasattr(query.message_chain, 'model_dump'): + message_content = json.dumps(query.message_chain.model_dump(), ensure_ascii=False) + else: + message_content = str(query) + + # Variables will be updated in record_query_success after preproc stage sets them + # Here we just record None, the full variables will be set when query completes + + message_id = await ap.monitoring_service.record_message( + bot_id=bot_id, + bot_name=bot_name, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + message_content=message_content, + session_id=session_id, + status='pending', + level='info', + platform=query.launcher_type.value + if hasattr(query.launcher_type, 'value') + else str(query.launcher_type), + user_id=query.sender_id, + runner_name=runner_name, + variables=None, # Will be updated in record_query_success + ) + + # Update session activity or create new session if it doesn't exist + # Always pass pipeline info to handle pipeline switches + session_updated = await ap.monitoring_service.update_session_activity( + session_id, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + ) + if not session_updated: + # Session doesn't exist, create it + await ap.monitoring_service.record_session_start( + session_id=session_id, + bot_id=bot_id, + bot_name=bot_name, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + platform=query.launcher_type.value + if hasattr(query.launcher_type, 'value') + else str(query.launcher_type), + user_id=query.sender_id, + ) + + return message_id + except Exception as e: + ap.logger.error(f'Failed to record query start: {e}') + return '' + + @staticmethod + async def record_query_success( + ap: app.Application, + message_id: str, + query: pipeline_query.Query | None = None, + ): + """Record successful query processing by updating message status and variables""" + try: + if message_id: + # Serialize query.variables (filtering out internal variables) + query_variables_str = None + if query and hasattr(query, 'variables') and query.variables: + filtered_vars = {k: v for k, v in query.variables.items() if not k.startswith('_')} + if filtered_vars: + try: + query_variables_str = json.dumps(filtered_vars, ensure_ascii=False, default=str) + except Exception: + pass + + await ap.monitoring_service.update_message_status( + message_id=message_id, + status='success', + variables=query_variables_str, + ) + except Exception as e: + ap.logger.error(f'Failed to record query success: {e}') + + @staticmethod + async def record_query_error( + ap: app.Application, + query: pipeline_query.Query, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + error: Exception, + runner_name: str | None = None, + ) -> str: + """Record query processing error, returns message_id""" + try: + session_id = f'{query.launcher_type}_{query.launcher_id}' + + # Record error message + message_id = await ap.monitoring_service.record_message( + bot_id=bot_id, + bot_name=bot_name, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + message_content=f'Error: {str(error)}', + session_id=session_id, + status='error', + level='error', + platform=query.launcher_type.value + if hasattr(query.launcher_type, 'value') + else str(query.launcher_type), + user_id=query.sender_id, + runner_name=runner_name, + ) + + # Record error log + await ap.monitoring_service.record_error( + bot_id=bot_id, + bot_name=bot_name, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + error_type=type(error).__name__, + error_message=str(error), + session_id=session_id, + stack_trace=traceback.format_exc(), + message_id=message_id, + ) + + return message_id + except Exception as e: + ap.logger.error(f'Failed to record query error: {e}') + return '' + + @staticmethod + async def record_llm_call( + ap: app.Application, + query: pipeline_query.Query, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + model_name: str, + input_tokens: int, + output_tokens: int, + duration_ms: int, + status: str = 'success', + cost: float | None = None, + error_message: str | None = None, + message_id: str | None = None, + ): + """Record LLM call""" + try: + session_id = f'{query.launcher_type}_{query.launcher_id}' + + await ap.monitoring_service.record_llm_call( + bot_id=bot_id, + bot_name=bot_name, + pipeline_id=pipeline_id, + pipeline_name=pipeline_name, + session_id=session_id, + model_name=model_name, + input_tokens=input_tokens, + output_tokens=output_tokens, + duration=duration_ms, + status=status, + cost=cost, + error_message=error_message, + message_id=message_id, + ) + except Exception as e: + ap.logger.error(f'Failed to record LLM call: {e}') + + +class LLMCallMonitor: + """Context manager for monitoring LLM calls""" + + def __init__( + self, + ap: app.Application, + query: pipeline_query.Query, + bot_id: str, + bot_name: str, + pipeline_id: str, + pipeline_name: str, + model_name: str, + ): + self.ap = ap + self.query = query + self.bot_id = bot_id + self.bot_name = bot_name + self.pipeline_id = pipeline_id + self.pipeline_name = pipeline_name + self.model_name = model_name + self.start_time = None + self.input_tokens = 0 + self.output_tokens = 0 + + async def __aenter__(self): + self.start_time = time.time() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + duration_ms = int((time.time() - self.start_time) * 1000) + + if exc_type is not None: + # Error occurred + await MonitoringHelper.record_llm_call( + ap=self.ap, + query=self.query, + bot_id=self.bot_id, + bot_name=self.bot_name, + pipeline_id=self.pipeline_id, + pipeline_name=self.pipeline_name, + model_name=self.model_name, + input_tokens=self.input_tokens, + output_tokens=self.output_tokens, + duration_ms=duration_ms, + status='error', + error_message=str(exc_val) if exc_val else None, + ) + else: + # Success + await MonitoringHelper.record_llm_call( + ap=self.ap, + query=self.query, + bot_id=self.bot_id, + bot_name=self.bot_name, + pipeline_id=self.pipeline_id, + pipeline_name=self.pipeline_name, + model_name=self.model_name, + input_tokens=self.input_tokens, + output_tokens=self.output_tokens, + duration_ms=duration_ms, + status='success', + ) + + return False # Don't suppress exceptions diff --git a/src/langbot/pkg/pipeline/pipelinemgr.py b/src/langbot/pkg/pipeline/pipelinemgr.py index 06f77b28..08c449a7 100644 --- a/src/langbot/pkg/pipeline/pipelinemgr.py +++ b/src/langbot/pkg/pipeline/pipelinemgr.py @@ -115,6 +115,25 @@ class RuntimePipeline: # Store bound plugins and MCP servers in query for filtering query.variables['_pipeline_bound_plugins'] = self.bound_plugins query.variables['_pipeline_bound_mcp_servers'] = self.bound_mcp_servers + + # Record query start for monitoring + try: + # Get bot name from bot_uuid + bot_name = 'WebChat' + if query.bot_uuid: + try: + bot = await self.ap.bot_service.get_bot(query.bot_uuid, include_secret=False) + if bot: + bot_name = bot.get('name', 'Unknown') + except Exception: + pass + + # Store for later use in process_query + query.variables['_monitoring_bot_name'] = bot_name + query.variables['_monitoring_pipeline_name'] = self.pipeline_entity.name + except Exception as e: + self.ap.logger.error(f'Failed to prepare monitoring data: {e}') + await self.process_query(query) async def _check_output(self, query: pipeline_query.Query, result: pipeline_entities.StageProcessResult): @@ -131,7 +150,7 @@ class RuntimePipeline: query.message_event, platform_events.GroupMessage ): result.user_notice.insert(0, platform_message.At(target=query.message_event.sender.id)) - if await query.adapter.is_stream_output_supported(): + if await query.adapter.is_stream_output_supported() and query.resp_messages: await query.adapter.reply_message_chunk( message_source=query.message_event, bot_message=query.resp_messages[-1], @@ -151,6 +170,37 @@ class RuntimePipeline: self.ap.logger.info(result.console_notice) if result.error_notice: self.ap.logger.error(result.error_notice) + # Mark query as having error + query.variables['_monitoring_has_error'] = True + # Record error to monitoring system + try: + bot_name = query.variables.get('_monitoring_bot_name', 'Unknown') + pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown') + message_id = query.variables.get('_monitoring_message_id', '') + session_id = f'{query.launcher_type}_{query.launcher_id}' + + # Update message status to error + if message_id: + await self.ap.monitoring_service.update_message_status( + message_id=message_id, + status='error', + level='error', + ) + + # Record error log + await self.ap.monitoring_service.record_error( + bot_id=query.bot_uuid or 'unknown', + bot_name=bot_name, + pipeline_id=self.pipeline_entity.uuid, + pipeline_name=pipeline_name, + error_type='PipelineError', + error_message=result.error_notice, + session_id=session_id, + stack_trace=result.debug_notice if result.debug_notice else None, + message_id=message_id, + ) + except Exception as e: + self.ap.logger.error(f'Failed to record error to monitoring: {e}') async def _execute_from_stage( self, @@ -221,6 +271,34 @@ class RuntimePipeline: async def process_query(self, query: pipeline_query.Query): """处理请求""" + # Get monitoring metadata + bot_name = query.variables.get('_monitoring_bot_name', 'Unknown') + pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown') + + # Get runner name from pipeline config + runner_name = None + if query.pipeline_config and 'ai' in query.pipeline_config and 'runner' in query.pipeline_config['ai']: + runner_name = query.pipeline_config['ai']['runner'].get('runner') + + # Record query start and store message_id + message_id = '' + try: + from . import monitoring_helper + + message_id = await monitoring_helper.MonitoringHelper.record_query_start( + ap=self.ap, + query=query, + bot_id=query.bot_uuid or 'unknown', + bot_name=bot_name, + pipeline_id=self.pipeline_entity.uuid, + pipeline_name=pipeline_name, + runner_name=runner_name, + ) + # Store message_id in query variables for LLM call monitoring + query.variables['_monitoring_message_id'] = message_id + except Exception as e: + self.ap.logger.error(f'Failed to record query start: {e}') + try: # Get bound plugins for this pipeline bound_plugins = query.variables.get('_pipeline_bound_plugins', None) @@ -249,10 +327,40 @@ class RuntimePipeline: self.ap.logger.debug(f'Processing query {query.query_id}') await self._execute_from_stage(0, query) + + # Record query success only if no error occurred during processing + if not query.variables.get('_monitoring_has_error', False): + try: + await monitoring_helper.MonitoringHelper.record_query_success( + ap=self.ap, + message_id=message_id, + query=query, + ) + except Exception as e: + self.ap.logger.error(f'Failed to record query success: {e}') + except Exception as e: inst_name = query.current_stage_name if query.current_stage_name else 'unknown' self.ap.logger.error(f'Error processing query {query.query_id} stage={inst_name} : {e}') self.ap.logger.error(f'Traceback: {traceback.format_exc()}') + + # Record query error + try: + from . import monitoring_helper + + await monitoring_helper.MonitoringHelper.record_query_error( + ap=self.ap, + query=query, + bot_id=query.bot_uuid or 'unknown', + bot_name=bot_name, + pipeline_id=self.pipeline_entity.uuid, + pipeline_name=pipeline_name, + error=e, + runner_name=runner_name, + ) + except Exception as me: + self.ap.logger.error(f'Failed to record query error: {me}') + finally: self.ap.logger.debug(f'Query {query.query_id} processed') del self.ap.query_pool.cached_queries[query.query_id] diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py index e9819e0d..39f50c32 100644 --- a/src/langbot/pkg/plugin/handler.py +++ b/src/langbot/pkg/plugin/handler.py @@ -324,7 +324,7 @@ class RuntimeConnectionHandler(handler.Handler): messages_obj = [provider_message.Message.model_validate(message) for message in messages] funcs_obj = [resource_tool.LLMTool.model_validate(func) for func in funcs] - result = await llm_model.provider.requester.invoke_llm( + result = await llm_model.provider.invoke_llm( query=None, model=llm_model, messages=messages_obj, diff --git a/src/langbot/pkg/provider/modelmgr/requester.py b/src/langbot/pkg/provider/modelmgr/requester.py index 3052a62f..c281d8ae 100644 --- a/src/langbot/pkg/provider/modelmgr/requester.py +++ b/src/langbot/pkg/provider/modelmgr/requester.py @@ -2,6 +2,7 @@ from __future__ import annotations import abc import typing +import time from ...core import app from ...entity.persistence import model as persistence_model @@ -33,6 +34,219 @@ class RuntimeProvider: self.token_mgr = token_mgr self.requester = requester + async def invoke_llm( + self, + query: pipeline_query.Query, + model: RuntimeLLMModel, + messages: typing.List[provider_message.Message], + funcs: typing.List[resource_tool.LLMTool] = None, + extra_args: dict[str, typing.Any] = {}, + remove_think: bool = False, + ) -> provider_message.Message: + """Bridge method for invoking LLM with monitoring""" + # Start timing for monitoring + start_time = time.time() + input_tokens = 0 + output_tokens = 0 + status = 'success' + error_message = None + + try: + # Call the underlying requester + result = await self.requester.invoke_llm( + query=query, + model=model, + messages=messages, + funcs=funcs, + extra_args=extra_args, + remove_think=remove_think, + ) + + # Try to extract token usage if the requester returns it + # For requesters that return tuple (message, usage_info) + if isinstance(result, tuple): + msg, usage_info = result + if usage_info: + input_tokens = usage_info.get('input_tokens', 0) + output_tokens = usage_info.get('output_tokens', 0) + return msg + else: + return result + + except Exception as e: + status = 'error' + error_message = str(e) + raise + finally: + # Record LLM call monitoring data (only if query is provided) + if query is not None: + duration_ms = int((time.time() - start_time) * 1000) + + # Import monitoring helper + try: + from ...pipeline import monitoring_helper + + # Get monitoring metadata from query variables + if query.variables: + bot_name = query.variables.get('_monitoring_bot_name', 'Unknown') + pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown') + message_id = query.variables.get('_monitoring_message_id') + else: + bot_name = 'Unknown' + pipeline_name = 'Unknown' + message_id = None + + await monitoring_helper.MonitoringHelper.record_llm_call( + ap=self.requester.ap, + query=query, + bot_id=query.bot_uuid or 'unknown', + bot_name=bot_name, + pipeline_id=query.pipeline_uuid or 'unknown', + pipeline_name=pipeline_name, + model_name=model.model_entity.name, + input_tokens=input_tokens, + output_tokens=output_tokens, + duration_ms=duration_ms, + status=status, + error_message=error_message, + message_id=message_id, + ) + except Exception as monitor_err: + self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM call: {monitor_err}') + + async def invoke_llm_stream( + self, + query: pipeline_query.Query, + model: RuntimeLLMModel, + messages: typing.List[provider_message.Message], + funcs: typing.List[resource_tool.LLMTool] = None, + extra_args: dict[str, typing.Any] = {}, + remove_think: bool = False, + ) -> provider_message.MessageChunk: + """Bridge method for invoking LLM stream with monitoring""" + # Start timing for monitoring + start_time = time.time() + status = 'success' + error_message = None + # Note: Stream doesn't easily provide token counts, set to 0 + input_tokens = 0 + output_tokens = 0 + + try: + # Stream the response + async for chunk in self.requester.invoke_llm_stream( + query=query, + model=model, + messages=messages, + funcs=funcs, + extra_args=extra_args, + remove_think=remove_think, + ): + yield chunk + except Exception as e: + status = 'error' + error_message = str(e) + raise + finally: + # Record LLM call monitoring data (only if query is provided) + if query is not None: + duration_ms = int((time.time() - start_time) * 1000) + + # Import monitoring helper + try: + from ...pipeline import monitoring_helper + + # Get monitoring metadata from query variables + if query.variables: + bot_name = query.variables.get('_monitoring_bot_name', 'Unknown') + pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown') + message_id = query.variables.get('_monitoring_message_id') + else: + bot_name = 'Unknown' + pipeline_name = 'Unknown' + message_id = None + + await monitoring_helper.MonitoringHelper.record_llm_call( + ap=self.requester.ap, + query=query, + bot_id=query.bot_uuid or 'unknown', + bot_name=bot_name, + pipeline_id=query.pipeline_uuid or 'unknown', + pipeline_name=pipeline_name, + model_name=model.model_entity.name, + input_tokens=input_tokens, + output_tokens=output_tokens, + duration_ms=duration_ms, + status=status, + error_message=error_message, + message_id=message_id, + ) + except Exception as monitor_err: + self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM stream call: {monitor_err}') + + async def invoke_embedding( + self, + model: RuntimeEmbeddingModel, + input_text: typing.List[str], + extra_args: dict[str, typing.Any] = {}, + knowledge_base_id: str | None = None, + query_text: str | None = None, + session_id: str | None = None, + message_id: str | None = None, + call_type: str | None = None, + ) -> typing.List[typing.List[float]]: + """Bridge method for invoking embedding with monitoring""" + # Start timing for monitoring + start_time = time.time() + prompt_tokens = 0 + total_tokens = 0 + status = 'success' + error_message = None + + try: + # Call the underlying requester + result = await self.requester.invoke_embedding( + model=model, + input_text=input_text, + extra_args=extra_args, + ) + + # Handle both old format (list only) and new format (tuple with usage) + if isinstance(result, tuple): + embeddings, usage_info = result + if usage_info: + prompt_tokens = usage_info.get('prompt_tokens', 0) + total_tokens = usage_info.get('total_tokens', 0) + return embeddings + else: + return result + + except Exception as e: + status = 'error' + error_message = str(e) + raise + finally: + # Record embedding call monitoring data + duration_ms = int((time.time() - start_time) * 1000) + + try: + await self.requester.ap.monitoring_service.record_embedding_call( + model_name=model.model_entity.name, + prompt_tokens=prompt_tokens, + total_tokens=total_tokens, + duration=duration_ms, + input_count=len(input_text), + status=status, + error_message=error_message, + knowledge_base_id=knowledge_base_id, + query_text=query_text, + session_id=session_id, + message_id=message_id, + call_type=call_type, + ) + except Exception as monitor_err: + self.requester.ap.logger.error(f'[Monitoring] Failed to record embedding call: {monitor_err}') + class RuntimeLLMModel: """运行时模型""" @@ -141,7 +355,7 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta): model: RuntimeEmbeddingModel, input_text: typing.List[str], extra_args: dict[str, typing.Any] = {}, - ) -> typing.List[typing.List[float]]: + ) -> typing.Union[typing.List[typing.List[float]], tuple[typing.List[typing.List[float]], dict]]: """调用 Embedding API Args: @@ -151,5 +365,6 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta): Returns: typing.List[typing.List[float]]: 返回的 embedding 向量 + 或者 tuple[typing.List[typing.List[float]], dict]: 返回 (embedding 向量, usage_info) """ pass diff --git a/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py b/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py index 97e61c89..cbf5543e 100644 --- a/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py +++ b/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py @@ -253,7 +253,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): use_funcs: list[resource_tool.LLMTool] = None, extra_args: dict[str, typing.Any] = {}, remove_think: bool = False, - ) -> provider_message.Message: + ) -> tuple[provider_message.Message, dict]: self.client.api_key = use_model.provider.token_mgr.get_token() args = {} @@ -285,7 +285,14 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): # 处理请求结果 message = await self._make_msg(resp, remove_think) - return message + # Extract token usage from response + usage_info = {} + if hasattr(resp, 'usage') and resp.usage: + usage_info['input_tokens'] = resp.usage.prompt_tokens or 0 + usage_info['output_tokens'] = resp.usage.completion_tokens or 0 + usage_info['total_tokens'] = resp.usage.total_tokens or 0 + + return message, usage_info async def invoke_llm( self, @@ -295,7 +302,8 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): funcs: typing.List[resource_tool.LLMTool] = None, extra_args: dict[str, typing.Any] = {}, remove_think: bool = False, - ) -> provider_message.Message: + ) -> tuple[provider_message.Message, dict]: + """Invoke LLM and return message with usage info""" req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行 for m in messages: msg_dict = m.dict(exclude_none=True) @@ -308,7 +316,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): req_messages.append(msg_dict) try: - msg = await self._closure( + msg, usage_info = await self._closure( query=query, req_messages=req_messages, use_model=model, @@ -316,30 +324,38 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): extra_args=extra_args, remove_think=remove_think, ) - return msg + return msg, usage_info except asyncio.TimeoutError: raise errors.RequesterError('请求超时') except openai.BadRequestError as e: - if 'context_length_exceeded' in e.message: - raise errors.RequesterError(f'上文过长，请重置会话: {e.message}') + error_message = str(e.message) if hasattr(e, 'message') else str(e) + if 'context_length_exceeded' in str(e): + raise errors.RequesterError(f'上文过长，请重置会话: {error_message}') else: - raise errors.RequesterError(f'请求参数错误: {e.message}') + raise errors.RequesterError(f'请求参数错误: {error_message}') except openai.AuthenticationError as e: - raise errors.RequesterError(f'无效的 api-key: {e.message}') + error_message = str(e.message) if hasattr(e, 'message') else str(e) + raise errors.RequesterError(f'无效的 api-key: {error_message}') except openai.NotFoundError as e: - raise errors.RequesterError(f'请求路径错误: {e.message}') + error_message = str(e.message) if hasattr(e, 'message') else str(e) + raise errors.RequesterError(f'请求路径错误: {error_message}') except openai.RateLimitError as e: - raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}') + error_message = str(e.message) if hasattr(e, 'message') else str(e) + raise errors.RequesterError(f'请求过于频繁或余额不足: {error_message}') + except openai.APIConnectionError as e: + error_message = f'连接错误: {str(e)}' + raise errors.RequesterError(error_message) except openai.APIError as e: - raise errors.RequesterError(f'请求错误: {e.message}') + error_message = str(e.message) if hasattr(e, 'message') else str(e) + raise errors.RequesterError(f'请求错误: {error_message}') async def invoke_embedding( self, model: requester.RuntimeEmbeddingModel, input_text: list[str], extra_args: dict[str, typing.Any] = {}, - ) -> list[list[float]]: - """调用 Embedding API""" + ) -> tuple[list[list[float]], dict]: + """调用 Embedding API, returns (embeddings, usage_info)""" self.client.api_key = model.provider.token_mgr.get_token() args = { @@ -355,7 +371,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester): try: resp = await self.client.embeddings.create(**args) - return [d.embedding for d in resp.data] + # Extract usage info + usage_info = {} + if hasattr(resp, 'usage') and resp.usage: + usage_info['prompt_tokens'] = resp.usage.prompt_tokens or 0 + usage_info['total_tokens'] = resp.usage.total_tokens or 0 + + return [d.embedding for d in resp.data], usage_info except asyncio.TimeoutError: raise errors.RequesterError('请求超时') except openai.BadRequestError as e: diff --git a/src/langbot/pkg/provider/runners/localagent.py b/src/langbot/pkg/provider/runners/localagent.py index 64dbfc63..dbda6622 100644 --- a/src/langbot/pkg/provider/runners/localagent.py +++ b/src/langbot/pkg/provider/runners/localagent.py @@ -130,7 +130,7 @@ class LocalAgentRunner(runner.RequestRunner): if not is_stream: # 非流式输出，直接请求 - msg = await use_llm_model.provider.requester.invoke_llm( + msg = await use_llm_model.provider.invoke_llm( query, use_llm_model, req_messages, @@ -147,7 +147,7 @@ class LocalAgentRunner(runner.RequestRunner): accumulated_content = '' # 从开始累积的所有内容 last_role = 'assistant' msg_sequence = 1 - async for msg in use_llm_model.provider.requester.invoke_llm_stream( + async for msg in use_llm_model.provider.invoke_llm_stream( query, use_llm_model, req_messages, @@ -265,7 +265,7 @@ class LocalAgentRunner(runner.RequestRunner): last_role = 'assistant' msg_sequence = first_end_sequence - async for msg in use_llm_model.provider.requester.invoke_llm_stream( + async for msg in use_llm_model.provider.invoke_llm_stream( query, use_llm_model, req_messages, @@ -321,7 +321,7 @@ class LocalAgentRunner(runner.RequestRunner): ) else: # 处理完所有调用，再次请求 - msg = await use_llm_model.provider.requester.invoke_llm( + msg = await use_llm_model.provider.invoke_llm( query, use_llm_model, req_messages, diff --git a/src/langbot/pkg/rag/knowledge/services/embedder.py b/src/langbot/pkg/rag/knowledge/services/embedder.py index 485bc21e..168b839d 100644 --- a/src/langbot/pkg/rag/knowledge/services/embedder.py +++ b/src/langbot/pkg/rag/knowledge/services/embedder.py @@ -38,10 +38,12 @@ class Embedder(BaseService): for i in range(0, len(chunks), MAX_BATCH_SIZE): batch = chunks[i : i + MAX_BATCH_SIZE] - batch_embeddings = await embedding_model.provider.requester.invoke_embedding( + batch_embeddings = await embedding_model.provider.invoke_embedding( model=embedding_model, input_text=batch, extra_args={}, # TODO: add extra args + knowledge_base_id=kb_id, + call_type='embedding', ) embeddings_list.extend(batch_embeddings) diff --git a/src/langbot/pkg/rag/knowledge/services/retriever.py b/src/langbot/pkg/rag/knowledge/services/retriever.py index f2f4bef5..15619c94 100644 --- a/src/langbot/pkg/rag/knowledge/services/retriever.py +++ b/src/langbot/pkg/rag/knowledge/services/retriever.py @@ -19,10 +19,13 @@ class Retriever(base_service.BaseService): f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}" ) - query_embedding: list[float] = await embedding_model.provider.requester.invoke_embedding( + query_embedding: list[float] = await embedding_model.provider.invoke_embedding( model=embedding_model, input_text=[query], extra_args={}, # TODO: add extra args + knowledge_base_id=kb_id, + query_text=query, + call_type='retrieve', ) vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k) diff --git a/web/package.json b/web/package.json index 5709fc10..ecf939bb 100644 --- a/web/package.json +++ b/web/package.json @@ -63,6 +63,7 @@ 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b/web/src/app/home/bots/components/bot-log/view/BotLogCard.tsx @@ -6,12 +6,33 @@ import styles from './botLog.module.css'; import { httpClient } from '@/app/infra/http/HttpClient'; import { PhotoProvider } from 'react-photo-view'; import { useTranslation } from 'react-i18next'; -import { Check } from 'lucide-react'; +import { Check, ChevronDown, ChevronRight } from 'lucide-react'; +import { toast } from 'sonner'; export function BotLogCard({ botLog }: { botLog: BotLog }) { const { t } = useTranslation(); const baseURL = httpClient.getBaseUrl(); const [copied, setCopied] = useState(false); + const [expanded, setExpanded] = useState(false); + + // Fallback 复制方法，用于不支持 clipboard API 的环境 + function fallbackCopy(text: string) { + const textArea = document.createElement('textarea'); + textArea.value = text; + textArea.style.position = 'fixed'; + textArea.style.left = '-9999px'; + textArea.style.top = '-9999px'; + document.body.appendChild(textArea); + textArea.focus(); + textArea.select(); + try { + document.execCommand('copy'); + toast.success(t('common.copySuccess')); + } catch { + toast.error(t('common.copyFailed')); + } + document.body.removeChild(textArea); + } function formatTime(timestamp: number) { const now = new Date(); @@ -63,6 +84,15 @@ export function BotLogCard({ botLog }: { botLog: BotLog }) { } } + // 截取文本的简短版本 + function getShortText(text: string, maxLength: number = 100) { + if (text.length <= maxLength) return text; + return text.substring(0, maxLength) + '...'; + } + + // 判断是否需要展开按钮 + const needsExpand = botLog.text.length > 100 || botLog.images.length > 0; + return (
{/* 头部标签，时间 */} @@ -78,13 +108,24 @@ export function BotLogCard({ botLog }: { botLog: BotLog }) { {botLog.message_session_id && (
{ - navigator.clipboard - .writeText(botLog.message_session_id) - .then(() => { - setCopied(true); - setTimeout(() => setCopied(false), 2000); - }); + onClick={(e) => { + e.stopPropagation(); + // 兼容性更好的复制方法 + if (navigator.clipboard && navigator.clipboard.writeText) { + navigator.clipboard + .writeText(botLog.message_session_id) + .then(() => { + setCopied(true); + setTimeout(() => setCopied(false), 2000); + toast.success(t('common.copySuccess')); + }) + .catch(() => { + // fallback + fallbackCopy(botLog.message_session_id); + }); + } else { + fallbackCopy(botLog.message_session_id); + } }} title={t('common.clickToCopy')} > @@ -125,12 +166,38 @@ export function BotLogCard({ botLog }: { botLog: BotLog }) {
)}
-
- {formatTime(botLog.timestamp)} +
+ {needsExpand && ( + + )} +
+ {formatTime(botLog.timestamp)} +
-
{botLog.text}
- {botLog.images.length > 0 && ( + + {/* 日志内容 - 简化显示 */} +
+ {expanded ? botLog.text : getShortText(botLog.text)} +
+ + {/* 图片 - 只在展开时显示 */} + {expanded && botLog.images.length > 0 && (
{botLog.images.map((item) => ( @@ -144,6 +211,13 @@ export function BotLogCard({ botLog }: { botLog: BotLog }) {
)} + + {/* 图片数量提示 - 未展开时显示 */} + {!expanded && botLog.images.length > 0 && ( +
+ 📷 {botLog.images.length} {t('bots.imagesAttached')} +
+ )} ); } diff --git a/web/src/app/home/bots/components/bot-log/view/BotLogListComponent.tsx b/web/src/app/home/bots/components/bot-log/view/BotLogListComponent.tsx index 4a5716ee..1e0cd572 100644 --- a/web/src/app/home/bots/components/bot-log/view/BotLogListComponent.tsx +++ b/web/src/app/home/bots/components/bot-log/view/BotLogListComponent.tsx @@ -13,12 +13,14 @@ import { } from '@/components/ui/popover'; import { Button } from '@/components/ui/button'; import { Checkbox } from '@/components/ui/checkbox'; -import { ChevronDownIcon } from 'lucide-react'; +import { ChevronDownIcon, ExternalLink } from 'lucide-react'; import { debounce } from 'lodash'; import { useTranslation } from 'react-i18next'; +import { useRouter } from 'next/navigation'; export function BotLogListComponent({ botId }: { botId: string }) { const { t } = useTranslation(); + const router = useRouter(); const manager = useRef(new BotLogManager(botId)).current; const [botLogList, setBotLogList] = useState([]); const [autoFlush, setAutoFlush] = useState(true); @@ -206,6 +208,15 @@ export function BotLogListComponent({ botId }: { botId: string }) { + {filteredLogs.map((botLog) => { diff --git a/web/src/app/home/components/home-sidebar/HomeSidebar.tsx b/web/src/app/home/components/home-sidebar/HomeSidebar.tsx index 0b853683..5a6d3a4f 100644 --- a/web/src/app/home/components/home-sidebar/HomeSidebar.tsx +++ b/web/src/app/home/components/home-sidebar/HomeSidebar.tsx @@ -228,6 +228,7 @@ function HomeSidebarContent({ ); if (routeSelectChild) { setSelectedChild(routeSelectChild); + onSelectedChangeAction(routeSelectChild); } } } diff --git a/web/src/app/home/components/home-sidebar/sidbarConfigList.tsx b/web/src/app/home/components/home-sidebar/sidbarConfigList.tsx index f8cfb633..cdb99945 100644 --- a/web/src/app/home/components/home-sidebar/sidbarConfigList.tsx +++ b/web/src/app/home/components/home-sidebar/sidbarConfigList.tsx @@ -49,6 +49,26 @@ export const sidebarConfigList = [ ja_JP: 'https://docs.langbot.app/ja/usage/pipelines/readme.html', }, }), + new SidebarChildVO({ + id: 'monitoring', + name: t('monitoring.title'), + icon: ( + + + + ), + route: '/home/monitoring', + description: t('monitoring.description'), + helpLink: { + en_US: 'https://docs.langbot.app/en/features/monitoring.html', + zh_Hans: 'https://docs.langbot.app/zh/features/monitoring.html', + }, + }), new SidebarChildVO({ id: 'knowledge', name: t('knowledge.title'), diff --git a/web/src/app/home/monitoring/components/MessageContentRenderer.tsx b/web/src/app/home/monitoring/components/MessageContentRenderer.tsx new file mode 100644 index 00000000..769aea92 --- /dev/null +++ b/web/src/app/home/monitoring/components/MessageContentRenderer.tsx @@ -0,0 +1,230 @@ +'use client'; + +import React, { useState } from 'react'; +import { + MessageChainComponent, + Image as ImageComponent, + Plain, + At, + Voice, + Quote, +} from '@/app/infra/entities/message'; +import ImagePreviewDialog from '@/app/home/pipelines/components/debug-dialog/ImagePreviewDialog'; + +interface MessageContentRendererProps { + content: string; + maxLines?: number; +} + +export function MessageContentRenderer({ + content, + maxLines = 3, +}: MessageContentRendererProps) { + const [previewImageUrl, setPreviewImageUrl] = useState(''); + const [showImagePreview, setShowImagePreview] = useState(false); + + // Try to parse content as message_chain JSON + const parseContent = (content: string): MessageChainComponent[] | null => { + try { + const parsed = JSON.parse(content); + if (Array.isArray(parsed) && parsed.length > 0 && parsed[0].type) { + return parsed as MessageChainComponent[]; + } + return null; + } catch { + return null; + } + }; + + const renderMessageComponent = ( + component: MessageChainComponent, + index: number, + ) => { + switch (component.type) { + case 'Plain': + return {(component as Plain).text}; + + case 'At': { + const atComponent = component as At; + const displayName = + atComponent.display || atComponent.target?.toString() || ''; + return ( + + @{displayName} + + ); + } + + case 'AtAll': + return ( + + @All + + ); + + case 'Image': { + const img = component as ImageComponent; + const imageUrl = img.url || (img.base64 ? img.base64 : ''); + + if (!imageUrl) { + return ( + + [Image] + + ); + } + + return ( + + Image { + e.stopPropagation(); + setPreviewImageUrl(imageUrl); + setShowImagePreview(true); + }} + /> + + ); + } + + case 'File': { + const file = component as MessageChainComponent & { name?: string }; + return ( + + + + + {file.name || 'File'} + + ); + } + + case 'Voice': { + const voice = component as Voice; + return ( + + + + + Voice{voice.length ? ` ${voice.length}s` : ''} + + ); + } + + case 'Quote': { + const quote = component as Quote; + return ( + + {quote.origin + ?.filter((c) => (c as MessageChainComponent).type === 'Plain') + .map((c) => (c as MessageChainComponent as Plain).text) + .join('') || '[Quote]'} + + ); + } + + case 'Source': + return null; + + default: + return ( + + [{component.type}] + + ); + } + }; + + const messageChain = parseContent(content); + + // Determine line clamp class + const lineClampClass = + maxLines === 2 + ? 'line-clamp-2' + : maxLines === 3 + ? 'line-clamp-3' + : maxLines === 4 + ? 'line-clamp-4' + : ''; + + if (messageChain) { + // Filter out Source components as they render to null + const visibleComponents = messageChain.filter( + (component) => component.type !== 'Source', + ); + + // If no visible components, show placeholder + if (visibleComponents.length === 0) { + return ( + + [Empty message] + + ); + } + + // Render as message chain + return ( + <> +
+ {messageChain.map((component, index) => + renderMessageComponent(component, index), + )} +
+ setShowImagePreview(false)} + /> + + ); + } + + // Handle empty plain text + if ( + !content || + content.trim() === '' || + content === '[]' || + content === '""' + ) { + return ( + + [Empty message] + + ); + } + + // Render as plain text + return {content}; +} diff --git a/web/src/app/home/monitoring/components/MessageDetailsCard.tsx b/web/src/app/home/monitoring/components/MessageDetailsCard.tsx new file mode 100644 index 00000000..723ec289 --- /dev/null +++ b/web/src/app/home/monitoring/components/MessageDetailsCard.tsx @@ -0,0 +1,292 @@ +'use client'; + +import React, { useMemo } from 'react'; +import { useTranslation } from 'react-i18next'; +import { MessageDetails } from '../types/monitoring'; + +interface MessageDetailsCardProps { + details: MessageDetails; +} + +export function MessageDetailsCard({ details }: MessageDetailsCardProps) { + const { t } = useTranslation(); + + // Parse query variables JSON string + const queryVariables = useMemo(() => { + if (!details.message?.variables) return null; + try { + return JSON.parse(details.message.variables); + } catch { + return null; + } + }, [details.message?.variables]); + + return ( +
+ {/* Context Info Section */} + {details.message && ( +
+

+ + + + {t('monitoring.messageList.viewDetails')} +

+ + {/* Metadata Grid */} +
+ {details.message.platform && ( +
+
+ {t('monitoring.messageList.platform')} +
+
+ {details.message.platform} +
+
+ )} + {details.message.userId && ( +
+
+ {t('monitoring.messageList.user')} +
+
+ {details.message.userId} +
+
+ )} + {details.message.runnerName && ( +
+
+ {t('monitoring.messageList.runner')} +
+
+ {details.message.runnerName} +
+
+ )} +
+
+ {t('monitoring.messageList.level')} +
+
+ {details.message.level.toUpperCase()} +
+
+
+
+ )} + + {/* LLM Calls Section */} + {details.llmCalls && details.llmCalls.length > 0 && ( +
+

+ + + + {t('monitoring.llmCalls.title')} ({details.llmCalls.length}) +

+ + {/* LLM Stats Summary */} +
+
+
+ {t('monitoring.llmCalls.totalTokens')} +
+
+ {details.llmStats.totalTokens.toLocaleString()} +
+
+
+
+ {t('monitoring.llmCalls.avgDuration')} +
+
+ {details.llmStats.averageDurationMs}ms +
+
+
+
+ {t('monitoring.llmCalls.calls')} +
+
+ {details.llmStats.totalCalls} +
+
+
+ + {/* Individual LLM Calls */} +
+ {details.llmCalls.map((call, index) => ( +
+
+
+ + #{index + 1} {call.modelName} + + + {call.status} + +
+ + {call.duration}ms + +
+
+
+ + In: + {' '} + {call.tokens.input.toLocaleString()} +
+
+ + Out: + {' '} + {call.tokens.output.toLocaleString()} +
+
+ + Total: + {' '} + {call.tokens.total.toLocaleString()} +
+
+ {call.errorMessage && ( +
+ {call.errorMessage} +
+ )} +
+ ))} +
+
+ )} + + {/* Errors Section */} + {details.errors && details.errors.length > 0 && ( +
+

+ + + + {t('monitoring.errors.title')} ({details.errors.length}) +

+
+ {details.errors.map((error) => ( +
+
+ {error.errorType} +
+
+ {error.errorMessage} +
+ {error.stackTrace && ( +
+ + {t('monitoring.errors.stackTrace')} + + 
+                      {error.stackTrace}
+
+
+ )} +
+ ))} +
+
+ )} + + {/* Query Variables Section - Only show for non-local-agent runners */} + {queryVariables && + Object.keys(queryVariables).length > 0 && + details.message?.runnerName !== 'local-agent' && ( +
+

+ + + + {t('monitoring.queryVariables.title')} +

+
+ {Object.entries(queryVariables).map(([key, value]) => ( +
+
{key}
+
+ {value === null || value === undefined ? ( + null + ) : typeof value === 'string' ? ( + value || ( + empty + ) + ) : ( + JSON.stringify(value) + )} +
+
+ ))} +
+
+ )} + + {/* No data message */} + {(!details.llmCalls || details.llmCalls.length === 0) && + (!details.errors || details.errors.length === 0) && + (details.message?.runnerName === 'local-agent' || + !queryVariables || + Object.keys(queryVariables).length === 0) && ( +
+ {t('monitoring.messageDetails.noData')} +
+ )} +
+ ); +} diff --git a/web/src/app/home/monitoring/components/filters/MonitoringFilters.tsx b/web/src/app/home/monitoring/components/filters/MonitoringFilters.tsx new file mode 100644 index 00000000..4b9b1324 --- /dev/null +++ b/web/src/app/home/monitoring/components/filters/MonitoringFilters.tsx @@ -0,0 +1,209 @@ +'use client'; + +import React, { useEffect, useState } from 'react'; +import { useTranslation } from 'react-i18next'; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from '@/components/ui/select'; +import { backendClient } from '@/app/infra/http'; +import { TimeRangeOption } from '../../types/monitoring'; + +interface MonitoringFiltersProps { + selectedBots: string[]; + selectedPipelines: string[]; + timeRange: TimeRangeOption; + onBotsChange: (bots: string[]) => void; + onPipelinesChange: (pipelines: string[]) => void; + onTimeRangeChange: (timeRange: TimeRangeOption) => void; +} + +interface Bot { + uuid: string; + name: string; +} + +interface Pipeline { + uuid: string; + name: string; +} + +export default function MonitoringFilters({ + selectedBots, + selectedPipelines, + timeRange, + onBotsChange, + onPipelinesChange, + onTimeRangeChange, +}: MonitoringFiltersProps) { + const { t } = useTranslation(); + const [bots, setBots] = useState([]); + const [pipelines, setPipelines] = useState([]); + const [loadingBots, setLoadingBots] = useState(false); + const [loadingPipelines, setLoadingPipelines] = useState(false); + + // Fetch bots list + useEffect(() => { + const fetchBots = async () => { + setLoadingBots(true); + try { + const response = await backendClient.getBots(); + // Filter out bots without uuid and map to local Bot interface + const validBots = (response.bots || []) + .filter((bot): bot is typeof bot & { uuid: string } => !!bot.uuid) + .map((bot) => ({ uuid: bot.uuid, name: bot.name })); + setBots(validBots); + } catch (error) { + console.error('Failed to fetch bots:', error); + } finally { + setLoadingBots(false); + } + }; + + fetchBots(); + }, []); + + // Fetch pipelines list + useEffect(() => { + const fetchPipelines = async () => { + setLoadingPipelines(true); + try { + const response = await backendClient.getPipelines(); + // Filter out pipelines without uuid and map to local Pipeline interface + const validPipelines = (response.pipelines || []) + .filter( + (pipeline): pipeline is typeof pipeline & { uuid: string } => + !!pipeline.uuid, + ) + .map((pipeline) => ({ uuid: pipeline.uuid, name: pipeline.name })); + setPipelines(validPipelines); + } catch (error) { + console.error('Failed to fetch pipelines:', error); + } finally { + setLoadingPipelines(false); + } + }; + + fetchPipelines(); + }, []); + + const handleBotChange = (value: string) => { + if (value === 'all') { + onBotsChange([]); + } else { + onBotsChange([value]); + } + }; + + const handlePipelineChange = (value: string) => { + if (value === 'all') { + onPipelinesChange([]); + } else { + onPipelinesChange([value]); + } + }; + + const handleTimeRangeChange = (value: string) => { + onTimeRangeChange(value as TimeRangeOption); + }; + + return ( +
+ {/* Bot Filter */} +
+ + +
+ + {/* Pipeline Filter */} +
+ + +
+ + {/* Time Range Filter */} +
+ + +
+
+ ); +} diff --git a/web/src/app/home/monitoring/components/overview-cards/MetricCard.tsx b/web/src/app/home/monitoring/components/overview-cards/MetricCard.tsx new file mode 100644 index 00000000..821a4558 --- /dev/null +++ b/web/src/app/home/monitoring/components/overview-cards/MetricCard.tsx @@ -0,0 +1,93 @@ +'use client'; + +import React from 'react'; +import { Card, CardHeader, CardTitle, CardContent } from '@/components/ui/card'; + +interface MetricCardProps { + title: string; + value: string | number; + icon: React.ReactNode; + trend?: { + value: number; + direction: 'up' | 'down'; + }; + loading?: boolean; +} + +export default function MetricCard({ + title, + value, + icon, + trend, + loading, +}: MetricCardProps) { + if (loading) { + return ( + + + + {title} + +
+
+ {icon} +
+
+ + +
+
+ + + ); + } + + return ( + + + + {title} + +
+
{icon}
+
+ + +
+ {value} +
+ {trend && ( +
+ + + {trend.direction === 'up' ? ( + + ) : ( + + )} + + {Math.abs(trend.value)}% + + + vs previous period + +
+ )} + + + ); +} diff --git a/web/src/app/home/monitoring/components/overview-cards/OverviewCards.tsx b/web/src/app/home/monitoring/components/overview-cards/OverviewCards.tsx new file mode 100644 index 00000000..5a097a16 --- /dev/null +++ b/web/src/app/home/monitoring/components/overview-cards/OverviewCards.tsx @@ -0,0 +1,135 @@ +'use client'; + +import React from 'react'; +import { useTranslation } from 'react-i18next'; +import MetricCard from './MetricCard'; +import TrafficChart from './TrafficChart'; +import { + OverviewMetrics, + MonitoringMessage, + LLMCall, +} from '../../types/monitoring'; + +interface OverviewCardsProps { + metrics: OverviewMetrics | null; + messages?: MonitoringMessage[]; + llmCalls?: LLMCall[]; + loading?: boolean; +} + +export default function OverviewCards({ + metrics, + messages = [], + llmCalls = [], + loading, +}: OverviewCardsProps) { + const { t } = useTranslation(); + + const cards = [ + { + title: t('monitoring.totalMessages'), + value: metrics?.totalMessages || 0, + icon: ( + + + + ), + trend: metrics?.trends + ? { + value: metrics.trends.messages, + direction: (metrics.trends.messages >= 0 ? 'up' : 'down') as + | 'up' + | 'down', + } + : undefined, + }, + { + title: t('monitoring.modelCallsCount'), + value: metrics?.modelCalls || 0, + icon: ( + + + + ), + trend: metrics?.trends + ? { + value: metrics.trends.llmCalls, + direction: (metrics.trends.llmCalls >= 0 ? 'up' : 'down') as + | 'up' + | 'down', + } + : undefined, + }, + { + title: t('monitoring.successRate'), + value: metrics ? `${metrics.successRate}%` : '0%', + icon: ( + + + + ), + trend: metrics?.trends + ? { + value: metrics.trends.successRate, + direction: (metrics.trends.successRate >= 0 ? 'up' : 'down') as + | 'up' + | 'down', + } + : undefined, + }, + { + title: t('monitoring.activeSessions'), + value: metrics?.activeSessions || 0, + icon: ( + + + + ), + trend: metrics?.trends + ? { + value: metrics.trends.sessions, + direction: (metrics.trends.sessions >= 0 ? 'up' : 'down') as + | 'up' + | 'down', + } + : undefined, + }, + ]; + + return ( +
+ {/* Metric Cards */} +
+ {cards.map((card, index) => ( + + ))} +
+ + {/* Traffic Chart */} + +
+ ); +} diff --git a/web/src/app/home/monitoring/components/overview-cards/TrafficChart.tsx b/web/src/app/home/monitoring/components/overview-cards/TrafficChart.tsx new file mode 100644 index 00000000..f4ac6bbd --- /dev/null +++ b/web/src/app/home/monitoring/components/overview-cards/TrafficChart.tsx @@ -0,0 +1,263 @@ +'use client'; + +import React, { useMemo } from 'react'; +import { useTranslation } from 'react-i18next'; +import { + AreaChart, + Area, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + ResponsiveContainer, + Legend, +} from 'recharts'; +import { MonitoringMessage, LLMCall } from '../../types/monitoring'; + +interface TrafficChartProps { + messages: MonitoringMessage[]; + llmCalls: LLMCall[]; + loading?: boolean; +} + +interface ChartDataPoint { + time: string; + timestamp: number; + messages: number; + llmCalls: number; +} + +export default function TrafficChart({ + messages, + llmCalls, + loading, +}: TrafficChartProps) { + const { t } = useTranslation(); + + const chartData = useMemo(() => { + if (!messages.length && !llmCalls.length) { + return []; + } + + // Combine all timestamps and find the range + const allTimestamps = [ + ...messages.map((m) => m.timestamp.getTime()), + ...llmCalls.map((c) => c.timestamp.getTime()), + ]; + + if (allTimestamps.length === 0) return []; + + const minTime = Math.min(...allTimestamps); + const maxTime = Math.max(...allTimestamps); + const timeRange = maxTime - minTime; + + // Determine bucket size based on time range + let bucketSize: number; + let formatTime: (date: Date) => string; + + if (timeRange <= 60 * 60 * 1000) { + // <= 1 hour: 5-minute buckets + bucketSize = 5 * 60 * 1000; + formatTime = (date) => + date.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' }); + } else if (timeRange <= 6 * 60 * 60 * 1000) { + // <= 6 hours: 15-minute buckets + bucketSize = 15 * 60 * 1000; + formatTime = (date) => + date.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' }); + } else if (timeRange <= 24 * 60 * 60 * 1000) { + // <= 24 hours: 1-hour buckets + bucketSize = 60 * 60 * 1000; + formatTime = (date) => + date.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' }); + } else if (timeRange <= 7 * 24 * 60 * 60 * 1000) { + // <= 7 days: 4-hour buckets + bucketSize = 4 * 60 * 60 * 1000; + formatTime = (date) => + `${date.toLocaleDateString([], { month: 'short', day: 'numeric' })} ${date.toLocaleTimeString([], { hour: '2-digit' })}`; + } else { + // > 7 days: 1-day buckets + bucketSize = 24 * 60 * 60 * 1000; + formatTime = (date) => + date.toLocaleDateString([], { month: 'short', day: 'numeric' }); + } + + // Create buckets + const buckets: Map = new Map(); + const startBucket = Math.floor(minTime / bucketSize) * bucketSize; + const endBucket = Math.ceil(maxTime / bucketSize) * bucketSize; + + for (let bucket = startBucket; bucket <= endBucket; bucket += bucketSize) { + buckets.set(bucket, { + time: formatTime(new Date(bucket)), + timestamp: bucket, + messages: 0, + llmCalls: 0, + }); + } + + // Count messages per bucket + messages.forEach((msg) => { + const bucket = + Math.floor(msg.timestamp.getTime() / bucketSize) * bucketSize; + const point = buckets.get(bucket); + if (point) { + point.messages++; + } + }); + + // Count LLM calls per bucket + llmCalls.forEach((call) => { + const bucket = + Math.floor(call.timestamp.getTime() / bucketSize) * bucketSize; + const point = buckets.get(bucket); + if (point) { + point.llmCalls++; + } + }); + + return Array.from(buckets.values()).sort( + (a, b) => a.timestamp - b.timestamp, + ); + }, [messages, llmCalls]); + + if (loading) { + return ( +
+
+
+
+
+
+
+
+
+
+
+
+ ); + } + + if (chartData.length === 0) { + return ( +
+

+ {t('monitoring.trafficChart.title')} +

+
+ + + +

+ {t('monitoring.trafficChart.noData')} +

+
+
+ ); + } + + return ( +
+

+ {t('monitoring.trafficChart.title')} +

+
+ + + + + + + + + + + + + + + + + + + + + +
+
+ ); +} diff --git a/web/src/app/home/monitoring/hooks/useMonitoringData.ts b/web/src/app/home/monitoring/hooks/useMonitoringData.ts new file mode 100644 index 00000000..f6dfc985 --- /dev/null +++ b/web/src/app/home/monitoring/hooks/useMonitoringData.ts @@ -0,0 +1,352 @@ +import { useState, useEffect, useCallback, useMemo } from 'react'; +import { + FilterState, + MonitoringData, + ModelCall, + LLMCall, + EmbeddingCall, +} from '../types/monitoring'; +import { backendClient } from '@/app/infra/http'; + +/** + * Custom hook for fetching and managing monitoring data + */ +export function useMonitoringData(filterState: FilterState) { + const [data, setData] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + // Memoize filter parameters to prevent unnecessary re-renders + const selectedBotsStr = useMemo( + () => JSON.stringify(filterState.selectedBots), + [filterState.selectedBots], + ); + const selectedPipelinesStr = useMemo( + () => JSON.stringify(filterState.selectedPipelines), + [filterState.selectedPipelines], + ); + const customDateRangeStr = useMemo( + () => JSON.stringify(filterState.customDateRange), + [filterState.customDateRange], + ); + + // Convert time range to datetime strings + const getTimeRange = useCallback(() => { + const now = new Date(); + let startTime: Date | null = null; + + switch (filterState.timeRange) { + case 'lastHour': + startTime = new Date(now.getTime() - 60 * 60 * 1000); + break; + case 'last6Hours': + startTime = new Date(now.getTime() - 6 * 60 * 60 * 1000); + break; + case 'last24Hours': + startTime = new Date(now.getTime() - 24 * 60 * 60 * 1000); + break; + case 'last7Days': + startTime = new Date(now.getTime() - 7 * 24 * 60 * 60 * 1000); + break; + case 'last30Days': + startTime = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000); + break; + case 'custom': + if (filterState.customDateRange) { + startTime = filterState.customDateRange.from; + } + break; + } + + const endTime = + filterState.timeRange === 'custom' && filterState.customDateRange + ? filterState.customDateRange.to + : now; + + return { + startTime: startTime?.toISOString(), + endTime: endTime.toISOString(), + }; + }, [filterState.timeRange, filterState.customDateRange]); + + // Fetch data based on filters + const fetchData = useCallback(async () => { + setLoading(true); + setError(null); + + try { + const { startTime, endTime } = getTimeRange(); + + const response = await backendClient.getMonitoringData({ + botId: + filterState.selectedBots.length > 0 + ? filterState.selectedBots + : undefined, + pipelineId: + filterState.selectedPipelines.length > 0 + ? filterState.selectedPipelines + : undefined, + startTime, + endTime, + limit: 50, + }); + + // Transform the response to match MonitoringData interface + const transformedData: MonitoringData = { + overview: { + totalMessages: response.overview.total_messages, + llmCalls: response.overview.llm_calls, + embeddingCalls: response.overview.embedding_calls || 0, + modelCalls: + response.overview.model_calls || response.overview.llm_calls, + successRate: response.overview.success_rate, + activeSessions: response.overview.active_sessions, + }, + messages: response.messages.map( + (msg: { + id: string; + timestamp: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + message_content: string; + session_id: string; + status: string; + level: string; + platform?: string; + user_id?: string; + runner_name?: string; + variables?: string; + }) => ({ + id: msg.id, + timestamp: new Date(msg.timestamp), + botId: msg.bot_id, + botName: msg.bot_name, + pipelineId: msg.pipeline_id, + pipelineName: msg.pipeline_name, + messageContent: msg.message_content, + sessionId: msg.session_id, + status: msg.status as 'success' | 'error' | 'pending', + level: msg.level as 'info' | 'warning' | 'error' | 'debug', + platform: msg.platform, + userId: msg.user_id, + runnerName: msg.runner_name, + variables: msg.variables, + }), + ), + llmCalls: response.llmCalls.map( + (call: { + id: string; + timestamp: string; + model_name: string; + input_tokens: number; + output_tokens: number; + total_tokens: number; + duration: number; + cost?: number; + status: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + error_message?: string; + message_id?: string; + }) => ({ + id: call.id, + timestamp: new Date(call.timestamp), + modelName: call.model_name, + tokens: { + input: call.input_tokens, + output: call.output_tokens, + total: call.total_tokens, + }, + duration: call.duration, + cost: call.cost, + status: call.status as 'success' | 'error', + botId: call.bot_id, + botName: call.bot_name, + pipelineId: call.pipeline_id, + pipelineName: call.pipeline_name, + errorMessage: call.error_message, + messageId: call.message_id, + }), + ), + embeddingCalls: (response.embeddingCalls || []).map( + (call: { + id: string; + timestamp: string; + model_name: string; + prompt_tokens: number; + total_tokens: number; + duration: number; + input_count: number; + status: string; + error_message?: string; + knowledge_base_id?: string; + query_text?: string; + session_id?: string; + message_id?: string; + call_type?: string; + }) => ({ + id: call.id, + timestamp: new Date(call.timestamp), + modelName: call.model_name, + promptTokens: call.prompt_tokens, + totalTokens: call.total_tokens, + duration: call.duration, + inputCount: call.input_count, + status: call.status as 'success' | 'error', + errorMessage: call.error_message, + knowledgeBaseId: call.knowledge_base_id, + queryText: call.query_text, + sessionId: call.session_id, + messageId: call.message_id, + callType: call.call_type as 'embedding' | 'retrieve' | undefined, + }), + ), + // Create merged modelCalls array from llmCalls and embeddingCalls + modelCalls: [] as ModelCall[], // Will be populated after transform + sessions: response.sessions.map( + (session: { + session_id: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + message_count: number; + last_activity: string; + start_time: string; + platform?: string; + user_id?: string; + }) => ({ + sessionId: session.session_id, + botId: session.bot_id, + botName: session.bot_name, + pipelineId: session.pipeline_id, + pipelineName: session.pipeline_name, + messageCount: session.message_count, + duration: + new Date(session.last_activity).getTime() - + new Date(session.start_time).getTime(), + lastActivity: new Date(session.last_activity), + startTime: new Date(session.start_time), + platform: session.platform, + userId: session.user_id, + }), + ), + errors: response.errors.map( + (error: { + id: string; + timestamp: string; + error_type: string; + error_message: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + session_id?: string; + stack_trace?: string; + message_id?: string; + }) => ({ + id: error.id, + timestamp: new Date(error.timestamp), + errorType: error.error_type, + errorMessage: error.error_message, + botId: error.bot_id, + botName: error.bot_name, + pipelineId: error.pipeline_id, + pipelineName: error.pipeline_name, + sessionId: error.session_id, + stackTrace: error.stack_trace, + messageId: error.message_id, + }), + ), + totalCount: { + messages: response.totalCount.messages, + llmCalls: response.totalCount.llmCalls, + embeddingCalls: response.totalCount.embeddingCalls || 0, + sessions: response.totalCount.sessions, + errors: response.totalCount.errors, + }, + }; + + // Merge LLM calls and embedding calls into modelCalls + const llmModelCalls: ModelCall[] = transformedData.llmCalls.map( + (call: LLMCall): ModelCall => ({ + id: call.id, + timestamp: call.timestamp, + modelName: call.modelName, + modelType: 'llm', + status: call.status, + duration: call.duration, + errorMessage: call.errorMessage, + messageId: call.messageId, + tokens: call.tokens, + cost: call.cost, + botId: call.botId, + botName: call.botName, + pipelineId: call.pipelineId, + pipelineName: call.pipelineName, + }), + ); + + const embeddingModelCalls: ModelCall[] = + transformedData.embeddingCalls.map( + (call: EmbeddingCall): ModelCall => ({ + id: call.id, + timestamp: call.timestamp, + modelName: call.modelName, + modelType: 'embedding', + status: call.status, + duration: call.duration, + errorMessage: call.errorMessage, + messageId: call.messageId, + callType: call.callType, + promptTokens: call.promptTokens, + totalTokens: call.totalTokens, + inputCount: call.inputCount, + knowledgeBaseId: call.knowledgeBaseId, + queryText: call.queryText, + sessionId: call.sessionId, + }), + ); + + // Combine and sort by timestamp (newest first) + transformedData.modelCalls = [ + ...llmModelCalls, + ...embeddingModelCalls, + ].sort((a, b) => b.timestamp.getTime() - a.timestamp.getTime()); + + setData(transformedData); + } catch (err) { + setError(err as Error); + console.error('Failed to fetch monitoring data:', err); + } finally { + setLoading(false); + } + }, [getTimeRange, filterState.selectedBots, filterState.selectedPipelines]); + + // Fetch data when filter state changes + useEffect(() => { + fetchData(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [ + selectedBotsStr, + selectedPipelinesStr, + filterState.timeRange, + customDateRangeStr, + ]); + + // Manual refetch function + const refetch = () => { + fetchData(); + }; + + return { + data, + loading, + error, + refetch, + }; +} diff --git a/web/src/app/home/monitoring/hooks/useMonitoringFilters.ts b/web/src/app/home/monitoring/hooks/useMonitoringFilters.ts new file mode 100644 index 00000000..aaa914f0 --- /dev/null +++ b/web/src/app/home/monitoring/hooks/useMonitoringFilters.ts @@ -0,0 +1,65 @@ +import { useState } from 'react'; +import { useSearchParams } from 'next/navigation'; +import { FilterState, TimeRangeOption, DateRange } from '../types/monitoring'; +import { getPresetDateRange } from '../utils/dateUtils'; + +/** + * Custom hook for managing monitoring filters + */ +export function useMonitoringFilters() { + const searchParams = useSearchParams(); + + // Initialize filters from URL params + const [selectedBots, setSelectedBots] = useState(() => { + const botId = searchParams.get('botId'); + return botId ? [botId] : []; + }); + + const [selectedPipelines, setSelectedPipelines] = useState(() => { + const pipelineId = searchParams.get('pipelineId'); + return pipelineId ? [pipelineId] : []; + }); + + const [timeRange, setTimeRange] = useState('last24Hours'); + const [customDateRange, setCustomDateRange] = useState( + null, + ); + + // Get the active date range (either preset or custom) + const getActiveDateRange = (): DateRange | null => { + if (timeRange === 'custom' && customDateRange) { + return customDateRange; + } + return getPresetDateRange(timeRange); + }; + + // Reset all filters + const resetFilters = () => { + setSelectedBots([]); + setSelectedPipelines([]); + setTimeRange('last24Hours'); + setCustomDateRange(null); + }; + + // Get the current filter state + const filterState: FilterState = { + selectedBots, + selectedPipelines, + timeRange, + customDateRange, + }; + + return { + selectedBots, + setSelectedBots, + selectedPipelines, + setSelectedPipelines, + timeRange, + setTimeRange, + customDateRange, + setCustomDateRange, + getActiveDateRange, + resetFilters, + filterState, + }; +} diff --git a/web/src/app/home/monitoring/page.tsx b/web/src/app/home/monitoring/page.tsx new file mode 100644 index 00000000..c6d83336 --- /dev/null +++ b/web/src/app/home/monitoring/page.tsx @@ -0,0 +1,817 @@ +'use client'; + +import React, { Suspense, useState } from 'react'; +import { useTranslation } from 'react-i18next'; +import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs'; +import { Button } from '@/components/ui/button'; +import { ChevronRight, ChevronDown, ExternalLink } from 'lucide-react'; +import OverviewCards from './components/overview-cards/OverviewCards'; +import MonitoringFilters from './components/filters/MonitoringFilters'; +import { useMonitoringFilters } from './hooks/useMonitoringFilters'; +import { useMonitoringData } from './hooks/useMonitoringData'; +import { MessageDetailsCard } from './components/MessageDetailsCard'; +import { MessageContentRenderer } from './components/MessageContentRenderer'; +import { MessageDetails } from './types/monitoring'; +import { httpClient } from '@/app/infra/http/HttpClient'; + +interface RawMessageData { + id: string; + timestamp: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + message_content: string; + session_id: string; + status: string; + level: string; + platform: string; + user_id: string; + runner_name: string; + variables: Record; +} + +interface RawLLMCallData { + id: string; + timestamp: string; + model_name: string; + status: string; + duration: number; + error_message: string | null; + input_tokens: number; + output_tokens: number; + total_tokens: number; +} + +interface RawLLMStatsData { + total_calls: number; + total_input_tokens: number; + total_output_tokens: number; + total_tokens: number; + total_duration_ms: number; + average_duration_ms: number; +} + +interface RawErrorData { + id: string; + timestamp: string; + error_type: string; + error_message: string; + stack_trace: string | null; +} + +function MonitoringPageContent() { + const { t } = useTranslation(); + const { filterState, setSelectedBots, setSelectedPipelines, setTimeRange } = + useMonitoringFilters(); + const { data, loading, refetch } = useMonitoringData(filterState); + + const [expandedMessageId, setExpandedMessageId] = useState( + null, + ); + const [messageDetails, setMessageDetails] = useState< + Record + >({}); + const [loadingDetails, setLoadingDetails] = useState>( + {}, + ); + + // State for expanded errors + const [expandedErrorId, setExpandedErrorId] = useState(null); + + // State for controlled tabs + const [activeTab, setActiveTab] = useState('messages'); + + // Function to jump to a message record + const jumpToMessage = async (messageId: string) => { + setActiveTab('messages'); + // Small delay to ensure tab switch completes + setTimeout(() => { + toggleMessageExpand(messageId); + }, 100); + }; + + const toggleMessageExpand = async (messageId: string) => { + if (expandedMessageId === messageId) { + // Collapse + setExpandedMessageId(null); + } else { + // Expand + setExpandedMessageId(messageId); + + // Fetch details if not already loaded + if (!messageDetails[messageId]) { + setLoadingDetails({ ...loadingDetails, [messageId]: true }); + try { + // httpClient.get() returns the inner data directly (response.data.data) + const result = await httpClient.get<{ + message_id: string; + found: boolean; + message: RawMessageData | null; + llm_calls: RawLLMCallData[]; + llm_stats: RawLLMStatsData; + errors: RawErrorData[]; + }>(`/api/v1/monitoring/messages/${messageId}/details`); + + if (result) { + setMessageDetails((prev) => ({ + ...prev, + [messageId]: { + messageId: result.message_id, + found: result.found, + message: result.message + ? { + id: result.message.id, + timestamp: new Date(result.message.timestamp), + botId: result.message.bot_id, + botName: result.message.bot_name, + pipelineId: result.message.pipeline_id, + pipelineName: result.message.pipeline_name, + messageContent: result.message.message_content, + sessionId: result.message.session_id, + status: result.message.status, + level: result.message.level, + platform: result.message.platform, + userId: result.message.user_id, + runnerName: result.message.runner_name, + variables: result.message.variables, + } + : undefined, + llmCalls: result.llm_calls.map((call: RawLLMCallData) => ({ + id: call.id, + timestamp: new Date(call.timestamp), + modelName: call.model_name, + status: call.status, + duration: call.duration, + errorMessage: call.error_message, + tokens: { + input: call.input_tokens || 0, + output: call.output_tokens || 0, + total: call.total_tokens || 0, + }, + })), + errors: result.errors.map((error: RawErrorData) => ({ + id: error.id, + timestamp: new Date(error.timestamp), + errorType: error.error_type, + errorMessage: error.error_message, + stackTrace: error.stack_trace, + })), + llmStats: { + totalCalls: result.llm_stats.total_calls, + totalInputTokens: result.llm_stats.total_input_tokens, + totalOutputTokens: result.llm_stats.total_output_tokens, + totalTokens: result.llm_stats.total_tokens, + totalDurationMs: result.llm_stats.total_duration_ms, + averageDurationMs: result.llm_stats.average_duration_ms, + }, + } as MessageDetails, + })); + } + } catch (error) { + console.error('Failed to fetch message details:', error); + } finally { + setLoadingDetails({ ...loadingDetails, [messageId]: false }); + } + } + } + }; + + const toggleErrorExpand = (errorId: string) => { + if (expandedErrorId === errorId) { + setExpandedErrorId(null); + } else { + setExpandedErrorId(errorId); + } + }; + + return ( +
+ {/* Filters and Refresh Button - Sticky */} +
+
+
+ + +
+
+
+ + {/* Content Area */} +
+ {/* Overview Section */} + + + {/* Tabs Section */} +
+ +
+ + + {t('monitoring.tabs.messages')} + + + {t('monitoring.tabs.modelCalls')} + + + {t('monitoring.tabs.errors')} + + +
+ + +
+ {loading && ( +
+
+

+ {t('monitoring.messageList.loading')} +

+
+ )} + + {!loading && + data && + data.messages && + data.messages.length > 0 && ( +
+ {data.messages + .filter((msg) => { + // Filter out messages with empty content + const content = msg.messageContent?.trim(); + return ( + content && content !== '[]' && content !== '""' + ); + }) + .map((msg) => ( +
+ {/* Message Header - Always Visible */} +
toggleMessageExpand(msg.id)} + > +
+
+ {/* Expand Icon */} +
+ {expandedMessageId === msg.id ? ( + + ) : ( + + )} +
+ + {/* Message Info */} +
+
+ + ID: {msg.id} + +
+
+ + {msg.botName} + + → + + {msg.pipelineName} + + {msg.runnerName && ( + <> + + → + + + {msg.runnerName} + + + )} +
+
+ +
+
+
+ + {/* Status and Timestamp */} +
+ + {msg.timestamp.toLocaleString()} + + + {msg.level} + +
+
+
+ + {/* Expanded Details */} + {expandedMessageId === msg.id && ( +
+ {loadingDetails[msg.id] && ( +
+
+
+ )} + {!loadingDetails[msg.id] && + messageDetails[msg.id] && ( + + )} +
+ )} +
+ ))} +
+ )} + + {!loading && + (!data || !data.messages || data.messages.length === 0) && ( +
+ + + +

+ {t('monitoring.messageList.noMessages')} +

+

+ {t('monitoring.messageList.noMessagesDescription')} +

+
+ )} +
+ + + +
+ {loading && ( +
+
+

{t('common.loading')}

+
+ )} + + {!loading && + data && + data.modelCalls && + data.modelCalls.length > 0 && ( +
+ {data.modelCalls.map((call) => ( +
+
+
+ {/* Query ID - only show if messageId exists */} + {call.messageId && ( +
+ + Query ID: {call.messageId} + + +
+ )} +
+ {/* Model Type Badge */} + + {call.modelType === 'llm' + ? t('monitoring.modelCalls.llmModel') + : t('monitoring.modelCalls.embeddingModel')} + + {/* Call Type Badge for Embedding */} + {call.modelType === 'embedding' && + call.callType && ( + + {call.callType === 'retrieve' + ? t( + 'monitoring.modelCalls.retrieveCall', + ) + : t( + 'monitoring.modelCalls.embeddingCall', + )} + + )} + {/* Status Badge */} + + {call.status} + +
+ {/* Model Name */} +
+ {call.modelName} +
+ {/* Context Info - only for LLM calls */} + {call.modelType === 'llm' && + call.botName && + call.pipelineName && ( +
+ {call.botName} → {call.pipelineName} +
+ )} + {/* Token Info */} +
+
+ {call.modelType === 'llm' && call.tokens && ( + <> + + {t('monitoring.llmCalls.inputTokens')}:{' '} + {call.tokens.input} + + + {t('monitoring.llmCalls.outputTokens')}:{' '} + {call.tokens.output} + + + {t('monitoring.llmCalls.totalTokens')}:{' '} + {call.tokens.total} + + + )} + {call.modelType === 'embedding' && ( + <> + + {t( + 'monitoring.embeddingCalls.promptTokens', + )} + : {call.promptTokens} + + + {t( + 'monitoring.embeddingCalls.totalTokens', + )} + : {call.totalTokens} + + + {t( + 'monitoring.embeddingCalls.inputCount', + )} + : {call.inputCount} + + + )} + + {t('monitoring.llmCalls.duration')}:{' '} + {call.duration}ms + + {call.cost && ( + + {t('monitoring.llmCalls.cost')}: $ + {call.cost.toFixed(4)} + + )} +
+ {/* Knowledge Base Info for Embedding */} + {call.modelType === 'embedding' && + call.knowledgeBaseId && ( +
+ {t( + 'monitoring.embeddingCalls.knowledgeBase', + )} + : {call.knowledgeBaseId} +
+ )} + {/* Query Text for Embedding Retrieve */} + {call.modelType === 'embedding' && + call.queryText && ( +
+ + {t( + 'monitoring.embeddingCalls.queryText', + )} + :{' '} + + + {call.queryText.length > 100 + ? call.queryText.substring(0, 100) + + '...' + : call.queryText} + +
+ )} +
+ {call.errorMessage && ( +
+ Error: {call.errorMessage} +
+ )} +
+ + {call.timestamp.toLocaleString()} + +
+
+ ))} +
+ )} + + {!loading && + (!data || + !data.modelCalls || + data.modelCalls.length === 0) && ( +
+ + + +

+ {t('monitoring.modelCalls.noData')} +

+
+ )} +
+ + + +
+ {loading && ( +
+
+

{t('common.loading')}

+
+ )} + + {!loading && data && data.errors && data.errors.length > 0 && ( +
+ {data.errors.map((error) => ( +
+ {/* Error Header - Always Visible */} +
toggleErrorExpand(error.id)} + > +
+
+ {/* Expand Icon */} +
+ {expandedErrorId === error.id ? ( + + ) : ( + + )} +
+ + {/* Error Info */} +
+ {/* Query ID */} +
+ + Query ID: {error.messageId || '-'} + + {error.messageId && ( + + )} +
+
+ + {error.errorType} + + → + + {error.botName} + + → + + {error.pipelineName} + +
+

+ {error.errorMessage} +

+
+
+ + {/* Timestamp */} +
+ + {error.timestamp.toLocaleString()} + +
+
+
+ + {/* Expanded Details */} + {expandedErrorId === error.id && ( +
+
+ {/* Error Details */} +
+

+ {t('monitoring.errors.errorMessage')} +

+
+ {error.errorMessage} +
+
+ + {/* Context Info */} +
+

+ {t('monitoring.messageList.viewDetails')} +

+
+
+
+ {t('monitoring.messageList.bot')} +
+
+ {error.botName} +
+
+
+
+ {t('monitoring.messageList.pipeline')} +
+
+ {error.pipelineName} +
+
+ {error.sessionId && ( +
+
+ {t('monitoring.sessions.sessionId')} +
+
+ {error.sessionId} +
+
+ )} +
+
+ + {/* Stack Trace */} + {error.stackTrace && ( +
+

+ {t('monitoring.errors.stackTrace')} +

+ 
+                                    {error.stackTrace}
+
+
+ )} +
+
+ )} +
+ ))} +
+ )} + + {!loading && + (!data || !data.errors || data.errors.length === 0) && ( +
+ + + +

+ {t('monitoring.errors.noErrors')} +

+
+ )} +
+ + +
+
+
+ ); +} + +export default function MonitoringPage() { + return ( + Loading...}> + + + ); +} diff --git a/web/src/app/home/monitoring/types/monitoring.ts b/web/src/app/home/monitoring/types/monitoring.ts new file mode 100644 index 00000000..e1eb18d8 --- /dev/null +++ b/web/src/app/home/monitoring/types/monitoring.ts @@ -0,0 +1,180 @@ +export interface MonitoringMessage { + id: string; + timestamp: Date; + botId: string; + botName: string; + pipelineId: string; + pipelineName: string; + messageContent: string; + sessionId: string; + status: 'success' | 'error' | 'pending'; + level: 'info' | 'warning' | 'error' | 'debug'; + platform?: string; + userId?: string; + runnerName?: string; + variables?: string; +} + +export interface LLMCall { + id: string; + timestamp: Date; + modelName: string; + tokens: { + input: number; + output: number; + total: number; + }; + duration: number; + cost?: number; + status: 'success' | 'error'; + botId: string; + botName: string; + pipelineId: string; + pipelineName: string; + errorMessage?: string; + messageId?: string; +} + +export interface EmbeddingCall { + id: string; + timestamp: Date; + modelName: string; + promptTokens: number; + totalTokens: number; + duration: number; + inputCount: number; + status: 'success' | 'error'; + errorMessage?: string; + knowledgeBaseId?: string; + queryText?: string; + sessionId?: string; + messageId?: string; + callType?: 'embedding' | 'retrieve'; +} + +// Unified model call type for displaying LLM and Embedding calls together +export interface ModelCall { + id: string; + timestamp: Date; + modelName: string; + modelType: 'llm' | 'embedding'; + status: 'success' | 'error'; + duration: number; + errorMessage?: string; + messageId?: string; + // LLM specific fields + tokens?: { + input: number; + output: number; + total: number; + }; + cost?: number; + botId?: string; + botName?: string; + pipelineId?: string; + pipelineName?: string; + // Embedding specific fields + callType?: 'embedding' | 'retrieve'; + promptTokens?: number; + totalTokens?: number; + inputCount?: number; + knowledgeBaseId?: string; + queryText?: string; + sessionId?: string; +} + +export interface SessionInfo { + sessionId: string; + botId: string; + botName: string; + pipelineId: string; + pipelineName: string; + messageCount: number; + duration: number; + lastActivity: Date; + startTime: Date; + platform?: string; + userId?: string; +} + +export interface ErrorLog { + id: string; + timestamp: Date; + errorType: string; + errorMessage: string; + botId: string; + botName: string; + pipelineId: string; + pipelineName: string; + sessionId?: string; + stackTrace?: string; + messageId?: string; +} + +export interface MessageDetails { + messageId: string; + found: boolean; + message?: MonitoringMessage; + llmCalls: LLMCall[]; + llmStats: { + totalCalls: number; + totalInputTokens: number; + totalOutputTokens: number; + totalTokens: number; + totalDurationMs: number; + averageDurationMs: number; + }; + errors: ErrorLog[]; +} + +export interface OverviewMetrics { + totalMessages: number; + llmCalls: number; + embeddingCalls: number; + modelCalls: number; + successRate: number; + activeSessions: number; + trends?: { + messages: number; + llmCalls: number; + successRate: number; + sessions: number; + }; +} + +export interface FilterState { + selectedBots: string[]; + selectedPipelines: string[]; + timeRange: TimeRangeOption; + customDateRange: DateRange | null; +} + +export type TimeRangeOption = + | 'lastHour' + | 'last6Hours' + | 'last24Hours' + | 'last7Days' + | 'last30Days' + | 'custom'; + +export interface DateRange { + from: Date; + to: Date; +} + +export interface MonitoringData { + overview: OverviewMetrics; + messages: MonitoringMessage[]; + llmCalls: LLMCall[]; + embeddingCalls: EmbeddingCall[]; + modelCalls: ModelCall[]; + sessions: SessionInfo[]; + errors: ErrorLog[]; + totalCount: { + messages: number; + llmCalls: number; + embeddingCalls: number; + sessions: number; + errors: number; + }; +} diff --git a/web/src/app/home/monitoring/utils/dateUtils.ts b/web/src/app/home/monitoring/utils/dateUtils.ts new file mode 100644 index 00000000..42ef8039 --- /dev/null +++ b/web/src/app/home/monitoring/utils/dateUtils.ts @@ -0,0 +1,99 @@ +import { DateRange, TimeRangeOption } from '../types/monitoring'; + +/** + * Get date range based on preset time range option + */ +export function getPresetDateRange(option: TimeRangeOption): DateRange | null { + if (option === 'custom') return null; + + const now = new Date(); + const from = new Date(); + + switch (option) { + case 'lastHour': + from.setHours(now.getHours() - 1); + break; + case 'last6Hours': + from.setHours(now.getHours() - 6); + break; + case 'last24Hours': + from.setHours(now.getHours() - 24); + break; + case 'last7Days': + from.setDate(now.getDate() - 7); + break; + case 'last30Days': + from.setDate(now.getDate() - 30); + break; + default: + return null; + } + + return { from, to: now }; +} + +/** + * Format timestamp to readable string + */ +export function formatTimestamp(date: Date): string { + const now = new Date(); + const diff = now.getTime() - date.getTime(); + const seconds = Math.floor(diff / 1000); + const minutes = Math.floor(seconds / 60); + const hours = Math.floor(minutes / 60); + const days = Math.floor(hours / 24); + + if (seconds < 60) return `${seconds}s ago`; + if (minutes < 60) return `${minutes}m ago`; + if (hours < 24) return `${hours}h ago`; + if (days < 7) return `${days}d ago`; + + return date.toLocaleString(); +} + +/** + * Format date to YYYY-MM-DD + */ +export function formatDate(date: Date): string { + const year = date.getFullYear(); + const month = String(date.getMonth() + 1).padStart(2, '0'); + const day = String(date.getDate()).padStart(2, '0'); + return `${year}-${month}-${day}`; +} + +/** + * Format date to YYYY-MM-DD HH:MM:SS + */ +export function formatDateTime(date: Date): string { + const dateStr = formatDate(date); + const hours = String(date.getHours()).padStart(2, '0'); + const minutes = String(date.getMinutes()).padStart(2, '0'); + const seconds = String(date.getSeconds()).padStart(2, '0'); + return `${dateStr} ${hours}:${minutes}:${seconds}`; +} + +/** + * Format duration in seconds to readable string + */ +export function formatDuration(seconds: number): string { + if (seconds < 60) return `${seconds}s`; + const minutes = Math.floor(seconds / 60); + if (minutes < 60) return `${minutes}m ${seconds % 60}s`; + const hours = Math.floor(minutes / 60); + return `${hours}h ${minutes % 60}m`; +} + +/** + * Check if date is within range + */ +export function isDateInRange(date: Date, range: DateRange | null): boolean { + if (!range) return true; + return date >= range.from && date <= range.to; +} + +/** + * Parse date string to Date object + */ +export function parseDate(dateStr: string): Date { + return new Date(dateStr); +} diff --git a/web/src/app/home/pipelines/PipelineDetailDialog.tsx b/web/src/app/home/pipelines/PipelineDetailDialog.tsx index b58afaa9..353b6e82 100644 --- a/web/src/app/home/pipelines/PipelineDetailDialog.tsx +++ b/web/src/app/home/pipelines/PipelineDetailDialog.tsx @@ -1,11 +1,13 @@ import React, { useState, useEffect } from 'react'; import { useTranslation } from 'react-i18next'; +import { useRouter } from 'next/navigation'; import { Dialog, DialogContent, DialogHeader, DialogTitle, } from '@/components/ui/dialog'; +import { Button } from '@/components/ui/button'; import { Sidebar, SidebarContent, @@ -45,6 +47,7 @@ export default function PipelineDialog({ onCancel, }: PipelineDialogProps) { const { t } = useTranslation(); + const router = useRouter(); const [pipelineId, setPipelineId] = useState( propPipelineId, ); @@ -190,23 +193,48 @@ export default function PipelineDialog({ > {getDialogTitle()} {currentMode === 'debug' && ( -
-
+
+
+ + {isWebSocketConnected ? t('pipelines.debugDialog.connected') - : t('pipelines.debugDialog.disconnected') - } - /> - - {isWebSocketConnected - ? t('pipelines.debugDialog.connected') - : t('pipelines.debugDialog.disconnected')} - -
+ : t('pipelines.debugDialog.disconnected')} + +
+
+ +
+ )}
; + llmCalls: Array<{ + id: string; + timestamp: string; + model_name: string; + input_tokens: number; + output_tokens: number; + total_tokens: number; + duration: number; + cost?: number; + status: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + error_message?: string; + message_id?: string; + }>; + embeddingCalls: Array<{ + id: string; + timestamp: string; + model_name: string; + prompt_tokens: number; + total_tokens: number; + duration: number; + input_count: number; + status: string; + error_message?: string; + knowledge_base_id?: string; + query_text?: string; + session_id?: string; + message_id?: string; + call_type?: string; + }>; + sessions: Array<{ + session_id: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + message_count: number; + last_activity: string; + start_time: string; + platform?: string; + user_id?: string; + }>; + errors: Array<{ + id: string; + timestamp: string; + error_type: string; + error_message: string; + bot_id: string; + bot_name: string; + pipeline_id: string; + pipeline_name: string; + session_id?: string; + stack_trace?: string; + message_id?: string; + }>; + totalCount: { + messages: number; + llmCalls: number; + embeddingCalls: number; + sessions: number; + errors: number; + }; + }> { + const queryParams = new URLSearchParams(); + if (params.botId) { + params.botId.forEach((id) => queryParams.append('botId', id)); + } + if (params.pipelineId) { + params.pipelineId.forEach((id) => queryParams.append('pipelineId', id)); + } + if (params.startTime) { + queryParams.append('startTime', params.startTime); + } + if (params.endTime) { + queryParams.append('endTime', params.endTime); + } + if (params.limit) { + queryParams.append('limit', params.limit.toString()); + } + + return this.get(`/api/v1/monitoring/data?${queryParams.toString()}`); + } + + public getMonitoringOverview(params: { + botId?: string[]; + pipelineId?: string[]; + startTime?: string; + endTime?: string; + }): Promise<{ + total_messages: number; + llm_calls: number; + success_rate: number; + active_sessions: number; + }> { + const queryParams = new URLSearchParams(); + if (params.botId) { + params.botId.forEach((id) => queryParams.append('botId', id)); + } + if (params.pipelineId) { + params.pipelineId.forEach((id) => queryParams.append('pipelineId', id)); + } + if (params.startTime) { + queryParams.append('startTime', params.startTime); + } + if (params.endTime) { + queryParams.append('endTime', params.endTime); + } + + return this.get(`/api/v1/monitoring/overview?${queryParams.toString()}`); + } } diff --git a/web/src/i18n/locales/en-US.ts b/web/src/i18n/locales/en-US.ts index fbada358..eb6aba89 100644 --- a/web/src/i18n/locales/en-US.ts +++ b/web/src/i18n/locales/en-US.ts @@ -276,6 +276,10 @@ const enUS = { allLevels: 'All Levels', selectLevel: 'Select Level', levelsSelected: 'levels selected', + viewDetailedLogs: 'View Detailed Logs', + viewDetails: 'Details', + collapse: 'Collapse', + imagesAttached: 'image(s) attached', }, plugins: { title: 'Extensions', @@ -802,6 +806,140 @@ const enUS = { spaceEmailMismatch: 'Space login email does not match the local account email', }, + monitoring: { + title: 'Monitoring', + description: 'Monitor bot activities, LLM calls, and system performance', + overview: 'Overview', + totalMessages: 'Total Messages', + llmCallsCount: 'LLM Calls', + modelCallsCount: 'Model Calls', + successRate: 'Success Rate', + activeSessions: 'Active Sessions', + last24Hours: 'Last 24 hours', + filters: { + title: 'Filters', + bot: 'Bot', + pipeline: 'Pipeline', + allBots: 'All Bots', + selectBot: 'Select Bot', + allPipelines: 'All Pipelines', + selectPipeline: 'Select Pipeline', + loading: 'Loading...', + timeRange: 'Time Range', + customRange: 'Custom Range', + from: 'From', + to: 'To', + apply: 'Apply', + reset: 'Reset Filters', + lastHour: 'Last 1 hour', + last6Hours: 'Last 6 hours', + last24Hours: 'Last 24 hours', + last7Days: 'Last 7 days', + last30Days: 'Last 30 days', + }, + tabs: { + messages: 'Message Records', + llmCalls: 'LLM Calls', + embeddingCalls: 'Embedding Calls', + modelCalls: 'Model Calls', + sessions: 'Session Analysis', + errors: 'Error Logs', + }, + messageList: { + timestamp: 'Timestamp', + bot: 'Bot', + pipeline: 'Pipeline', + message: 'Message', + sessionId: 'Session ID', + status: 'Status', + actions: 'Actions', + viewDetails: 'View Details', + copyId: 'Copy ID', + noMessages: 'No messages found', + noMessagesDescription: 'Try adjusting your filters or check back later', + loading: 'Loading messages...', + loadMore: 'Load More', + autoRefresh: 'Auto Refresh', + platform: 'Role', + user: 'User', + level: 'Level', + runner: 'Runner', + viewConversation: 'View Conversation', + }, + llmCalls: { + title: 'LLM Calls', + model: 'Model', + tokens: 'Tokens', + duration: 'Duration', + cost: 'Cost', + noData: 'No LLM calls found', + inputTokens: 'Input Tokens', + outputTokens: 'Output Tokens', + totalTokens: 'Total Tokens', + avgDuration: 'Avg Duration', + calls: 'Calls', + }, + embeddingCalls: { + title: 'Embedding Calls', + model: 'Model', + tokens: 'Tokens', + duration: 'Duration', + noData: 'No embedding calls found', + promptTokens: 'Prompt Tokens', + totalTokens: 'Total Tokens', + inputCount: 'Input Count', + knowledgeBase: 'Knowledge Base', + queryText: 'Query', + }, + modelCalls: { + title: 'Model Calls', + llmModel: 'LLM', + embeddingModel: 'Embedding', + embeddingCall: 'Embedding', + retrieveCall: 'Retrieve', + noData: 'No model calls found', + }, + sessions: { + sessionId: 'Session ID', + messageCount: 'Messages', + duration: 'Duration', + lastActivity: 'Last Activity', + noSessions: 'No sessions found', + startTime: 'Start Time', + messageStats: 'Message Statistics', + totalMessages: 'Total Messages', + successMessages: 'Successful', + errorMessages: 'Failed', + llmStats: 'LLM Statistics', + noData: 'Session not found', + }, + errors: { + title: 'Errors', + errorType: 'Error Type', + errorMessage: 'Error Message', + occurredAt: 'Occurred At', + noErrors: 'No errors found', + stackTrace: 'Stack Trace', + }, + queries: { + title: 'Queries', + }, + messageDetails: { + noData: 'No LLM calls or errors for this query', + }, + queryVariables: { + title: 'Query Variables', + }, + trafficChart: { + title: 'Traffic Overview', + messages: 'Messages', + llmCalls: 'LLM Calls', + noData: 'No traffic data available', + }, + viewMonitoring: 'View Monitoring', + refreshData: 'Refresh Data', + exportData: 'Export Data', + }, }; export default enUS; diff --git a/web/src/i18n/locales/ja-JP.ts b/web/src/i18n/locales/ja-JP.ts index 8b1f79d6..b7864aea 100644 --- a/web/src/i18n/locales/ja-JP.ts +++ b/web/src/i18n/locales/ja-JP.ts @@ -811,6 +811,123 @@ const jaJP = { spaceEmailMismatch: 'Spaceログインのメールアドレスがローカルアカウントのメールアドレスと一致しません', }, + monitoring: { + title: 'モニタリング', + description: + 'ボットアクティビティ、LLM呼び出し、システムパフォーマンスを監視', + overview: '概要', + totalMessages: '総メッセージ数', + llmCallsCount: 'LLM呼び出し', + modelCallsCount: 'モデル呼び出し', + successRate: '成功率', + activeSessions: 'アクティブセッション', + last24Hours: '過去24時間', + filters: { + title: 'フィルター', + bot: 'ボット', + pipeline: 'パイプライン', + allBots: 'すべてのボット', + selectBot: 'ボットを選択', + allPipelines: 'すべてのパイプライン', + selectPipeline: 'パイプラインを選択', + loading: '読み込み中...', + timeRange: '時間範囲', + customRange: 'カスタム範囲', + from: '開始', + to: '終了', + apply: '適用', + reset: 'フィルターをリセット', + lastHour: '過去1時間', + last6Hours: '過去6時間', + last24Hours: '過去24時間', + last7Days: '過去7日間', + last30Days: '過去30日間', + }, + tabs: { + messages: 'メッセージ記録', + llmCalls: 'LLM呼び出し', + embeddingCalls: 'Embedding呼び出し', + modelCalls: 'モデル呼び出し', + sessions: 'セッション分析', + errors: 'エラーログ', + }, + messageList: { + timestamp: 'タイムスタンプ', + bot: 'ボット', + pipeline: 'パイプライン', + message: 'メッセージ', + sessionId: 'セッションID', + status: 'ステータス', + actions: 'アクション', + viewDetails: '詳細を表示', + copyId: 'IDをコピー', + noMessages: 'メッセージが見つかりません', + noMessagesDescription: 'フィルターを調整するか、後で確認してください', + loading: 'メッセージを読み込んでいます...', + loadMore: 'もっと読み込む', + autoRefresh: '自動更新', + platform: 'プラットフォーム', + user: 'ユーザー', + level: 'レベル', + runner: 'ランナー', + viewConversation: '会話詳細を表示', + }, + llmCalls: { + model: 'モデル', + tokens: 'トークン数', + duration: '期間', + cost: 'コスト', + noData: 'LLM呼び出し記録が見つかりません', + inputTokens: '入力トークン', + outputTokens: '出力トークン', + totalTokens: '合計トークン数', + }, + embeddingCalls: { + title: 'Embedding呼び出し', + model: 'モデル', + tokens: 'トークン数', + duration: '期間', + noData: 'Embedding呼び出し記録が見つかりません', + promptTokens: '入力トークン', + totalTokens: '合計トークン数', + inputCount: '入力数', + knowledgeBase: 'ナレッジベース', + queryText: 'クエリ', + }, + modelCalls: { + title: 'モデル呼び出し', + llmModel: '対話モデル', + embeddingModel: '埋め込みモデル', + embeddingCall: '埋め込み呼び出し', + retrieveCall: '検索呼び出し', + noData: 'モデル呼び出し記録が見つかりません', + }, + sessions: { + sessionId: 'セッションID', + messageCount: 'メッセージ数', + duration: '期間', + lastActivity: '最終アクティビティ', + noSessions: 'セッションが見つかりません', + startTime: '開始時刻', + }, + errors: { + errorType: 'エラータイプ', + errorMessage: 'エラーメッセージ', + occurredAt: '発生時刻', + noErrors: 'エラーが見つかりません', + stackTrace: 'スタックトレース', + title: 'エラー', + }, + messageDetails: { + noData: 'このクエリにはLLM呼び出しやエラーがありません', + }, + queryVariables: { + title: 'クエリ変数', + }, + viewMonitoring: 'モニタリングを表示', + refreshData: 'データを更新', + exportData: 'データをエクスポート', + }, }; export default jaJP; diff --git a/web/src/i18n/locales/zh-Hans.ts b/web/src/i18n/locales/zh-Hans.ts index 0544e869..19c6ec5f 100644 --- a/web/src/i18n/locales/zh-Hans.ts +++ b/web/src/i18n/locales/zh-Hans.ts @@ -265,6 +265,10 @@ const zhHans = { allLevels: '全部级别', selectLevel: '选择级别', levelsSelected: '个级别已选', + viewDetailedLogs: '查看详细日志', + viewDetails: '详情', + collapse: '收起', + imagesAttached: '张图片', }, plugins: { title: '插件扩展', @@ -729,7 +733,7 @@ const zhHans = { }, llm: { llmModels: '对话模型', - description: '管理 LLM 模型，用于对话消息生成', + description: '管理 LLM 模型,用于对话消息生成', extraParametersDescription: '将在请求时附加到请求体中，如 max_tokens, temperature, top_p 等', }, @@ -763,6 +767,140 @@ const zhHans = { setPasswordHint: '设置密码后可使用邮箱密码登录', spaceEmailMismatch: 'Space登录账号邮箱与本实例账号邮箱不匹配', }, + monitoring: { + title: '日志监控', + description: '查看机器人活动、LLM调用和系统性能', + overview: '概览', + totalMessages: '总消息数', + llmCallsCount: 'LLM调用', + modelCallsCount: '模型调用', + successRate: '成功率', + activeSessions: '活跃会话', + last24Hours: '最近24小时', + filters: { + title: '筛选', + bot: '机器人', + pipeline: '流水线', + allBots: '全部机器人', + selectBot: '选择机器人', + allPipelines: '全部流水线', + selectPipeline: '选择流水线', + loading: '加载中...', + timeRange: '时间范围', + customRange: '自定义范围', + from: '从', + to: '到', + apply: '应用', + reset: '重置筛选', + lastHour: '最近1小时', + last6Hours: '最近6小时', + last24Hours: '最近24小时', + last7Days: '最近7天', + last30Days: '最近30天', + }, + tabs: { + messages: '消息记录', + llmCalls: 'LLM调用', + embeddingCalls: 'Embedding调用', + modelCalls: '模型调用', + sessions: '会话分析', + errors: '错误日志', + }, + messageList: { + timestamp: '时间戳', + bot: '机器人', + pipeline: '流水线', + message: '消息', + sessionId: '会话ID', + status: '状态', + actions: '操作', + viewDetails: '查看详情', + copyId: '复制ID', + noMessages: '未找到消息', + noMessagesDescription: '尝试调整筛选条件或稍后查看', + loading: '加载消息中...', + loadMore: '加载更多', + autoRefresh: '自动刷新', + platform: '角色', + user: '用户', + level: '级别', + runner: '执行器', + viewConversation: '显示对话详情', + }, + llmCalls: { + title: 'LLM调用', + model: '模型', + tokens: 'Token数', + duration: '持续时间', + cost: '成本', + noData: '未找到LLM调用记录', + inputTokens: '输入Token', + outputTokens: '输出Token', + totalTokens: '总Token数', + avgDuration: '平均耗时', + calls: '调用次数', + }, + embeddingCalls: { + title: 'Embedding调用', + model: '模型', + tokens: 'Token数', + duration: '持续时间', + noData: '未找到Embedding调用记录', + promptTokens: '输入Token', + totalTokens: '总Token数', + inputCount: '输入数量', + knowledgeBase: '知识库', + queryText: '查询文本', + }, + modelCalls: { + title: '模型调用', + llmModel: '对话模型', + embeddingModel: '嵌入模型', + embeddingCall: '嵌入调用', + retrieveCall: '检索调用', + noData: '未找到模型调用记录', + }, + sessions: { + sessionId: '会话ID', + messageCount: '消息数', + duration: '持续时间', + lastActivity: '最后活动', + noSessions: '未找到会话', + startTime: '开始时间', + messageStats: '消息统计', + totalMessages: '总消息数', + successMessages: '成功', + errorMessages: '失败', + llmStats: 'LLM统计', + noData: '会话未找到', + }, + errors: { + title: '错误', + errorType: '错误类型', + errorMessage: '错误消息', + occurredAt: '发生时间', + noErrors: '未找到错误', + stackTrace: '堆栈追踪', + }, + queries: { + title: '查询记录', + }, + messageDetails: { + noData: '此查询没有LLM调用或错误记录', + }, + queryVariables: { + title: '查询变量', + }, + trafficChart: { + title: '流量概览', + messages: '消息数', + llmCalls: 'LLM调用', + noData: '暂无流量数据', + }, + viewMonitoring: '查看日志监控', + refreshData: '刷新数据', + exportData: '导出数据', + }, }; export default zhHans; diff --git a/web/src/i18n/locales/zh-Hant.ts b/web/src/i18n/locales/zh-Hant.ts index 3524795d..75f64099 100644 --- a/web/src/i18n/locales/zh-Hant.ts +++ b/web/src/i18n/locales/zh-Hant.ts @@ -760,6 +760,122 @@ const zhHant = { setPasswordHint: '設定密碼後可使用電子郵件密碼登入', spaceEmailMismatch: 'Space登入帳號電子郵件與本實例帳號電子郵件不匹配', }, + monitoring: { + title: '日誌監控', + description: '監控機器人活動、LLM調用和系統效能', + overview: '概覽', + totalMessages: '總訊息數', + llmCallsCount: 'LLM調用', + modelCallsCount: '模型調用', + successRate: '成功率', + activeSessions: '活躍會話', + last24Hours: '最近24小時', + filters: { + title: '篩選', + bot: '機器人', + pipeline: '流水線', + allBots: '全部機器人', + selectBot: '選擇機器人', + allPipelines: '全部流水線', + selectPipeline: '選擇流水線', + loading: '載入中...', + timeRange: '時間範圍', + customRange: '自訂範圍', + from: '從', + to: '到', + apply: '套用', + reset: '重置篩選', + lastHour: '最近1小時', + last6Hours: '最近6小時', + last24Hours: '最近24小時', + last7Days: '最近7天', + last30Days: '最近30天', + }, + tabs: { + messages: '訊息記錄', + llmCalls: 'LLM調用', + embeddingCalls: 'Embedding調用', + modelCalls: '模型調用', + sessions: '會話分析', + errors: '錯誤日誌', + }, + messageList: { + timestamp: '時間戳記', + bot: '機器人', + pipeline: '流水線', + message: '訊息', + sessionId: '會話ID', + status: '狀態', + actions: '操作', + viewDetails: '查看詳情', + copyId: '複製ID', + noMessages: '未找到訊息', + noMessagesDescription: '嘗試調整篩選條件或稍後查看', + loading: '載入訊息中...', + loadMore: '載入更多', + autoRefresh: '自動重新整理', + platform: '平台', + user: '使用者', + level: '級別', + runner: '執行器', + viewConversation: '顯示對話詳情', + }, + llmCalls: { + model: '模型', + tokens: '代幣數', + duration: '持續時間', + cost: '成本', + noData: '未找到LLM調用記錄', + inputTokens: '輸入代幣', + outputTokens: '輸出代幣', + totalTokens: '總代幣數', + }, + embeddingCalls: { + title: 'Embedding調用', + model: '模型', + tokens: '代幣數', + duration: '持續時間', + noData: '未找到Embedding調用記錄', + promptTokens: '輸入代幣', + totalTokens: '總代幣數', + inputCount: '輸入數量', + knowledgeBase: '知識庫', + queryText: '查詢文字', + }, + modelCalls: { + title: '模型調用', + llmModel: '對話模型', + embeddingModel: '嵌入模型', + embeddingCall: '嵌入調用', + retrieveCall: '檢索調用', + noData: '未找到模型調用記錄', + }, + sessions: { + sessionId: '會話ID', + messageCount: '訊息數', + duration: '持續時間', + lastActivity: '最後活動', + noSessions: '未找到會話', + startTime: '開始時間', + }, + errors: { + errorType: '錯誤類型', + errorMessage: '錯誤訊息', + occurredAt: '發生時間', + noErrors: '未找到錯誤', + stackTrace: '堆疊追蹤', + title: '錯誤', + }, + messageDetails: { + noData: '此查詢沒有LLM調用或錯誤記錄', + }, + queryVariables: { + title: '查詢變數', + }, + viewMonitoring: '查看日誌監控', + refreshData: '重新整理資料', + exportData: '匯出資料', + }, }; export default zhHant;