from __future__ import annotations from dataclasses import dataclass from datetime import datetime from decimal import Decimal from typing import Any import sqlalchemy as sa from sqlalchemy import func, or_, select from core.app.entities.app_invoke_entities import InvokeFrom from libs.helper import convert_datetime_to_date, escape_like_pattern, to_timestamp from models.enums import MessageStatus from models.model import App, Conversation, Message @dataclass(frozen=True) class AgentLogQueryParams: page: int = 1 limit: int = 20 keyword: str | None = None status: str | None = None source: str | None = None start: datetime | None = None end: datetime | None = None @dataclass(frozen=True) class AgentStatisticsQueryParams: source: str | None = None start: datetime | None = None end: datetime | None = None timezone: str = "UTC" class AgentObservabilityService: _SOURCE_ALIASES: dict[str, InvokeFrom] = { "api": InvokeFrom.SERVICE_API, "service-api": InvokeFrom.SERVICE_API, "service_api": InvokeFrom.SERVICE_API, "console": InvokeFrom.EXPLORE, "explore": InvokeFrom.EXPLORE, "explore-app": InvokeFrom.EXPLORE, "explore_app": InvokeFrom.EXPLORE, "web": InvokeFrom.WEB_APP, "web-app": InvokeFrom.WEB_APP, "web_app": InvokeFrom.WEB_APP, "debugger": InvokeFrom.DEBUGGER, "dev": InvokeFrom.DEBUGGER, "openapi": InvokeFrom.OPENAPI, "trigger": InvokeFrom.TRIGGER, } def __init__(self, session: Any): self._session = session @classmethod def resolve_source(cls, source: str | None) -> InvokeFrom | None: if not source or source == "all": return None normalized = source.strip().lower() if not normalized or normalized == "all": return None try: return cls._SOURCE_ALIASES[normalized] except KeyError as exc: raise ValueError(f"Unsupported source: {source}") from exc @staticmethod def _message_status(message: Message) -> str: if message.error or message.status == MessageStatus.ERROR: return "failed" if message.status == MessageStatus.PAUSED: return "paused" return "success" @staticmethod def _total_tokens(message: Message) -> int: return int(message.message_tokens or 0) + int(message.answer_tokens or 0) @classmethod def serialize_log_message(cls, message: Message, conversation: Conversation | None = None) -> dict[str, Any]: invoke_from = message.invoke_from.value if message.invoke_from else None return { "id": message.id, "message_id": message.id, "conversation_id": message.conversation_id, "conversation_name": conversation.name if conversation else None, "query": message.query, "answer": message.answer, "status": cls._message_status(message), "error": message.error, "source": invoke_from, "from_source": message.from_source.value if message.from_source else None, "from_end_user_id": message.from_end_user_id, "from_account_id": message.from_account_id, "message_tokens": int(message.message_tokens or 0), "answer_tokens": int(message.answer_tokens or 0), "total_tokens": cls._total_tokens(message), "total_price": str(message.total_price or Decimal(0)), "currency": message.currency, "latency": float(message.provider_response_latency or 0), "created_at": to_timestamp(message.created_at), "updated_at": to_timestamp(message.updated_at), } def list_logs(self, *, app: App, params: AgentLogQueryParams) -> dict[str, Any]: source = self.resolve_source(params.source) stmt = ( select(Message, Conversation) .join(Conversation, Conversation.id == Message.conversation_id) .where(Message.app_id == app.id, Conversation.app_id == app.id) ) stmt = self._apply_source_filter(stmt, source) if params.start: stmt = stmt.where(Message.created_at >= params.start) if params.end: stmt = stmt.where(Message.created_at < params.end) if params.keyword: escaped_keyword = escape_like_pattern(params.keyword) pattern = f"%{escaped_keyword}%" stmt = stmt.where( or_( Message.query.ilike(pattern, escape="\\"), Message.answer.ilike(pattern, escape="\\"), Conversation.name.ilike(pattern, escape="\\"), ) ) if params.status: stmt = self._apply_status_filter(stmt, params.status) total = self._session.scalar(select(func.count()).select_from(stmt.subquery())) or 0 rows = list( self._session.execute( stmt.order_by(Message.created_at.desc(), Message.id.desc()) .offset((params.page - 1) * params.limit) .limit(params.limit) ).all() ) data = [] for message, conversation in rows: data.append(self.serialize_log_message(message, conversation)) return { "data": data, "page": params.page, "limit": params.limit, "total": total, "has_more": params.page * params.limit < total, } @classmethod def _apply_source_filter(cls, stmt, source: InvokeFrom | None): if source is None: return stmt.where(Message.invoke_from != InvokeFrom.DEBUGGER) return stmt.where(Message.invoke_from == source) @staticmethod def _apply_status_filter(stmt, status: str): normalized = status.strip().lower() if normalized in {"success", "normal"}: return stmt.where(Message.error.is_(None), Message.status == MessageStatus.NORMAL) if normalized in {"failed", "error"}: return stmt.where(or_(Message.error.is_not(None), Message.status == MessageStatus.ERROR)) if normalized == "paused": return stmt.where(Message.status == MessageStatus.PAUSED) raise ValueError(f"Unsupported status: {status}") def get_statistics_summary(self, *, app: App, params: AgentStatisticsQueryParams) -> dict[str, Any]: source = self.resolve_source(params.source) rows = self._load_daily_statistics(app=app, params=params, source=source) charts = self._build_charts(rows) summary = self._build_summary(rows) return { "source": source.value if source else "all", "summary": summary, "charts": charts, } def _load_daily_statistics( self, *, app: App, params: AgentStatisticsQueryParams, source: InvokeFrom | None ) -> list[dict[str, Any]]: converted_created_at = convert_datetime_to_date("m.created_at") source_condition = "AND m.invoke_from != :debugger" if source is None else "AND m.invoke_from = :source" sql_query = f"""SELECT {converted_created_at} AS date, COUNT(m.id) AS message_count, COUNT(DISTINCT m.conversation_id) AS conversation_count, COUNT(DISTINCT m.from_end_user_id) AS end_user_count, COALESCE(SUM(COALESCE(m.message_tokens, 0) + COALESCE(m.answer_tokens, 0)), 0) AS token_count, COALESCE(SUM(COALESCE(m.total_price, 0)), 0) AS total_price, COALESCE(AVG(m.provider_response_latency), 0) AS avg_latency, COALESCE(SUM(m.provider_response_latency), 0) AS latency_sum, COALESCE(SUM(m.answer_tokens), 0) AS answer_tokens, COUNT(mf.id) AS like_count FROM messages m LEFT JOIN message_feedbacks mf ON mf.message_id = m.id AND mf.rating = 'like' WHERE m.app_id = :app_id {source_condition}""" args: dict[str, Any] = { "tz": params.timezone, "app_id": app.id, "debugger": InvokeFrom.DEBUGGER, } if source is not None: args["source"] = source if params.start: sql_query += " AND m.created_at >= :start" args["start"] = params.start if params.end: sql_query += " AND m.created_at < :end" args["end"] = params.end sql_query += " GROUP BY date ORDER BY date" return [dict(row._mapping) for row in self._session.execute(sa.text(sql_query), args).all()] @staticmethod def _build_charts(rows: list[dict[str, Any]]) -> dict[str, list[dict[str, Any]]]: messages = [] conversations = [] end_users = [] token_usage = [] average_session_interactions = [] average_response_time = [] tokens_per_second = [] user_satisfaction_rate = [] for row in rows: date = str(row["date"]) message_count = int(row["message_count"] or 0) conversation_count = int(row["conversation_count"] or 0) token_count = int(row["token_count"] or 0) total_price = row["total_price"] or Decimal(0) avg_latency = float(row["avg_latency"] or 0) latency_sum = float(row["latency_sum"] or 0) answer_tokens = int(row["answer_tokens"] or 0) like_count = int(row["like_count"] or 0) messages.append({"date": date, "message_count": message_count}) conversations.append({"date": date, "conversation_count": conversation_count}) end_users.append({"date": date, "terminal_count": int(row["end_user_count"] or 0)}) token_usage.append( { "date": date, "token_count": token_count, "total_price": str(total_price), "currency": "USD", } ) average_session_interactions.append( { "date": date, "interactions": round(message_count / conversation_count, 2) if conversation_count else 0, } ) average_response_time.append({"date": date, "latency": round(avg_latency * 1000, 4)}) tokens_per_second.append({"date": date, "tps": round(answer_tokens / latency_sum, 4) if latency_sum else 0}) user_satisfaction_rate.append( {"date": date, "rate": round(like_count * 100 / message_count, 2) if message_count else 0} ) return { "daily_messages": messages, "daily_conversations": conversations, "daily_end_users": end_users, "token_usage": token_usage, "average_session_interactions": average_session_interactions, "average_response_time": average_response_time, "tokens_per_second": tokens_per_second, "user_satisfaction_rate": user_satisfaction_rate, } @staticmethod def _build_summary(rows: list[dict[str, Any]]) -> dict[str, Any]: total_messages = sum(int(row["message_count"] or 0) for row in rows) total_conversations = sum(int(row["conversation_count"] or 0) for row in rows) total_end_users = sum(int(row["end_user_count"] or 0) for row in rows) total_tokens = sum(int(row["token_count"] or 0) for row in rows) total_price = sum(Decimal(str(row["total_price"] or 0)) for row in rows) total_answer_tokens = sum(int(row["answer_tokens"] or 0) for row in rows) total_latency = sum(float(row["latency_sum"] or 0) for row in rows) weighted_latency = sum(float(row["avg_latency"] or 0) * int(row["message_count"] or 0) for row in rows) total_likes = sum(int(row["like_count"] or 0) for row in rows) return { "total_messages": total_messages, "total_conversations": total_conversations, "total_end_users": total_end_users, "total_tokens": total_tokens, "total_price": str(total_price), "currency": "USD", "average_session_interactions": round(total_messages / total_conversations, 2) if total_conversations else 0, "average_response_time": round((weighted_latency / total_messages) * 1000, 4) if total_messages else 0, "tokens_per_second": round(total_answer_tokens / total_latency, 4) if total_latency else 0, "user_satisfaction_rate": round(total_likes * 100 / total_messages, 2) if total_messages else 0, }