mirror of
https://github.com/hoshikawa2/compass_backoffice.git
synced 2026-07-09 22:04:20 +00:00
721 lines
33 KiB
Python
721 lines
33 KiB
Python
from agent_framework.checkpoints.langgraph_saver import create_langgraph_checkpointer
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from langgraph.graph import END, START, StateGraph
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from agent_framework.guardrails.pipeline import GuardrailPipeline
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from agent_framework.guardrails.output_supervisor import OutputSupervisor
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from agent_framework.guardrails.rail_action import RailAction
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from agent_framework.guardrails.rail_result import RailResult
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from agent_framework.judges.judge import JudgePipeline
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from agent_framework.routing.enterprise_router import EnterpriseRouter
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from agent_framework.supervisor.supervisor import Supervisor
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from agent_framework.observability.workflow_events import WorkflowTelemetry
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from agent_framework.observability.guardrail_events import GuardrailTelemetry
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from agent_framework.observability.judge_events import JudgeTelemetry
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from agent_framework.observability.langgraph_telemetry import LangGraphDeepTelemetry
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from agent_framework.observability.observer import AgentObserver
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from app.agents.billing_agent import BillingAgent
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from app.agents.product_agent import ProductAgent
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from app.agents.orders_agent import OrdersAgent
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from app.agents.support_agent import SupportAgent
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from app.agents.backoffice_agent import BackofficeAgent
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from app.state import AgentState
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from agent_framework.rag.rag_service import RagService
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from agent_framework.rag.embedding_provider import create_embedding_provider
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from agent_framework.cache.cache import create_cache
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class LegacyOutputGuardrailRail:
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"""Adapter: reutiliza GuardrailPipeline.run_output dentro do OutputSupervisor novo.
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O framework antigo retornava decisões allowed=True/False. O OutputSupervisor
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corporativo trabalha com RailAction (allow/sanitize/retry/block/handover).
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Este adapter evita reescrever todos os rails agora e mantém compatibilidade.
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"""
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code = "LEGACY_OUTPUT_GUARDRAILS"
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def __init__(self, pipeline: GuardrailPipeline):
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self.pipeline = pipeline
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async def evaluate(self, candidate: str, context: dict):
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final, decisions = await self.pipeline.run_output(candidate, context)
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serialized = [d.model_dump() for d in decisions]
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blocked = [d for d in decisions if not getattr(d, "allowed", True)]
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if blocked:
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first = blocked[0]
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code = (getattr(first, "code", "") or "").upper()
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action = RailAction.RETRY if code in {"REVPREC", "CMP", "SCO", "GND"} else RailAction.BLOCK
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return RailResult(
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code=code or self.code,
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action=action,
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reason=getattr(first, "reason", "Resposta bloqueada por guardrail de saída"),
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guidance=getattr(first, "reason", "Regerar resposta seguindo as políticas de saída."),
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sanitized_text=final,
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metadata={"legacy_decisions": serialized},
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)
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if final != candidate:
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return RailResult(
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code=self.code,
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action=RailAction.SANITIZE,
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reason="Resposta sanitizada por guardrail de saída legado.",
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sanitized_text=final,
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metadata={"legacy_decisions": serialized},
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)
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return RailResult(
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code=self.code,
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action=RailAction.ALLOW,
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reason="Resposta aprovada pelos guardrails de saída legados.",
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sanitized_text=final,
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metadata={"legacy_decisions": serialized},
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)
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class AgentWorkflow:
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"""Workflow principal com dois modos de roteamento.
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Modos suportados por configuração:
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ROUTING_MODE=router
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input_guardrails -> routing_decision/EnterpriseRouter -> 1 agente -> output_guardrails
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ROUTING_MODE=supervisor
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input_guardrails -> routing_decision/Supervisor -> supervisor_agent -> N agentes -> consolidação
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Em ambos os modos, memória/checkpoint/session usam tenant_id:agent_id:session_id.
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"""
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def __init__(self, llm, memory, telemetry, analytics, settings, observer: AgentObserver | None = None, tool_router=None, summary_memory=None):
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self.llm = llm
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self.memory = memory
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self.telemetry = telemetry
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self.analytics = analytics
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self.observer = observer or AgentObserver(analytics=analytics)
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self.settings = settings
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self.tool_router = tool_router
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self.summary_memory = summary_memory
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self.guardrails = GuardrailPipeline(
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observer=self.observer,
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enable_parallel=bool(getattr(settings, "ENABLE_PARALLEL_GUARDRAILS", True)),
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fail_fast=bool(getattr(settings, "GUARDRAILS_FAIL_FAST", True)),
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)
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self.output_supervisor_engine = OutputSupervisor(
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rails=[LegacyOutputGuardrailRail(self.guardrails)],
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observer=self.observer,
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max_retries=int(getattr(settings, "OUTPUT_SUPERVISOR_MAX_RETRIES", 3)),
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enable_parallel=bool(getattr(settings, "ENABLE_PARALLEL_GUARDRAILS", True)),
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fail_fast=bool(getattr(settings, "GUARDRAILS_FAIL_FAST", True)),
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)
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self.judges = JudgePipeline()
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self.supervisor = Supervisor()
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self.workflow_telemetry = WorkflowTelemetry(telemetry)
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self.guardrail_telemetry = GuardrailTelemetry(telemetry)
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self.judge_telemetry = JudgeTelemetry(telemetry)
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self.langgraph_telemetry = LangGraphDeepTelemetry(telemetry)
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self.cache = create_cache(settings)
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self.embedding_provider = create_embedding_provider(settings)
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self.rag_service = RagService(settings, telemetry=telemetry)
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self.router = EnterpriseRouter(settings, llm=llm, telemetry=telemetry)
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agent_kwargs = {"telemetry": telemetry, "tool_router": getattr(self, "tool_router", None), "rag_service": self.rag_service, "cache": self.cache, "settings": settings, "observer": self.observer}
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self.billing = BillingAgent(llm, **agent_kwargs)
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self.product = ProductAgent(llm, **agent_kwargs)
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self.orders = OrdersAgent(llm, **agent_kwargs)
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self.support = SupportAgent(llm, **agent_kwargs)
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self.backoffice = BackofficeAgent(llm, **agent_kwargs)
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self.graph = self._build_graph()
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def _node(self, name, fn):
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async def _wrapped(state):
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async with self.langgraph_telemetry.node(name, state):
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return await fn(state)
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return _wrapped
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def _build_graph(self):
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builder = StateGraph(AgentState)
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builder.add_node("input_guardrails", self._node("input_guardrails", self.input_guardrails))
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builder.add_node("routing_decision", self._node("routing_decision", self.routing_decision))
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builder.add_node("billing_agent", self._node("billing_agent", self.billing_agent))
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builder.add_node("product_agent", self._node("product_agent", self.product_agent))
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builder.add_node("orders_agent", self._node("orders_agent", self.orders_agent))
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builder.add_node("support_agent", self._node("support_agent", self.support_agent))
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builder.add_node("backoffice_agent", self._node("backoffice_agent", self.backoffice_agent))
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builder.add_node("handoff", self._node("handoff", self.handoff))
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builder.add_node("supervisor_agent", self._node("supervisor_agent", self.supervisor_agent))
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builder.add_node("output_supervisor", self._node("output_supervisor", self.output_supervisor))
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builder.add_node("output_guardrails", self._node("output_guardrails", self.output_guardrails))
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builder.add_node("judge", self._node("judge", self.judge))
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builder.add_node("supervisor_review", self._node("supervisor_review", self.supervisor_review))
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builder.add_node("persist", self._node("persist", self.persist))
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builder.add_edge(START, "input_guardrails")
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builder.add_conditional_edges(
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"input_guardrails",
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self._after_input_guardrails,
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{"blocked": "persist", "continue": "routing_decision"},
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)
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builder.add_conditional_edges(
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"routing_decision",
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lambda s: s.get("route", "billing_agent"),
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{
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"billing_agent": "billing_agent",
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"product_agent": "product_agent",
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"orders_agent": "orders_agent",
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"support_agent": "support_agent",
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"backoffice_agent": "backoffice_agent",
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"handoff": "handoff",
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"supervisor_agent": "supervisor_agent",
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},
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)
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builder.add_edge("billing_agent", "output_supervisor")
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builder.add_edge("product_agent", "output_supervisor")
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builder.add_edge("orders_agent", "output_supervisor")
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builder.add_edge("support_agent", "output_supervisor")
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builder.add_edge("backoffice_agent", "output_supervisor")
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builder.add_edge("handoff", "output_supervisor")
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builder.add_edge("supervisor_agent", "output_supervisor")
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builder.add_edge("output_supervisor", "output_guardrails")
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builder.add_edge("output_guardrails", "judge")
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builder.add_edge("judge", "supervisor_review")
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builder.add_edge("supervisor_review", "persist")
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builder.add_edge("persist", END)
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return builder.compile(checkpointer=create_langgraph_checkpointer(self.settings))
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def _after_input_guardrails(self, state):
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return "blocked" if state.get("blocked") else "continue"
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async def input_guardrails(self, state):
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async with self.telemetry.span(
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"workflow.input_guardrails",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input=state.get("user_text"),
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):
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history_texts = [m.get("content", "") for m in state.get("history", [])]
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await self.observer.emit_grl(
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"001",
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{
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"session_id": state.get("conversation_key") or state.get("session_id"),
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"phase": "input",
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},
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component="workflow.input_guardrails.start",
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)
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sanitized, decisions = await self.guardrails.run_input(
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state["user_text"],
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{
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**(state.get("context") or {}),
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"history_texts": history_texts,
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"agent_profile": state.get("agent_profile") or {},
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},
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)
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for _decision in decisions:
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await self.guardrail_telemetry.evaluated("input", _decision)
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await self.observer.emit_grl(
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"002" if _decision.allowed else "004",
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{
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"session_id": state.get("conversation_key") or state.get("session_id"),
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"phase": "input",
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"rail_code": getattr(_decision, "code", None),
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"allowed": bool(_decision.allowed),
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"reason": getattr(_decision, "reason", None),
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},
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component="workflow.input_guardrails.decision",
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)
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if not _decision.allowed:
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await self.guardrail_telemetry.blocked("input", _decision)
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await self.telemetry.event(
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"guardrails.input.completed",
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{
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"session_id": state.get("conversation_key") or state.get("session_id"),
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"decisions": [d.model_dump() for d in decisions],
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},
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)
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await self.observer.emit_grl(
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"009",
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{
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"session_id": state.get("conversation_key") or state.get("session_id"),
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"phase": "input",
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"blocked": any(not d.allowed for d in decisions),
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"decision_count": len(decisions),
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},
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component="workflow.input_guardrails.final",
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)
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if any(not d.allowed for d in decisions):
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return {
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"sanitized_input": sanitized,
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"answer": "Não consegui seguir com essa mensagem por regra de segurança.",
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"final_answer": "Não consegui seguir com essa mensagem por regra de segurança.",
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"guardrail_decisions": [d.model_dump() for d in decisions],
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"route": "blocked",
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"blocked": True,
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}
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return {
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"sanitized_input": sanitized,
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"guardrail_decisions": [d.model_dump() for d in decisions],
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"blocked": False,
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}
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async def routing_decision(self, state):
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mode = getattr(self.settings, "ROUTING_MODE", "router")
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async with self.telemetry.span(
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"workflow.routing_decision",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={
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"mode": mode,
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"text": state.get("sanitized_input") or state.get("user_text"),
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"previous_state": state.get("next_state"),
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},
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):
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if mode == "supervisor":
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plan = await self.supervisor.route_plan(state)
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await self.langgraph_telemetry.edge("routing_decision", "supervisor_agent", state, {"method": "supervisor", "intent": plan.intent, "confidence": plan.confidence})
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return {
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"route": "supervisor_agent",
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"intent": plan.intent,
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"supervisor_plan": {
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"agents": plan.agents,
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"intent": plan.intent,
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"confidence": plan.confidence,
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"reason": plan.reason,
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"metadata": plan.metadata,
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},
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"route_decision": {
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"route": "supervisor_agent",
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"agent": "supervisor",
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"intent": plan.intent,
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"confidence": plan.confidence,
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"reason": plan.reason,
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"method": "supervisor",
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"metadata": plan.metadata,
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},
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}
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decision = await self.router.route(state)
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await self.langgraph_telemetry.edge("routing_decision", decision.route, state, {"method": getattr(decision, "method", None), "intent": decision.intent, "confidence": decision.confidence})
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await self.observer.emit_ic(
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"ROUTE_SELECTED",
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{
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"session_id": state.get("conversation_key") or state.get("session_id"),
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"tenant_id": state.get("tenant_id"),
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"agent_id": state.get("agent_id"),
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"route": decision.route,
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"intent": decision.intent,
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"confidence": decision.confidence,
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"method": getattr(decision, "method", None),
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},
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component="workflow.routing_decision",
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)
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return {
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"route": decision.route,
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"intent": decision.intent,
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"route_decision": decision.model_dump(mode="json"),
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"domain": decision.domain,
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"mcp_tools": decision.mcp_tools,
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"next_state": decision.next_state,
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}
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async def billing_agent(self, state):
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async with self.langgraph_telemetry.node("billing_agent", state):
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async with self.telemetry.span(
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"workflow.agent.billing",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"intent": state.get("intent")},
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):
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return await self.billing.run(state)
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async def product_agent(self, state):
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async with self.langgraph_telemetry.node("product_agent", state):
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async with self.telemetry.span(
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"workflow.agent.product",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"intent": state.get("intent")},
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):
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return await self.product.run(state)
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async def orders_agent(self, state):
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async with self.langgraph_telemetry.node("orders_agent", state):
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async with self.telemetry.span(
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"workflow.agent.orders",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"intent": state.get("intent")},
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):
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return await self.orders.run(state)
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async def support_agent(self, state):
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async with self.langgraph_telemetry.node("support_agent", state):
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async with self.telemetry.span(
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"workflow.agent.support",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"intent": state.get("intent")},
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):
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return await self.support.run(state)
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async def backoffice_agent(self, state):
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async with self.langgraph_telemetry.node("backoffice_agent", state):
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async with self.telemetry.span(
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"workflow.agent.backoffice",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"intent": state.get("intent")},
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):
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return await self.backoffice.run(state)
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async def supervisor_agent(self, state):
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"""Executa um ou mais agentes no modo supervisor e consolida a resposta.
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Este nó mantém o desenho de supervisor sem obrigar o restante do workflow
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a conhecer quantos agentes foram acionados. Cada execução especializada
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recebe o mesmo estado, mas com route/active_agent atualizados.
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"""
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plan = state.get("supervisor_plan") or {}
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agents = plan.get("agents") or ["backoffice_agent"]
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handlers = {
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"billing_agent": self.billing.run,
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"product_agent": self.product.run,
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"orders_agent": self.orders.run,
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"support_agent": self.support.run,
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"backoffice_agent": self.backoffice.run,
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}
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partials = []
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mcp_results = []
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async with self.telemetry.span(
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"workflow.supervisor_agent",
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session_id=state.get("conversation_key") or state.get("session_id"),
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input={"agents": agents, "intent": state.get("intent")},
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):
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for agent_name in agents:
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handler = handlers.get(agent_name)
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if handler is None:
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continue
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child_state = {**state, "route": agent_name, "active_agent": agent_name}
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result = await handler(child_state)
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partials.append({"agent": agent_name, "answer": result.get("answer", "")})
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mcp_results.extend(result.get("mcp_results") or [])
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if len(partials) == 1:
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answer = partials[0]["answer"]
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else:
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joined = "\n\n".join(f"{p['agent']}: {p['answer']}" for p in partials)
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answer = (
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"[Supervisor] Consolidação de múltiplos agentes acionados.\n"
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f"{joined}"
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)
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return {
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"answer": answer,
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"supervisor_results": partials,
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"mcp_results": mcp_results,
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"next_state": "SUPERVISOR_ACTIVE",
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}
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async def handoff(self, state):
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async with self.telemetry.span("workflow.handoff", session_id=state.get("session_id")):
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target = (state.get("route_decision") or {}).get("metadata", {}).get("target_agent")
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answer = (
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"Vou redirecionar sua solicitação para o especialista correto. "
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f"Destino sugerido: {target or 'agente especializado'}."
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)
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return {"answer": answer}
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async def output_supervisor(self, state):
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"""Valida a resposta candidata com o OutputSupervisor corporativo.
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Este nó não substitui o roteador/supervisor multiagente. Ele roda após o
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agente gerar `answer` e antes dos judges/persistência, produzindo campos
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|
supervisor_* no state e eventos GRL.001..GRL.009 via AgentObserver.
|
|
"""
|
|
if not bool(getattr(self.settings, "ENABLE_OUTPUT_SUPERVISOR", True)):
|
|
return {
|
|
"output_guardrails_already_applied": False,
|
|
"supervisor_action": "disabled",
|
|
"supervisor_attempt": int(state.get("supervisor_attempt", 0)),
|
|
}
|
|
|
|
candidate = state.get("answer") or ""
|
|
context = {
|
|
**(state.get("context") or {}),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
"supervisor_attempt": int(state.get("supervisor_attempt", 0)),
|
|
}
|
|
async with self.telemetry.span(
|
|
"workflow.output_supervisor",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
input=candidate,
|
|
):
|
|
decision = await self.output_supervisor_engine.evaluate(candidate, context)
|
|
action = decision.action.value
|
|
await self.telemetry.event(
|
|
"output_supervisor.completed",
|
|
{
|
|
"session_id": context["session_id"],
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"action": action,
|
|
"approved": decision.approved,
|
|
"guidance": decision.guidance,
|
|
},
|
|
)
|
|
|
|
await self.observer.emit_ic(
|
|
"IC.OUTPUT_SUPERVISOR_COMPLETED",
|
|
{
|
|
"session_id": context["session_id"],
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
"action": action,
|
|
"approved": decision.approved,
|
|
"result_count": len(decision.results),
|
|
},
|
|
component="workflow.output_supervisor",
|
|
)
|
|
|
|
if decision.action in {RailAction.ALLOW, RailAction.SANITIZE, RailAction.OBSERVE}:
|
|
final_answer = decision.candidate
|
|
elif decision.action == RailAction.HANDOVER:
|
|
final_answer = "Vou encaminhar seu atendimento para continuidade com um especialista."
|
|
else:
|
|
final_answer = decision.fallback_message
|
|
|
|
return {
|
|
"answer": final_answer,
|
|
"final_answer": final_answer,
|
|
"supervisor_action": action,
|
|
"supervisor_guidance": decision.guidance,
|
|
"supervisor_attempt": int(state.get("supervisor_attempt", 0)) + (1 if decision.action == RailAction.RETRY else 0),
|
|
"supervisor_handover_reason": decision.handover_reason,
|
|
"output_supervisor_results": [
|
|
{
|
|
"code": r.code,
|
|
"action": r.action.value,
|
|
"reason": r.reason,
|
|
"guidance": r.guidance,
|
|
"metadata": r.metadata,
|
|
}
|
|
for r in decision.results
|
|
],
|
|
"output_guardrails_already_applied": True,
|
|
"guardrail_decisions": state.get("guardrail_decisions", [])
|
|
+ [item for r in decision.results for item in (r.metadata or {}).get("legacy_decisions", [])],
|
|
}
|
|
|
|
async def output_guardrails(self, state):
|
|
if state.get("output_guardrails_already_applied"):
|
|
return {"final_answer": state.get("final_answer") or state.get("answer") or ""}
|
|
|
|
async with self.telemetry.span(
|
|
"workflow.output_guardrails",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
input=state.get("answer"),
|
|
):
|
|
await self.observer.emit_grl(
|
|
"001",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"phase": "output",
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
},
|
|
component="workflow.output_guardrails.start",
|
|
)
|
|
final, decisions = await self.guardrails.run_output(
|
|
state["answer"], state.get("context", {})
|
|
)
|
|
for _decision in decisions:
|
|
await self.guardrail_telemetry.evaluated("output", _decision)
|
|
await self.observer.emit_grl(
|
|
"002" if _decision.allowed else "004",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"phase": "output",
|
|
"rail_code": getattr(_decision, "code", None),
|
|
"allowed": bool(_decision.allowed),
|
|
"reason": getattr(_decision, "reason", None),
|
|
},
|
|
component="workflow.output_guardrails.decision",
|
|
)
|
|
if not _decision.allowed:
|
|
await self.guardrail_telemetry.blocked("output", _decision)
|
|
await self.telemetry.event(
|
|
"guardrails.output.completed",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"decisions": [d.model_dump() for d in decisions],
|
|
},
|
|
)
|
|
await self.observer.emit_grl(
|
|
"009",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"phase": "output",
|
|
"blocked": any(not d.allowed for d in decisions),
|
|
"decision_count": len(decisions),
|
|
},
|
|
component="workflow.output_guardrails.final",
|
|
)
|
|
return {
|
|
"final_answer": final,
|
|
"guardrail_decisions": state.get("guardrail_decisions", [])
|
|
+ [d.model_dump() for d in decisions],
|
|
}
|
|
|
|
async def judge(self, state):
|
|
async with self.telemetry.span(
|
|
"workflow.judge",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
input={"question": state.get("user_text"), "answer": state.get("final_answer")},
|
|
):
|
|
results = await self.judges.evaluate_all(
|
|
state["user_text"], state["final_answer"], state.get("context", {})
|
|
)
|
|
for _result in results:
|
|
await self.judge_telemetry.evaluated(_result)
|
|
await self.telemetry.event(
|
|
"judges.completed",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"results": [r.model_dump() for r in results],
|
|
},
|
|
)
|
|
return {"judge_results": [r.model_dump() for r in results]}
|
|
|
|
async def supervisor_review(self, state):
|
|
async with self.telemetry.span(
|
|
"workflow.supervisor_review",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
input=state.get("final_answer"),
|
|
):
|
|
ok, answer = await self.supervisor.review(
|
|
state["final_answer"], state.get("context", {})
|
|
)
|
|
await self.telemetry.event(
|
|
"supervisor.review.completed",
|
|
{"session_id": state.get("session_id"), "approved": ok},
|
|
)
|
|
return {"final_answer": answer if ok else answer}
|
|
|
|
async def persist(self, state):
|
|
async with self.telemetry.span(
|
|
"workflow.persist",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
input={"route": state.get("route"), "intent": state.get("intent")},
|
|
):
|
|
await self.observer.emit_ic(
|
|
"AGENT_COMPLETED",
|
|
{
|
|
"session_id": state.get("conversation_key") or state["session_id"],
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
"route_decision": state.get("route_decision"),
|
|
"judges": state.get("judge_results", []),
|
|
"mcp_tools": state.get("mcp_tools", []),
|
|
"mcp_results": state.get("mcp_results", []),
|
|
},
|
|
)
|
|
|
|
await self.observer.emit_noc(
|
|
"006",
|
|
{
|
|
"session_id": state.get("conversation_key") or state["session_id"],
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
"answer_chars": len(state.get("final_answer") or ""),
|
|
},
|
|
component="workflow.persist",
|
|
)
|
|
|
|
await self.telemetry.event(
|
|
"agent.completed",
|
|
{
|
|
"session_id": state.get("conversation_key") or state["session_id"],
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"route": state.get("route"),
|
|
"intent": state.get("intent"),
|
|
"answer_chars": len(state.get("final_answer") or ""),
|
|
},
|
|
)
|
|
return state
|
|
|
|
async def ainvoke(self, state):
|
|
thread_id = state.get("conversation_key") or state["session_id"]
|
|
config = {"configurable": {"thread_id": thread_id}}
|
|
async with self.telemetry.span(
|
|
"workflow.langgraph.ainvoke",
|
|
session_id=state.get("conversation_key") or state.get("session_id"),
|
|
user_id=state.get("context", {}).get("user_id"),
|
|
input={"user_text": state.get("user_text")},
|
|
tags=["langgraph", "agent-workflow", f"routing-mode:{getattr(self.settings, 'ROUTING_MODE', 'router')}",],
|
|
):
|
|
await self.workflow_telemetry.started("agent_workflow", state)
|
|
await self.observer.emit_noc(
|
|
"001",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"channel_id": (state.get("context") or {}).get("channel"),
|
|
"message_id": (state.get("context") or {}).get("message_id"),
|
|
"ura_call_id": (state.get("context") or {}).get("ura_call_id"),
|
|
},
|
|
component="workflow.ainvoke",
|
|
)
|
|
await self.observer.emit_ic(
|
|
"AGENT_STARTED",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"channel_id": (state.get("context") or {}).get("channel"),
|
|
"message_id": (state.get("context") or {}).get("message_id"),
|
|
"user_text_chars": len(state.get("user_text") or ""),
|
|
},
|
|
component="workflow.ainvoke",
|
|
)
|
|
try:
|
|
result = await self.graph.ainvoke(state, config=config)
|
|
await self.workflow_telemetry.completed("agent_workflow", result)
|
|
return result
|
|
except Exception as exc:
|
|
await self.workflow_telemetry.failed("agent_workflow", exc)
|
|
await self.observer.emit_noc(
|
|
"005",
|
|
{
|
|
"session_id": state.get("conversation_key") or state.get("session_id"),
|
|
"tenant_id": state.get("tenant_id"),
|
|
"agent_id": state.get("agent_id"),
|
|
"error": str(exc),
|
|
"exception_type": exc.__class__.__name__,
|
|
},
|
|
component="workflow.ainvoke",
|
|
)
|
|
raise
|