133 lines
4.7 KiB
Python
133 lines
4.7 KiB
Python
from app.agents.prompting import apply_agent_profile_prompt
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from app.agents.contas_prompting import load_domain_prompt
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from app.agents.runtime import AgentRuntimeMixin
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class FaturasAgent(AgentRuntimeMixin):
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name = "faturas_agent"
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def __init__(
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self,
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llm,
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telemetry=None,
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tool_router=None,
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rag_service=None,
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cache=None,
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settings=None,
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observer=None,
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memory=None,
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summary_memory=None,
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guardrail_pipeline=None,
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):
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self.llm = llm
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self.telemetry = telemetry
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self.tool_router = tool_router
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self.rag_service = rag_service
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self.cache = cache
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self.settings = settings
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self.observer = observer
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self.memory = memory
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self.summary_memory = summary_memory
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self.guardrail_pipeline = guardrail_pipeline
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async def run(self, state):
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await self._emit_ic(
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"IC.FATURAS_AGENT_STARTED",
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state,
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{"business_component": "faturas"},
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component="agent.faturas.start",
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)
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tool_context = await self._collect_tool_context(state)
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if tool_context:
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await self._emit_ic(
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"IC.FATURAS_MCP_CONTEXT_COLLECTED",
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state,
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{"tool_result_count": len(tool_context)},
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component="agent.faturas.mcp",
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)
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state["mcp_results"] = tool_context
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clarification_message = self.transaction_clarification_message(state)
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if clarification_message:
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return {
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"answer": f"[{self.__class__.__name__}] {clarification_message}",
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"next_state": state.get("next_state") or "COLLECTING_PARAMETERS",
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"mcp_results": tool_context,
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**self.transaction_state_patch(state),
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}
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confirmation_message = self.transaction_confirmation_message(state)
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if confirmation_message:
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result = {
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"answer": f"[{self.__class__.__name__}] {confirmation_message}",
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"next_state": state.get("next_state"),
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"mcp_results": tool_context,
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**self.transaction_state_patch(state),
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}
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return result
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direct_answer = self.build_direct_mcp_answer(state, tool_context, agent_label="FaturasAgent")
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if direct_answer:
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return {
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"answer": direct_answer,
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"next_state": state.get("next_state") or "ACTIVE",
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"mcp_results": tool_context,
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"rag": {"enabled": False, "skipped": True, "reason": "direct_mcp_answer"},
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**self.transaction_state_patch(state),
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}
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rag_context, rag_metadata = await self._retrieve_rag_context(state)
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if rag_metadata.get("enabled"):
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await self._emit_ic(
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"IC.FATURAS_RAG_CONTEXT_RETRIEVED",
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state,
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{
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"document_count": rag_metadata.get("document_count"),
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"graph_neighbors": rag_metadata.get("graph_neighbors"),
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"latency_ms": rag_metadata.get("latency_ms"),
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},
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component="agent.faturas.rag",
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)
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# Prepara ConversationSummaryMemory antes de montar o prompt.
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# O build_messages() do framework injeta resumo + últimas mensagens quando habilitado.
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await self.prepare_memory_context(state)
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messages = self.build_messages(
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state,
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system_prompt=apply_agent_profile_prompt(
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state,
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load_domain_prompt("billing"),
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),
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mcp_results=tool_context,
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rag_context=rag_context,
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rag_metadata=rag_metadata,
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)
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answer = await self._invoke_llm_cached(state, "FaturasAgent", messages)
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result = {
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"answer": f"[FaturasAgent] {answer}",
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"next_state": "FATURAS_ACTIVE",
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"mcp_results": tool_context,
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"rag": rag_metadata,
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"memory_context_metadata": state.get("memory_context_metadata"),
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**self.transaction_state_patch(state),
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}
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await self._emit_ic(
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"IC.FATURAS_AGENT_COMPLETED",
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state,
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{
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"answer_chars": len(result.get("answer") or ""),
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"has_mcp_results": bool(tool_context),
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"rag_enabled": bool(rag_metadata.get("enabled")),
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"memory_context": state.get("memory_context_metadata"),
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},
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component="agent.faturas.completed",
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)
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return result
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async def _collect_tool_context(self, state):
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return await self._collect_mcp_context(state)
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