mirror of
https://github.com/hoshikawa2/compass_backoffice.git
synced 2026-07-09 22:04:20 +00:00
bugfixes
This commit is contained in:
@@ -1,11 +1,10 @@
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import time
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import logging
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import json
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import re
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from src.agent.state.agent_state import AgentState, update_state_metadata
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from src.agent.state.steps import GraphStep
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from src.agent.state.step_helpers import set_current_step
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from src.components.clients.speech_analytics_client import SpeechAnalyticsClient
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from src.components.clients.exceptions.speech_exceptions import SpeechClientError
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from src.utils.observer import trace_node, score_current_trace
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from src.utils.ics_collector import build_ic_payload, build_noc_api_metadata, build_noc_llm_metadata
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from src.compat.framework_observer import event
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@@ -13,6 +12,7 @@ from src.providers.llm_provider import classification_llm, chat_llm_with_usage,
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from src.core.config import settings
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from src.core.prompt_manager import get_prompt
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from src.agent.local_prompts.speech_history_analysis import speech_history_similarity_pt
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from src.compat.framework_services import call_mcp_tool
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logger = logging.getLogger(__name__)
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@@ -42,9 +42,7 @@ _NULL_SPEECH_DATA = {
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def _map_prediction_response(response: dict) -> dict:
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"""
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Map the raw Prediction API response to the internal speech_analytics schema.
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"""
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"""Map the raw Prediction API response to the internal speech_analytics schema."""
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variables = response.get("variables", {})
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return {
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"reclamacao_resumo": response.get("resume") or variables.get("resume"),
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@@ -57,18 +55,8 @@ def _map_prediction_response(response: dict) -> dict:
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}
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def _build_related_history(
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raw_history: list,
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current_complaint_id: str | None,
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llm_scores: list,
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threshold: int,
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) -> list:
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"""
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Merge LLM similarity scores with raw history items and filter by threshold.
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Each returned item has: protocolo, data_reclamacao, similaridade_pct,
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reasoning, plus the 7 detail keys (same as reclamacao_atual).
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"""
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def _build_related_history(raw_history: list, current_complaint_id: str | None, llm_scores: list, threshold: int) -> list:
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"""Merge LLM similarity scores with raw history items and filter by threshold."""
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if not raw_history or not llm_scores:
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return []
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@@ -105,18 +93,13 @@ def _build_related_history(
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def _build_analise_agente(related_items: list) -> str:
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"""
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Build the analise_agente string from related items: one line per item with
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'Protocolo X (Y%): <reasoning>'. Empty list -> negative spec message.
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"""
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"""Build the analise_agente text from related items."""
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if not related_items:
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return _NO_SIMILAR_MESSAGE
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lines = []
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for item in related_items:
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reasoning = (item.get("reasoning") or "").strip()
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if not reasoning:
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reasoning = "—"
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reasoning = (item.get("reasoning") or "").strip() or "—"
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lines.append(f"Protocolo {item.get('protocolo')} ({item.get('similaridade_pct')}%): {reasoning}")
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return "\n".join(lines)
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@@ -128,20 +111,31 @@ async def _score_history_with_llm(
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session_id: str = "",
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state: dict | None = None,
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) -> list:
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"""
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Calls the LLM to score similarity between the current complaint description
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and each historical item. Returns the LLM-parsed JSON list. Items below
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SPEECH_SIMILARITY_THRESHOLD will be dropped later by _build_related_history.
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"""
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"""Score similarity between current complaint and historical complaints using the LLM."""
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if not current_complaint_description or not history_list:
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event("AGA.015", {
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"status": "Houve acionamento do Agente Especializado (LLM): não — sem resumo atual ou histórico vazio",
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"type": "INFO",
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"session_id": session_id,
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"tag": "AGA.015",
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"call_id": session_id,
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"origin": "LLM",
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**build_ic_payload({"session_id": session_id}, "AGA.015"),
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})
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return []
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clean_history = [
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item for item in history_list
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if str(item.get("protocolo")) != str(current_id or "")
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]
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clean_history = [item for item in history_list if str(item.get("protocolo")) != str(current_id or "")]
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if not clean_history:
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logger.info("No historical items left after deduplication.")
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event("AGA.015", {
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"status": "Houve acionamento do Agente Especializado (LLM): não — histórico vazio após deduplicação",
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"type": "INFO",
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"session_id": session_id,
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"tag": "AGA.015",
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"call_id": session_id,
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"origin": "LLM",
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**build_ic_payload({"session_id": session_id}, "AGA.015"),
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})
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return []
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llm = classification_llm
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@@ -163,6 +157,305 @@ async def _score_history_with_llm(
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history_json=json.dumps(history_payload, ensure_ascii=False),
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)
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logger.info(f"Calling LLM for history similarity filtering. Items: {len(clean_history)}")
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logger.info("Calling LLM for history similarity filtering. Items: %s", len(clean_history))
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event("AGA.014", {
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"status": "Houve acionamento do Agente Especializado (LLM): sim — filtragem de histórico Speech",
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"type": "INFO",
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"session_id": session_id,
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"tag": "AGA.014",
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"call_id": session_id,
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"origin": "LLM",
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**build_ic_payload({"session_id": session_id}, "AGA.014"),
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})
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# LLM generation telemetry is recorded by agent_framework.llm.providers.
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try:
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_t0 = time.perf_counter()
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llm_resp = chat_llm_with_usage(llm, message)
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content = llm_resp.content
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json_match = re.search(r"```(?:json)?\s*(\[.*?\])\s*```", content, re.DOTALL)
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if json_match:
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content = json_match.group(1)
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else:
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json_match = re.search(r"(\[.*?\])", content, re.DOTALL)
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if json_match:
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content = json_match.group(1)
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scores = json.loads(content)
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if not isinstance(scores, list):
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raise ValueError(f"LLM returned non-list: {type(scores).__name__}")
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score_current_trace(name="speech_history_similarity_valid", value=1.0)
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return scores
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except Exception as e:
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logger.error("Error during LLM history scoring: %s", e, exc_info=True)
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score_current_trace(name="speech_history_similarity_valid", value=0.0, comment=str(e))
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if state is not None:
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event("NOC.004", {
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"status": f"LLM error during history similarity analysis: {e}",
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"type": "FAILURE",
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**build_noc_llm_metadata(
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state,
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"NOC.004",
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latency_ms=int((time.perf_counter() - _t0) * 1000) if "_t0" in locals() else 0,
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llm_endpoint=LLM_ENDPOINT,
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model_name=str(getattr(llm, "eligibleModel_name", "unknown")),
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),
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}, metadata={"noc": True})
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return []
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@trace_node
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async def enrich_with_speech(state: AgentState) -> AgentState:
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"""Enrich the ticket with Speech Analytics NLP insights and historical similarity."""
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logger.info("Running speech enrichment node.")
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await set_current_step(state, GraphStep.SPEECH_ENRICHMENT)
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session_id = state.get("session_id", "")
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context = state.get("metadata", {}).get("request_context", {})
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if "speech_analytics" in context:
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logger.info("Speech analytics data already exists in context (cached). Skipping API call.")
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return state
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complaint = context.get("complaint", {})
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reclamacao_id: str | None = complaint.get("complaintProtocol")
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raw_text: str | None = complaint.get("description")
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customer = context.get("customer", {})
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cpf_cnpj: str | None = customer.get("cpfCnpj")
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msisdn: str | None = customer.get("msisdn")
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# Framework-native path: call Speech through MCP/fallback instead of direct legacy clients.
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mcp_response = await call_mcp_tool(
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"consultar_speech_analytics",
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{
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"protocol_id": reclamacao_id,
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"customer_key": cpf_cnpj or msisdn,
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"interaction_key": reclamacao_id,
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"document": cpf_cnpj,
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},
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business_context={"customer_key": cpf_cnpj or msisdn, "interaction_key": reclamacao_id},
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original_context=context,
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)
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mcp_result = mcp_response.get("result") if isinstance(mcp_response.get("result"), dict) else mcp_response
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if mcp_response.get("ok") or str(mcp_result.get("source", "")).startswith("mock_"):
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summary = mcp_result.get("summary") or (raw_text or "Reclamação recebida para análise.")
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speech_data = {
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"reclamacao_resumo": summary,
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"causa_raiz": mcp_result.get("root_cause") or "Cobrança/contato indevido",
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"descortesia_cliente": mcp_result.get("rude_customer") or "False",
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"motivo_reclamacao": mcp_result.get("reason") or complaint.get("motive"),
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"submotivo_reclamacao": mcp_result.get("subreason") or complaint.get("modality"),
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"sentimento_cliente": mcp_result.get("sentiment") or "Negativo",
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"solucao_proposta_cliente": mcp_result.get("proposed_solution") or "Analisar cobrança e cessar contatos indevidos",
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"historico_bruto": mcp_result.get("events") or [],
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"historico_relacionado": mcp_result.get("related_history") or [],
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"analise_agente": mcp_result.get("agent_analysis") or (mcp_result.get("message") or _NO_SIMILAR_MESSAGE),
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"source": mcp_result.get("source", "framework_mcp"),
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"mcp_tool": "consultar_speech_analytics",
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}
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context = dict(context)
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context["speech_analytics"] = speech_data
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state = update_state_metadata(state, request_context=context)
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event("AGA.034", {
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"status": "Resultado Speech: sucesso via MCP/framework",
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"type": "SUCCESS",
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"session_id": session_id,
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"tag": "AGA.034",
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"call_id": session_id,
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"origin": "MCP",
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**build_ic_payload(state, "AGA.034"),
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}, metadata={"noc": True})
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return state
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if not getattr(settings, "BACKOFFICE_ALLOW_LEGACY_CLIENTS", False):
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speech_data = dict(_NULL_SPEECH_DATA)
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speech_data.update({
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"reclamacao_resumo": raw_text or "Indisponível SPEECH para Reclamação Atual - realizar consulta manualmente",
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"historico_bruto": [],
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"historico_relacionado": [],
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"analise_agente": _HISTORY_UNAVAILABLE_MESSAGE,
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"source": "framework_local_fallback",
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})
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context = dict(context)
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context["speech_analytics"] = speech_data
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state = update_state_metadata(state, request_context=context)
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await set_current_step(state, GraphStep.SPEECH_ENRICHMENT_UNAVAILABLE)
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return state
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from src.components.clients.speech_analytics_client import SpeechAnalyticsClient
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from src.components.clients.exceptions.speech_exceptions import SpeechClientError
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speech_data: dict = dict(_NULL_SPEECH_DATA)
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history_unavailable = False
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if not reclamacao_id or not raw_text:
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logger.warning(
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"Speech enrichment skipped prediction: missing complaintProtocol or description. "
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"reclamacao_id=%s, raw_text_present=%s",
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bool(reclamacao_id), bool(raw_text)
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)
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event("AGA.010", {
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"status": "Resultado consulta Speech: ignorada — complaintProtocol ou description ausente",
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"type": "INFO",
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"session_id": session_id,
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"tag": "AGA.010",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.010"),
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}, metadata={"noc": True})
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else:
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try:
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client = SpeechAnalyticsClient()
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response, http_meta = await client.get_prediction(
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reclamacao_id=reclamacao_id,
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raw_text=raw_text,
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customer_segment="",
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)
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speech_data = _map_prediction_response(response)
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if not speech_data.get("reclamacao_resumo"):
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speech_data["reclamacao_resumo"] = "Reclamação Atual não foi encontrada no SPEECH"
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event("NOC.002", {
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"status": "Invalid API response",
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"type": "WARNING",
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**build_noc_api_metadata(
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state,
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"NOC.002",
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retry_count=http_meta.get("retry_count", 0),
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latency_ms=http_meta["latency_ms"],
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api_url=http_meta["url"],
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status_code=http_meta["status_code"],
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),
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}, metadata={"noc": True})
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logger.info("Speech Analytics prediction retrieved successfully. reclamacao_id=%s", reclamacao_id)
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event("AGA.010", {
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"status": "Resultado consulta Speech: sucesso",
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"type": "SUCCESS",
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"session_id": session_id,
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"tag": "AGA.010",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.010", {
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"apiUrl": http_meta["url"],
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"apiStatusCode": http_meta["status_code"],
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"apiResponsePayload": http_meta["response_text"],
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"latencyMs": http_meta["latency_ms"],
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}),
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}, metadata={"noc": True})
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except SpeechClientError as client_exc:
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logger.warning("Speech Analytics unavailable — continuing without prediction. Error: %s", client_exc)
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speech_data["reclamacao_resumo"] = "Indisponível SPEECH para Reclamação Atual - realizar consulta manualmente"
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await set_current_step(state, GraphStep.SPEECH_ENRICHMENT_UNAVAILABLE)
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api_fields = {
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"apiUrl": getattr(client_exc, "url", None) or "N/A",
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"apiStatusCode": getattr(client_exc, "status_code", None) if getattr(client_exc, "status_code", None) is not None else "N/A",
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"apiResponsePayload": getattr(client_exc, "response_text", None) if getattr(client_exc, "response_text", None) is not None else "N/A",
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"latencyMs": getattr(client_exc, "latency_ms", None) if getattr(client_exc, "latency_ms", None) is not None else "N/A",
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}
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event("AGA.035", {
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"status": f"Erro na consulta Speech (Reclamação Atual): SpeechClientError: {str(client_exc)}",
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"type": "FAILURE",
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"session_id": session_id,
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"tag": "AGA.035",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.035", api_fields),
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}, metadata={"noc": True})
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historico: list = []
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if not cpf_cnpj:
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logger.warning("Speech enrichment skipped history fetch: cpfCnpj absent.", extra={"session_id": session_id})
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event("AGA.034", {
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"status": "Resultado consulta histórico Speech: ignorada — cpfCnpj ausente",
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"type": "INFO",
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"session_id": session_id,
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"tag": "AGA.034",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.034"),
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}, metadata={"noc": True})
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else:
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try:
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client = SpeechAnalyticsClient()
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historico, http_meta = await client.get_history(cpf_cnpj, msisdn)
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logger.info("Speech Analytics history retrieved successfully. %s items.", len(historico), extra={"session_id": session_id})
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event("AGA.034", {
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"status": f"Resultado consulta histórico Speech: sucesso — {len(historico)} item(ns)",
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"type": "SUCCESS",
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"session_id": session_id,
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"tag": "AGA.034",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.034", {
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"apiUrl": http_meta["url"],
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"apiStatusCode": http_meta["status_code"],
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"apiResponsePayload": http_meta["response_text"],
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"latencyMs": http_meta["latency_ms"],
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}),
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}, metadata={"noc": True})
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if not historico:
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event("NOC.002", {
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"status": "Invalid API response",
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"type": "WARNING",
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**build_noc_api_metadata(
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state,
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"NOC.002",
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retry_count=http_meta.get("retry_count", 0),
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latency_ms=http_meta["latency_ms"],
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api_url=http_meta["url"],
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status_code=http_meta["status_code"],
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),
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}, metadata={"noc": True})
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except Exception as exc:
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logger.warning("Failed to fetch speech history: %s", exc)
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await set_current_step(state, GraphStep.SPEECH_ENRICHMENT_UNAVAILABLE)
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api_fields = {}
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if isinstance(exc, SpeechClientError):
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api_fields = {
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"apiUrl": getattr(exc, "url", None) or "N/A",
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"apiStatusCode": getattr(exc, "status_code", None) if getattr(exc, "status_code", None) is not None else "N/A",
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"apiResponsePayload": getattr(exc, "response_text", None) if getattr(exc, "response_text", None) is not None else "N/A",
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"latencyMs": getattr(exc, "latency_ms", None) if getattr(exc, "latency_ms", None) is not None else "N/A",
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}
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event("AGA.035", {
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"status": f"Falha ao buscar histórico Speech: {str(exc)}",
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"type": "FAILURE",
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"session_id": session_id,
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"tag": "AGA.035",
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"call_id": session_id,
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"origin": "AGENT",
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**build_ic_payload(state, "AGA.035", api_fields),
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}, metadata={"noc": True})
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history_unavailable = True
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historico_relacionado: list = []
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if historico and raw_text:
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scores = await _score_history_with_llm(
|
||||
current_id=reclamacao_id,
|
||||
current_complaint_description=raw_text,
|
||||
history_list=historico,
|
||||
session_id=session_id,
|
||||
state=state,
|
||||
)
|
||||
historico_relacionado = _build_related_history(
|
||||
raw_history=historico,
|
||||
current_complaint_id=reclamacao_id,
|
||||
llm_scores=scores,
|
||||
threshold=settings.SPEECH_SIMILARITY_THRESHOLD,
|
||||
)
|
||||
|
||||
def _strip_to_detail_keys(h_list: list) -> list:
|
||||
return [{k: item.get(k) for k in _HISTORY_DETAIL_KEYS} for item in h_list]
|
||||
|
||||
speech_data["historico_bruto"] = _strip_to_detail_keys(historico)
|
||||
speech_data["historico_relacionado"] = historico_relacionado
|
||||
speech_data["analise_agente"] = _HISTORY_UNAVAILABLE_MESSAGE if history_unavailable else _build_analise_agente(historico_relacionado)
|
||||
|
||||
context = dict(context)
|
||||
context["speech_analytics"] = speech_data
|
||||
state = update_state_metadata(state, request_context=context)
|
||||
|
||||
logger.info("Speech enrichment node completed successfully. State metadata and current step updated: %s", state.get("current_step"))
|
||||
return state
|
||||
|
||||
Reference in New Issue
Block a user