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src/agent/nodes/speech_enrichment_node.py

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+import time
+import logging
+import json
+from src.agent.state.agent_state import AgentState, update_state_metadata
+from src.agent.state.steps import GraphStep
+from src.agent.state.step_helpers import set_current_step
+from src.components.clients.speech_analytics_client import SpeechAnalyticsClient
+from src.components.clients.exceptions.speech_exceptions import SpeechClientError
+from src.utils.observer import trace_node, score_current_trace
+from src.utils.ics_collector import build_ic_payload, build_noc_api_metadata, build_noc_llm_metadata
+from src.compat.framework_observer import event
+from src.providers.llm_provider import classification_llm, chat_llm_with_usage, LLM_ENDPOINT
+from src.core.config import settings
+from src.core.prompt_manager import get_prompt
+from src.agent.local_prompts.speech_history_analysis import speech_history_similarity_pt
+
+logger = logging.getLogger(__name__)
+
+_NO_SIMILAR_MESSAGE = "Não foram encontradas reclamações anteriores similares à reclamação atual"
+_HISTORY_UNAVAILABLE_MESSAGE = "Indisponível SPEECH para Histórico de Reclamação - realizar consulta manualmente"
+
+_HISTORY_DETAIL_KEYS = (
+    "reclamacao_resumo",
+    "causa_raiz",
+    "descortesia_cliente",
+    "motivo_reclamacao",
+    "submotivo_reclamacao",
+    "sentimento_cliente",
+    "solucao_proposta_cliente",
+)
+
+_NULL_SPEECH_DATA = {
+    "reclamacao_resumo": None,
+    "causa_raiz": None,
+    "descortesia_cliente": None,
+    "motivo_reclamacao": None,
+    "submotivo_reclamacao": None,
+    "sentimento_cliente": None,
+    "solucao_proposta_cliente": None,
+    "analise_agente": None,
+}
+
+
+def _map_prediction_response(response: dict) -> dict:
+    """
+    Map the raw Prediction API response to the internal speech_analytics schema.
+    """
+    variables = response.get("variables", {})
+    return {
+        "reclamacao_resumo": response.get("resume") or variables.get("resume"),
+        "causa_raiz": variables.get("causa_raiz"),
+        "descortesia_cliente": variables.get("descortesia_cliente"),
+        "motivo_reclamacao": variables.get("motivo"),
+        "submotivo_reclamacao": variables.get("submotivo"),
+        "sentimento_cliente": variables.get("sentimento_cliente"),
+        "solucao_proposta_cliente": variables.get("solucao_proposta_cliente"),
+    }
+
+
+def _build_related_history(
+    raw_history: list,
+    current_complaint_id: str | None,
+    llm_scores: list,
+    threshold: int,
+) -> list:
+    """
+    Merge LLM similarity scores with raw history items and filter by threshold.
+
+    Each returned item has: protocolo, data_reclamacao, similaridade_pct,
+    reasoning, plus the 7 detail keys (same as reclamacao_atual).
+    """
+    if not raw_history or not llm_scores:
+        return []
+
+    history_by_protocol = {str(item.get("protocolo")): item for item in raw_history}
+    related: list[dict] = []
+
+    for score in llm_scores:
+        protocolo = str(score.get("protocolo")) if score.get("protocolo") is not None else None
+        if not protocolo or protocolo == str(current_complaint_id or ""):
+            continue
+        try:
+            similaridade_pct = int(score.get("similaridade_pct", 0))
+        except (TypeError, ValueError):
+            continue
+        if similaridade_pct < threshold:
+            continue
+
+        raw_item = history_by_protocol.get(protocolo)
+        if not raw_item:
+            continue
+
+        merged = {
+            "protocolo": raw_item.get("protocolo"),
+            "data_reclamacao": raw_item.get("data_reclamacao"),
+            "similaridade_pct": similaridade_pct,
+            "reasoning": (score.get("reasoning") or "").strip(),
+        }
+        for key in _HISTORY_DETAIL_KEYS:
+            merged[key] = raw_item.get(key)
+        related.append(merged)
+
+    related.sort(key=lambda x: x["similaridade_pct"], reverse=True)
+    return related
+
+
+def _build_analise_agente(related_items: list) -> str:
+    """
+    Build the analise_agente string from related items: one line per item with
+    'Protocolo X (Y%): <reasoning>'. Empty list -> negative spec message.
+    """
+    if not related_items:
+        return _NO_SIMILAR_MESSAGE
+
+    lines = []
+    for item in related_items:
+        reasoning = (item.get("reasoning") or "").strip()
+        if not reasoning:
+            reasoning = "—"
+        lines.append(f"Protocolo {item.get('protocolo')} ({item.get('similaridade_pct')}%): {reasoning}")
+    return "\n".join(lines)
+
+
+async def _score_history_with_llm(
+    current_id: str | None,
+    current_complaint_description: str,
+    history_list: list,
+    session_id: str = "",
+    state: dict | None = None,
+) -> list:
+    """
+    Calls the LLM to score similarity between the current complaint description
+    and each historical item. Returns the LLM-parsed JSON list. Items below
+    SPEECH_SIMILARITY_THRESHOLD will be dropped later by _build_related_history.
+    """
+    if not current_complaint_description or not history_list:
+        return []
+
+    clean_history = [
+        item for item in history_list
+        if str(item.get("protocolo")) != str(current_id or "")
+    ]
+    if not clean_history:
+        logger.info("No historical items left after deduplication.")
+        return []
+
+    llm = classification_llm
+    prompt_template = get_prompt("speech_history_similarity_pt", speech_history_similarity_pt)
+
+    history_payload = [
+        {
+            "protocolo": item.get("protocolo"),
+            "motivo_reclamacao": item.get("motivo_reclamacao"),
+            "submotivo_reclamacao": item.get("submotivo_reclamacao"),
+            "causa_raiz": item.get("causa_raiz"),
+            "reclamacao_resumo": item.get("reclamacao_resumo"),
+        }
+        for item in clean_history
+    ]
+
+    message = prompt_template.format(
+        current_complaint_description=current_complaint_description,
+        history_json=json.dumps(history_payload, ensure_ascii=False),
+    )
+
+    logger.info(f"Calling LLM for history similarity filtering. Items: {len(clean_history)}")
+
+    # LLM generation telemetry is recorded by agent_framework.llm.providers.