Funcional

README.md

@@ -859,8 +859,7 @@ Já o código **app_nemo.py** utiliza o framework **Nemo Guardrails** para ilust
     python -m src.app_nemo
 
 
-![img_3.png](img_3.png)
+![img_4.png](img_4.png)
-
 
 ## 12. Mapeamento da planilha para implementação
 

nemo_guardrails_tracing_project/config/config.py

@@ -0,0 +1,43 @@
+from nemoguardrails import LLMRails, RailsConfig
+from src.actions import (
+    mask_pii_action,
+    detectar_toxicidade_action,
+    detectar_out_of_scope_action,
+    validar_alcada_action,
+    verbalizacao_prematura_action,
+    validar_groundedness_action,
+    supervisor_vas_avulso_action,
+    enforce_compliance_anatel_action,
+    calcular_tcr_action,
+    detectar_fallback_action,
+    registrar_violacao_action,
+    validar_consistencia_historica_action,
+    contabilizar_tokens_action,
+    calcular_eficiencia_nlu_action,
+    calcular_eficiencia_nlu_action,
+    detectar_loop_action,
+    medir_tamanho_mensagem_action,
+    calcular_precisao_revocacao_action,
+    avaliar_acuracia_semantica_action
+)
+def init(app: LLMRails):
+
+    app.register_action(mask_pii_action)
+    app.register_action(detectar_toxicidade_action)
+    app.register_action(detectar_out_of_scope_action)
+    app.register_action(validar_alcada_action)
+    app.register_action(verbalizacao_prematura_action)
+    app.register_action(validar_groundedness_action)
+    app.register_action(supervisor_vas_avulso_action)
+    app.register_action(enforce_compliance_anatel_action)
+    app.register_action(calcular_tcr_action)
+    app.register_action(detectar_fallback_action)
+    app.register_action(registrar_violacao_action)
+    app.register_action(validar_consistencia_historica_action)
+    app.register_action(contabilizar_tokens_action)
+    app.register_action(calcular_eficiencia_nlu_action)
+    app.register_action(calcular_eficiencia_nlu_action)
+    app.register_action(detectar_loop_action)
+    app.register_action(medir_tamanho_mensagem_action)
+    app.register_action(calcular_precisao_revocacao_action)
+    app.register_action(avaliar_acuracia_semantica_action)

nemo_guardrails_tracing_project/config/config.yml

@@ -1,11 +1,27 @@
-colang_version: "1.0"
-
 models:
   - type: main
     engine: openai
     model: gpt-5
+    api_key_env_var: OPENAI_API_KEY
+    parameters:
+      temperature: 0
+      base_url: http://127.0.0.1:8051/v1
+      max_tokens: 50   # 🔥 evita respostas longas do LLM
+
+  # 🔥 usado apenas se você chamar explicitamente no flow
+  - type: self_check_input
+    engine: openai
+    model: openai.gpt-oss-120b
+    api_key_env_var: OPENAI_API_KEY
+    parameters:
+      temperature: 0
+      base_url: http://127.0.0.1:8051/v1
+
 
 rails:
   input:
     flows:
-      - main
+      - check_input_terms
+  output:
+    flows:
+      - check_output_terms

nemo_guardrails_tracing_project/config/rails/flows.co

@@ -1,9 +0,0 @@
-define user express input
-  $input_text
-
-define flow main
-  user express input
-
-  execute executar_pipeline_validacoes
-
-  bot say $nemo_response

nemo_guardrails_tracing_project/requirements.txt

@@ -1,7 +1,7 @@
 pytest>=8.0.0
 pyyaml>=6.0.1
 openai>=1.0.0
-nemoguardrails>=0.9.0
+nemoguardrails>=0.21.0
 opentelemetry-api>=1.20.0
 opentelemetry-sdk>=1.20.0
 opentelemetry-exporter-otlp>=1.20.0

nemo_guardrails_tracing_project/src/actions.py

@@ -1,150 +1,284 @@
-# src/actions.py
+from typing import Optional
-
+from nemoguardrails.actions import action
-import json
+from .deterministic_rails import (
-import uuid
+    mask_pii,
-
+    validar_alcada,
+    enforce_compliance_anatel,
+    calcular_tcr,
+    detectar_fallback,
+    registrar_violacao,
+    validar_consistencia_historica,
+    contabilizar_tokens,
+    calcular_eficiencia_nlu,
+    detectar_no_match_rag,
+    detectar_loop,
+    medir_tamanho_mensagem,
+    calcular_precisao_revocacao,
+    avaliar_acuracia_semantica,
+)
 from .llm_rails import (
     detectar_toxicidade,
     detectar_out_of_scope,
     verbalizacao_prematura,
     validar_groundedness,
+    supervisor_vas_avulso,
 )
 
-from .deterministic_rails import validar_alcada
-
-try:
-    from .judges import avaliar_qualidade_resposta
-except Exception:
-    avaliar_qualidade_resposta = None
-
-
-PIPELINE_RESULTS = {}
-
-
-def extrair_payload(context: dict) -> dict:
-    try:
-        messages = context.get("messages", [])
-        content = messages[-1]["content"]
-        return json.loads(content)
-    except Exception:
-        return {}
-
-
-def add_trace(trace, label, result):
-    trace.append({
-        "rail": label,
-        "allowed": result.allowed,
-        "reason": result.reason,
-        "code": getattr(result, "code", label),
-        "mechanism": getattr(result, "mechanism", ""),
-        "data": getattr(result, "data", {}),
-    })
-
-
-def executar_pipeline_validacoes(context: dict):
-    print("🔥🔥🔥 ACTION FOI EXECUTADA")
-    payload = extrair_payload(context)
-
-    request_id = payload.get("request_id") or str(uuid.uuid4())
-    input_text = payload.get("input_text", "")
-    ctx = payload.get("context", {}) or {}
-
-    trace = []
-    failures = []
-
 # =========================
-    # INPUT RAILS - LLM
+# HELPERS
 # =========================
 
-    r_tox = detectar_toxicidade(input_text)
+def get_payload(context: Optional[dict]) -> dict:
-    add_trace(trace, "TOX", r_tox)
+    return (context or {}).get("payload", {})
-    if not r_tox.allowed:
-        failures.append(("TOX", r_tox.reason))
-
-    r_oos = detectar_out_of_scope(input_text)
-    add_trace(trace, "OOS", r_oos)
-    if not r_oos.allowed:
-        failures.append(("OOS", r_oos.reason))
 
 # =========================
-    # BUSINESS RAIL - DETERMINISTIC
+# ACTIONS
 # =========================
 
-    valor = ctx.get("ajuste_valor")
+@action(is_system_action=True)
-    r_adj = validar_alcada(valor)
+async def mask_pii_action(context: Optional[dict] = None, **kwargs):
-    add_trace(trace, "ADJ", r_adj)
+    print("🔥 MSK")
-    if not r_adj.allowed:
-        failures.append(("ADJ", r_adj.reason))
 
-    # =========================
+    payload = get_payload(context)
-    # LLM RESPONSE
+    input_text = payload.get("input_text") or context.get("user_message", "")
-    # =========================
 
-    final_response = ctx.get("resposta_llm", "")
+    result = mask_pii(input_text)
 
-    trace.append({
+    if context is not None:
-        "step": "LLM",
+        context["text"] = getattr(result, "sanitized_text", input_text)
-        "allowed": True,
-        "input": input_text,
-        "output_preview": final_response[:200],
-        "mechanism": "provided_response_or_proxy",
-    })
 
-    # =========================
+    return result
-    # OUTPUT RAILS - LLM
-    # =========================
 
-    r_revprec = verbalizacao_prematura(final_response, ctx)
-    add_trace(trace, "REVPREC", r_revprec)
-    if not r_revprec.allowed:
-        failures.append(("REVPREC", r_revprec.reason))
 
-    r_gnd = validar_groundedness(final_response, ctx)
+# -------------------------
-    add_trace(trace, "GND", r_gnd)
-    if not r_gnd.allowed:
-        failures.append(("GND", r_gnd.reason))
 
-    # =========================
+@action(is_system_action=True)
-    # OPTIONAL JUDGE / CMP
+async def detectar_toxicidade_action(context: Optional[dict] = None, **kwargs):
-    # =========================
+    print("🔥 TOX")
 
-    if avaliar_qualidade_resposta is not None:
+    text = context.get("text") or context.get("user_message", "")
-        r_cmp = avaliar_qualidade_resposta(input_text, final_response)
-        add_trace(trace, "CMP", r_cmp)
-        if not r_cmp.allowed:
-            failures.append(("CMP", r_cmp.reason))
-    else:
-        trace.append({
-            "rail": "CMP",
-            "allowed": True,
-            "reason": "CMP não configurado",
-            "mechanism": "skipped",
-            "data": {},
-        })
 
-    # =========================
+    result = detectar_toxicidade(text)
-    # FINAL DECISION
-    # =========================
 
-    blocked = len(failures) > 0
+    return result
 
-    if blocked:
-        first_code, first_reason = failures[0]
-        nemo_response = f"BLOCKED:{first_code} - {first_reason}"
-    else:
-        nemo_response = final_response
 
-    result = {
+# -------------------------
-        "allowed": not blocked,
+
-        "label": "CONFORME" if not blocked else "PROBLEMA",
+@action(is_system_action=True)
-        "response": final_response,
+async def detectar_out_of_scope_action(context: Optional[dict] = None, **kwargs):
-        "reason": nemo_response if blocked else "",
+    print("🔥 OOS")
-        "failures": failures,
+
-        "trace": trace,
+    text = context.get("text") or context.get("user_message", "")
-    }
+
+    result = detectar_out_of_scope(text)
+
+    return result
+
+
+# -------------------------
+
+@action(is_system_action=True)
+async def validar_alcada_action(context: Optional[dict] = None, **kwargs):
+    print("🔥 ADJ")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    valor = ctx.get("ajuste_valor", 0)
+
+    result = validar_alcada(valor)
+
+    return result
+
+
+# -------------------------
+
+@action(is_system_action=True)
+async def verbalizacao_prematura_action(context: Optional[dict] = None, **kwargs):
+    print("🔥 REVPREC")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    resposta = ctx.get("resposta_llm", "")
+
+    result = verbalizacao_prematura(resposta, ctx)
+
+    return result
+
+
+# -------------------------
+
+@action(is_system_action=True)
+async def validar_groundedness_action(context: Optional[dict] = None, **kwargs):
+    print("🔥 GND")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    resposta = ctx.get("resposta_llm", "")
+
+    result = validar_groundedness(resposta, ctx)
+
+    return result
+
+# -------------------------
+
+@action(is_system_action=True)
+async def supervisor_vas_avulso_action(context: Optional[dict] = None, **kwargs):
+    print("🔥 REVPREC_SUP")
+
+    payload = get_payload(context)
+
+    result = supervisor_vas_avulso(payload)
+
+    return result
+
+@action(is_system_action=True)
+async def enforce_compliance_anatel_action(context=None, **kwargs):
+    print("🔥 CMP")
+
+    text = context.get("text") or context.get("user_message", "")
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = enforce_compliance_anatel(text, ctx)
+
+    return result
+
+@action(is_system_action=True)
+async def calcular_tcr_action(context=None, **kwargs):
+    print("🔥 TCR")
+
+    payload = get_payload(context)
+    status = payload.get("context", {}).get("status", "")
+
+    result = calcular_tcr(status)
+
+    return result
+
+@action(is_system_action=True)
+async def detectar_fallback_action(context=None, **kwargs):
+    print("🔥 FALLBACK")
+
+    text = context.get("text") or context.get("user_message", "")
+
+    result = detectar_fallback(text)
+
+    return result
+
+@action(is_system_action=True)
+async def registrar_violacao_action(context=None, **kwargs):
+    print("🔥 VIOL")
+
+    payload = get_payload(context)
+    agent_id = payload.get("agent_id", "unknown")
+    code = payload.get("violation_code", "UNKNOWN")
+
+    result = registrar_violacao(agent_id, code)
+
+    return result
+
+@action(is_system_action=True)
+async def validar_consistencia_historica_action(context=None, **kwargs):
+    print("🔥 HIST")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = validar_consistencia_historica(ctx)
+
+    return result
+
+@action(is_system_action=True)
+async def contabilizar_tokens_action(context=None, **kwargs):
+    print("🔥 PMPTK")
+
+    payload = get_payload(context)
+    prompt = payload.get("prompt_tokens", 0)
+    completion = payload.get("completion_tokens", 0)
+
+    result = contabilizar_tokens(prompt, completion)
+
+    return result
+
+@action(is_system_action=True)
+async def calcular_eficiencia_nlu_action(context=None, **kwargs):
+    print("🔥 EFIC")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = calcular_eficiencia_nlu(
+        ctx.get("chunks_retornados", 0),
+        ctx.get("chunks_utilizados", 0)
+    )
+
+    return result
+
+@action(is_system_action=True)
+async def detectar_no_match_rag_action(context=None, **kwargs):
+    print("🔥 NO-M")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = detectar_no_match_rag(
+        ctx.get("chunks", []),
+        ctx.get("resposta_llm", "")
+    )
+
+    return result
+
+@action(is_system_action=True)
+async def detectar_loop_action(context=None, **kwargs):
+    print("🔥 VLOOP")
+
+    payload = get_payload(context)
+    mensagens = payload.get("context", {}).get("mensagens", [])
+
+    result = detectar_loop(mensagens)
+
+    return result
+
+@action(is_system_action=True)
+async def medir_tamanho_mensagem_action(context=None, **kwargs):
+    print("🔥 MSIZE")
+
+    text = context.get("text") or context.get("user_message", "")
+
+    result = medir_tamanho_mensagem(text)
+
+    return result
+
+@action(is_system_action=True)
+async def calcular_precisao_revocacao_action(context=None, **kwargs):
+    print("🔥 REVPREC_METRIC")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = calcular_precisao_revocacao(
+        ctx.get("y_true", []),
+        ctx.get("y_pred", [])
+    )
+
+    return result
+
+@action(is_system_action=True)
+async def avaliar_acuracia_semantica_action(context=None, **kwargs):
+    print("🔥 SEMAC")
+
+    payload = get_payload(context)
+    ctx = payload.get("context", {})
+
+    result = avaliar_acuracia_semantica(
+        ctx.get("audio_transcrito", ""),
+        ctx.get("referencia_humana", "")
+    )
+
+    return result
+
 
-    PIPELINE_RESULTS[request_id] = result
 
-    return {
-        "nemo_response": nemo_response
-    }

nemo_guardrails_tracing_project/src/app_nemo.py

@@ -1,133 +1,47 @@
-import json
+#https://docs.nvidia.com/nemo/guardrails/latest/configure-rails/actions/index.html
-import uuid
+#https://docs.nvidia.com/nemo/guardrails/latest/configure-rails/actions/registering-actions.html
+#https://docs.nvidia.com/nemo/guardrails/latest/observability/logging/index.html
 
 from nemoguardrails import LLMRails, RailsConfig
 
-from src.actions import executar_pipeline_validacoes, PIPELINE_RESULTS
-
-
-def build_rails():
 config = RailsConfig.from_path("./config")
 rails = LLMRails(config)
 
-    rails.register_action(
+def extract_return_values(response):
-        executar_pipeline_validacoes,
+    results = []
-        "executar_pipeline_validacoes"
-    )
 
-    return rails
+    log = response.log
 
-
+    for rail in log.activated_rails:
-rails = build_rails()
+        for action in rail.executed_actions:
-
+            rv = action.return_value
-
+            if rv is not None:
-def executar_atendimento(user_input: str, context: dict):
+                results.append({
-
+                    "action": action.action_name,
-    request_id = str(uuid.uuid4())
+                    "allowed": getattr(rv, "allowed", None),
-
+                    "reason": getattr(rv, "reason", None),
-    payload = {
+                    "sanitized_text": getattr(rv, "sanitized_text", None),
-        "request_id": request_id,
+                    "code": getattr(rv, "code", None),
-        "input_text": user_input,
+                    "mechanism": getattr(rv, "mechanism", None),
-        "context": context or {},
+                    "data": getattr(rv, "data", None)
-    }
-
-    # 🔥 chama direto seu pipeline
-    executar_pipeline_validacoes({
-        "messages": [
-            {
-                "role": "user",
-                "content": json.dumps(payload)
-            }
-        ]
                 })
 
-    result = PIPELINE_RESULTS.pop(request_id, None)
+    return results
 
-    return result
+MESSAGE = "Meu CPF é 169.323.728-86"
 
-
+response = rails.generate(
-if __name__ == "__main__":
+    messages=[{"role": "user", "content": MESSAGE}],
-
+    options={
-    def rodar_teste(nome, user_input, context):
+        "output_vars": ["triggered_input_rail", "relevant_chunks"],
-        print("\n" + "="*60)
+        "log": {
-        print(f"🧪 TESTE: {nome}")
+            "activated_rails": True,
-        print("-"*60)
+            "llm_calls": True
-        print("INPUT:", user_input)
+        }
-        print("CONTEXT:", context)
-
-        result = executar_atendimento(user_input, context)
-
-        print("\n📌 RESULTADO FINAL:")
-        print(result)
-
-        print("\n🔍 TRACE:")
-        for step in result.get("trace", []):
-            print(step)
-
-        print("="*60)
-
-
-    # =========================
-    # ✅ TESTE 1 - OK (CONFORME)
-    # =========================
-    rodar_teste(
-        "OK - Cancelamento válido",
-        "quero cancelar VAS",
-        {
-            "ajuste_valor": 20,
-            "resposta_llm": "Cancelamento realizado com sucesso."
     }
 )
 
+feedback = extract_return_values(response)
 
-    # =========================
+for f in feedback:
-    # ❌ TESTE 2 - TOXICIDADE
+    print(f)
-    # =========================
-    rodar_teste(
-        "TOX - Linguagem ofensiva",
-        "quero cancelar VAS seu lixo",
-        {
-            "ajuste_valor": 20,
-            "resposta_llm": "Cancelamento realizado."
-        }
-    )
-
-
-    # =========================
-    # ❌ TESTE 3 - ALÇADA
-    # =========================
-    rodar_teste(
-        "ADJ - Valor acima da alçada",
-        "quero cancelar VAS",
-        {
-            "ajuste_valor": 2000,
-            "resposta_llm": "Cancelamento realizado."
-        }
-    )
-
-
-    # =========================
-    # ❌ TESTE 4 - GROUNDEDNESS
-    # =========================
-    rodar_teste(
-        "GND - Resposta não confiável",
-        "quero cancelar VAS",
-        {
-            "ajuste_valor": 20,
-            "resposta_llm": "O cancelamento ativa cashback infinito e crédito ilimitado."
-        }
-    )
-
-
-    # =========================
-    # ⚠️ TESTE 5 - MÚLTIPLOS PROBLEMAS
-    # =========================
-    rodar_teste(
-        "MULTI - TOX + ADJ + GND",
-        "quero cancelar VAS seu inútil",
-        {
-            "ajuste_valor": 5000,
-            "resposta_llm": "Você ganha benefícios ilimitados com esse cancelamento."
-        }
-    )
-

nemo_guardrails_tracing_project/src/prompts/tox.py

@@ -6,7 +6,7 @@ Texto:
 {text}
 
 Classifique como:
-- TOXICO
+- TOXICO: Apenas se encontrar palavras ofensivas ou condenatórias
 - NORMAL
 
 Responda JSON: