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17 lines
1.2 KiB
Python
17 lines
1.2 KiB
Python
from .models import RailResult
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from .llm_client import LLMClient
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from .tracing import span
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_client=LLMClient()
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def classificar_sentimento(text:str)->RailResult:
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with span("judge.CSI", mechanism="llm_judge"):
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out=_client.classify("CSI", {"text":text}); return RailResult(True,out.get("reason",""),text,"CSI","llm_judge",{"sentimento":out.get("label"),**out})
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def avaliar_alucinacao(resposta:str, dados_reais:str)->RailResult:
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with span("judge.ALUC", mechanism="llm_judge"):
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out=_client.classify("ALUC", {"resposta":resposta,"dados_reais":dados_reais}); return RailResult(out["allowed"],out.get("reason",""),resposta,"ALUC","llm_judge",{"alucinacao":out.get("label")=="ALUCINACAO",**out})
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def avaliar_qualidade_resposta(pergunta:str, resposta:str)->RailResult:
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with span("judge.RQLT", mechanism="llm_judge"):
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out=_client.classify("RQLT", {"pergunta":pergunta,"resposta":resposta}); return RailResult(True,out.get("reason",""),resposta,"RQLT","llm_judge",out)
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def avaliar_tom_de_voz(text:str)->RailResult:
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with span("judge.VCTN", mechanism="llm_judge"):
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out=_client.classify("VCTN", {"text":text}); return RailResult(out["allowed"],out.get("reason",""),text,"VCTN","llm_judge",{"aderente":out["allowed"],**out})
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