User Satisfaction

from uptrain import EvalLLM, ConversationSatisfaction

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    'conversation' : [
        {"role": "patient", "content": "Help"}, 
        {"role": "nurse", "content": "what do you need"}, 
        {"role": "patient", "content": "Having chest pain"}, 
        {"role": "nurse", "content": "Sorry, I am not sure what that means"},
        {"role": "patient", "content": "You don't understand. Do something! I am having severe pain in my chest"}
    ]  
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data=data,
    checks=[ConversationSatisfaction(user_persona="patient", llm_persona="nurse")]
)

[
   {
      "score_conversation_satisfaction": 0.0,
      "explanation_conversation_satisfaction": "1. The patient starts the conversation with a simple \"Help\" which indicates urgency and distress.\n2. The nurse responds by asking what the patient needs, showing willingness to help.\n3. The patient then explains that they are having chest pain, which is a serious medical concern.\n4. The nurse responds by saying they are not sure what that means, which could be perceived as dismissive or unhelpful.\n5. The patient then expresses frustration and urgency by saying \"Do something! I am having severe pain in my chest.\"\n\nBased on the conversation, it is clear that the patient is not at all satisfied in the conversation. The nurse's response did not address the patient's urgent medical concern, and the patient's frustration is evident in their final statement.\n\n0.0\n0.0"
   }
]