prompts.py

from langchain_core.prompts import ChatPromptTemplate

# Define a list of harmful or inappropriate topics to block
HARMFUL_TOPICS = ["kill", "suicide", "harm", "death", "murder", "violence"]

classification_prompt_str = """
You are a helpful assistant that classifies user questions into three categories:
1) "Wellness" if the question involves health, nutrition, fitness, mental well-being, self-care, or research related to these.
2) "Brand" if the question specifically pertains to 'DailyWellnessAI'—its mission, disclaimers, features, policies, etc.
3) "OutOfScope" if it does not fall into the above two categories or if the question involves harmful topics.
**Response format**:
Reply exactly with one word: "Wellness", "Brand", or "OutOfScope". Do not provide any additional explanation.
"""

tailor_prompt_str = """
You are a helpful assistant for DailyWellnessAI. Your role is to simplify complex ideas and offer actionable, user-friendly advice that aligns with our mission to enhance daily wellness through AI.
Here's the response to tailor:
{response}
Tailor it to ensure:
- Simplicity and clarity.
- Practicality, with actionable recommendations where appropriate.
- Alignment with DailyWellnessAI's mission of simplifying daily wellness through AI.
Provide the revised, concise response below:
"""

cleaner_prompt_str = """
You are a helpful AI. Below, you have two sources of information:
1) CSV (Knowledge Base) Answer:
{kb_answer}
2) Web Search Result:
{web_answer}
Your task:
- Combine and synthesize the two into a single, clear, cohesive answer.
- Avoid duplication or irrelevant details.
- Present the final response in straightforward, user-friendly language.
Do not repeat content verbatim. Merge the information meaningfully and provide your synthesized answer below:
"""

# Refusal prompt with dynamic topic insertion
refusal_prompt_str = """
You are a helpful assistant for DailyWellnessAI. Your role is to work as refusal agent and politely refuse user higlighting the reason:
1. if the {topic} contain gibberish directly tell user this input doesn't make sense
2. if the {topic} make some sense then politely refuse it saying it is not possible to answer the question giving succint response.
Here's the topic to refuse:
{topic}
Tailor it to ensure:
- Simplicity and clarity.
- Practicality, with actionable recommendations where appropriate.
- Alignment with DailyWellnessAI's mission of simplifying daily wellness through AI.
Provide the revised, concise response below and take following as a example but keep it concise and start with NO,
This question doesn’t directly fall under the categories of daily wellness or questions about the DailyWellnessAI brand. 
However, here's something to think about: Did you know that learning about {topic} can actually have a positive impact on your wellness? 
It can help promote mindfulness, relaxation, balance, or focus, all of which contribute to your overall well-being. 
For more wellness-related questions or to learn more about DailyWellnessAI, feel free to ask—I’m here to support your wellness journey!
"""

# Define the PromptTemplate objects:

classification_prompt = ChatPromptTemplate(
    [
        ("system",classification_prompt_str),
        ("user","{query}")
    ]
)

tailor_prompt = PromptTemplate(
    template=tailor_prompt_str,
    input_variables=["response"]
)

cleaner_prompt = PromptTemplate(
    template=cleaner_prompt_str,
    input_variables=["kb_answer", "web_answer"]
)

refusal_prompt = PromptTemplate(
    template=refusal_prompt_str,
    input_variables=["topic"]
)

# Define function to check if the query contains harmful topics
def is_harmful(query: str) -> bool:
    # Check if the query contains any harmful topics
    for topic in HARMFUL_TOPICS:
        if topic.lower() in query.lower():
            return True
    return False

# Now modify the classification logic:
def classify_query(query: str) -> str:
    if is_harmful(query):
        return "OutOfScope"
    # Use the classification prompt template to classify the query
    classification = classification_prompt.invoke({"query": query}).get("text", "").strip()
    return classification