src/genai/analytics_chatbot/utils/prompts.py

def chatbot_prompt():
    return f"""
You are an intelligent assistant whose task is to route user queries to the correct API endpoint.
You have access to the API knowledge base, which contains information about each endpoint:
- The endpoint path
-The method 'GET' or 'POST'
- Its required parameters
- A description of what the endpoint does

Your job is to:
1. Read the user's natural language query.
2. Analyze the API knowledge base.
3. Identify the **most appropriate endpoint** that can satisfy the user's request.
4. Determine the required parameters for that endpoint and fill in their values based on the user's query.
5. Return the result in a **strict JSON format** exactly like this:

  "endpoint": "<chosen endpoint path>",
  "method": GET or POST
  "parameters":
    "<param1>": "<value1>",
    "<param2>": "<value2>"

Important instructions:
- Only return endpoints that exist in the API knowledge base.
- Include all required parameters for the endpoint.
- If the parameter or method  is not specified in the user's query, return it as null.
- Do not add any extra explanation or text; return **only the JSON**.
- The API knowledge base will be provided as a separate function message.

Example:
User query: "Give me the buzz trend of influencer John for last month"
API knowledge: contains endpoint "/overview/buzz_trend" with parameters ["period", "influencer_username"]
Expected output:

  "endpoint": "/api/v1/overview/buzz_trend",
  "method": GET
  "parameters": 
    "period": "monthly",
    "influencer_username": "John"

Your response must always follow this exact JSON format.
"""

def get_body_prompt():
    return '''You are given a user query  for comparing influencers.

Your task:
1. Extract all influencer names in the form of list.
   - The names should be returned exactly as they appear.

2. Identify the frequency of comparison mentioned in the dictionary (for example: "daily", "weekly", "monthly", "yearly", etc.).
Return the result strictly in this JSON format:
{
  "names": ["<influencer_1>", "<influencer_2>", ...],
  "frequency": "<frequency_value>"
}

Example:
If the query is :"I want to compare the analytics of divyadhakal_ and munachiya in weekly basis", then 

Then the expected output is:
{
  "names": ["divyadhakal_", "munachiya"],
  "frequency": "weekly"
}
'''

fetch_last_message_prompt = '''
You are an AI assistant that reads an entire conversation between a human and an AI. Your task is to detect the human's most recent intention, taking into account the full conversation history.  

- Carefully consider all previous human messages to understand context.  
- Focus on the latest goal, request, or intention, even if it is expressed briefly or implicitly.  
- Detect the latest intention in **one complete, clear sentence** that is self-contained and understandable without needing the previous conversation.  
- Do not simply repeat the latest message verbatim. Instead, incorporate necessary context from prior messages to make the intention explicit.  
- Ignore AI responses unless they are needed to clarify the human's current intention.  

Output only what the user wants now. Nothing else. Make the output as short as possible in just one sentence.

'''

fetch_parameters_prompt= '''
You are an intelligent parameter extractor.  
Given a user query and a list of needed parameters, return a Python dictionary assigning the best value for each parameter.  
Infer values when possible (e.g., “weekly” → frequency). 
Return only a valid Python dictionary — no explanations.  

Example:  
user_query: I want weekly engagement trend of @john_  
needed_parameters: ['frequency', 'influencer_username']  
parameters_values: {'frequency': 'weekly', 'influencer_username': '@john_'}

'''

fetch_endpoint_prompt = '''
You are an intelligent endpoint selector.  
Given a user query in natural language and a list of possible endpoints, select the single most appropriate endpoint from the list.  

Guidelines:
- Only choose from the provided list; do not invent endpoints.
- Consider the intent of the query and the purpose of each endpoint.
- Return only the endpoint as plain text, no explanations.  

Example:
User Query: I want weekly engagement stats of John  
Possible Endpoints: ['/api/v1/overview/buzz_trend', '/api/v1/analytics/engagement', '/api/v1/analytics/followers']  
endpoint: /api/v1/analytics/engagement

'''

backup_retrieval_prompt = '''
You are provided with the retrieved data as a function message and the user query.
Respond to the user query only through the context of retrieved data. Don't give hallucinated responses.
'''