tools.py

import os
from langchain.agents import tool
from langchain_community.chat_models import ChatOpenAI
import pandas as pd
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain.schema.runnable import RunnablePassthrough
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.agents import AgentExecutor
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from config import settings

MEMORY = None


def get_embeddings(text_list):
    encoded_input = settings.tokenizer(
        text_list, padding=True, truncation=True, return_tensors="pt"
    )
    # encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
    encoded_input = {k: v for k, v in encoded_input.items()}
    model_output = settings.model(**encoded_input)
    
    cls_pool = model_output.last_hidden_state[:, 0]
    return cls_pool

def reg(chat):
  question_embedding = get_embeddings([chat]).cpu().detach().numpy()
  scores, samples = settings.dataset.get_nearest_examples(
      "embeddings", question_embedding, k=5
  )
  samples_df = pd.DataFrame.from_dict(samples)
  # print(samples_df.columns)
  samples_df["scores"] = scores
  samples_df.sort_values("scores", ascending=False, inplace=True)
  return samples_df[['title', 'cover_image', 'referral_link', 'category_id']]


@tool("MOXICASTS-questions", )
def moxicast(prompt: str) -> str:
    """this function is used when user wants to know about MOXICASTS feature.MOXICASTS is a feature of BMoxi for Advice and guidance on life topics.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MOXICASTS is a feature of BMoxi for Advice and guidance on life topics."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content

@tool("PEP-TALKPODS-questions", )
def peptalks(prompt: str) -> str:
    """this function is used when user wants to know about PEP TALK PODS feature.PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PEP TALK PODS: Quick audio pep talks for boosting mood and motivation."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content



@tool("SOCIAL-SANCTUARY-questions", )
def sactury(prompt: str) -> str:
    """this function is used when user wants to know about SOCIAL SANCTUARY feature.THE SOCIAL SANCTUARY Anonymous community forum for support and sharing.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. THE SOCIAL SANCTUARY Anonymous community forum for support and sharing."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content


@tool("POWER-ZENS-questions", )
def power_zens(prompt: str) -> str:
    """this function is used when user wants to know about POWER ZENS feature. POWER ZENS Mini meditations for emotional control.

    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. POWER ZENS Mini meditations for emotional control."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content



@tool("MY-CALENDAR-questions", )
def my_calender(prompt: str) -> str:
    """this function is used when user wants to know about MY CALENDAR feature.MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY CALENDAR: Visual calendar for tracking self-care rituals and moods."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content




@tool("PUSH-AFFIRMATIONS-questions", )
def affirmations(prompt: str) -> str:
    """this function is used when user wants to know about PUSH AFFIRMATIONS feature.PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PUSH AFFIRMATIONS: Daily text affirmations for positive thinking."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content

@tool("HOROSCOPE-questions", )
def horoscope(prompt: str) -> str:
    """this function is used when user wants to know about HOROSCOPE feature.SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content



@tool("INFLUENCER-POSTS-questions", )
def influencer_post(prompt: str) -> str:
    """this function is used when user wants to know about INFLUENCER POSTS feature.INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon)."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content


@tool("MY-VIBECHECK-questions", )
def my_vibecheck(prompt: str) -> str:
    """this function is used when user wants to know about MY VIBECHECK feature. MY VIBECHECK: Monitor and understand emotional patterns.

    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY VIBECHECK: Monitor and understand emotional patterns."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content



@tool("MY-RITUALS-questions", )
def my_rituals(prompt: str) -> str:
    """this function is used when user wants to know about MY RITUALS feature.MY RITUALS: Create personalized self-care routines.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY RITUALS: Create personalized self-care routines."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content




@tool("MY-REWARDS-questions", )
def my_rewards(prompt: str) -> str:
    """this function is used when user wants to know about MY REWARDS feature.MY REWARDS: Earn points for self-care, redeemable for gift cards.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY REWARDS: Earn points for self-care, redeemable for gift cards."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content


@tool("mentoring-questions", )
def mentoring(prompt: str) -> str:
    """this function is used when user wants to know about 1-1 mentoring feature.  1:1 MENTORING: Personalized mentoring (coming soon).

    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.  1:1 MENTORING: Personalized mentoring (coming soon)."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content



@tool("MY-JOURNAL-questions", )
def my_journal(prompt: str) -> str:
    """this function is used when user wants to know about MY JOURNAL feature.MY JOURNAL: Guided journaling exercises for self-reflection.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY JOURNAL: Guided journaling exercises for self-reflection."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you are going to make answer only using this context not use any other information
   context : {context}
    Input: {input}
    """
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content

@tool("podcast-recommendation-tool")
def recommand_podcast(prompt: str) -> str:
    """ this function must used when user wants to any resources only.
    Args:
        prompt (string): user query

    Returns:
        string: answer of the query
    """
    df = reg(prompt)
    context = """"""
    for index, row in df.iterrows():
        'title', 'cover_image', 'referral_link', 'category_id'
        context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """ you have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast.
    you must use the context only not any other information.
    context : {context}
    """
    # print(system_template.format(context=context, input=prompt))
    response = llm.invoke(system_template.format(context=context, input=prompt))

    return response.content

@tool("set-chat-bot-name",return_direct=True )
def set_chatbot_name(name: str) -> str:
    """ this function is used when your best friend want to give you new name.
    Args:
        name (string): new name of you.

    Returns:
        string: response after setting new name.
    """

    return "Okay, from now my name will be "+ name 

@tool("clossing-chat",return_direct=True)
def close_chat(summary:str)-> str:
    """ when you feel it's time to finish the conversation use this tool.
        must use this tool when user closing the conversation. must use this tool when you are ending the conversation.

    Args:
        summary (str): summary of whole chat with your friend.

    Returns:
        str: closing chat statements.
    """
    
    print('close tool starts')
    system_template = """ you have given one summary of chat. 
    summary : {summary}.
    using this summary give recommandation of podcast or suggest any features from given tools and make response. also you are going to close the conversation with your friend.
    if user already received suggestions, you must not give again. and just end the conversation inshort wihtout mentioning anything. 
    # make all responses short and don't remove any podcast links.
    """
           
    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast]
    functions = [convert_to_openai_function(f) for f in tools]
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7).bind(functions=functions)
    print('llm is created')
    
    prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
    print('chain is rolling')
    agent = AgentExecutor(agent=chain, tools=tools, memory=MEMORY, verbose=True)
    # Define the system prompt
                      
    print('agent is created')
    # print(system_template.format(context=context, input=prompt))\

    response = agent.invoke({})['output']
    return response


@tool("App-Fetures")
def app_features(summary:str)-> str:
    """ For any app features details only use this tool.

    Args:
        summary (str): summary of whole chat with your friend.

    Returns:
        str: closing chat statements.
    """
    
    print('app feature tool starts')
    system_template = """ you have given one summary of chat. 
    summary : {summary}.
    if summary doesn't specify any feature name ask question don't invoke any tool.
    using this summary give appropriate features suggestions using tools.
    # make all responses short.
    """
           
    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post]
    functions = [convert_to_openai_function(f) for f in tools]
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7).bind(functions=functions)
    print('llm is created')
    
    prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
    print('chain is rolling')
    agent = AgentExecutor(agent=chain, tools=tools, memory=MEMORY, verbose=True)
    # Define the system prompt
                      
    print('agent is created')
    # print(system_template.format(context=context, input=prompt))\

    response = agent.invoke({})['output']
    return response

# close_chat('Suggest a podcast or self-care tool for someone looking to unwind after a hectic day at work.')



@tool("Joke-teller", )
def joke_teller(summary: str) -> str:
    """If user needs mood boost and when you feel to lighten the environment use this tool to tell the jokes.
     Args:
        summary (str): summary of whole chat with your friend.

    Returns:
        string: answer of the query
    """
    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY REWARDS: Earn points for self-care, redeemable for gift cards."
    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    # Define the system prompt
    system_template = """  summary : {summary}.
    you are given summary of current chat. make one joke for your friend. to boost her mood.
    # make all responses short.
    """
    response = llm.invoke(system_template.format(summary=summary))

    return response.content