Update tools.py

tools.py

@@ -1,473 +1,476 @@
-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
-from database_functions import set_recommendation_count,get_recommendation_count
-
-MEMORY = None
-SESSION_ID= ""
-
-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:
-    """  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. give 3-4 podcast only.
-    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))
-    set_recommendation_count(SESSION_ID)
-    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:
-    """  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. must use I for yourself and for user use my friend.
-
-    Returns:
-        str: closing chat statements.
-    """
-    
-    print('close tool starts')
-
-    global SESSION_ID
-    tools = []
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    if (not get_recommendation_count(SESSION_ID)):
-        tools.extend([moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post,recommand_podcast])
-    
-    if len(tools)!=0:
-        functions = [convert_to_openai_function(f) for f in tools]
-        llm = llm.bind(functions= functions)
-    print('llm is created')
-    
-    system_template = """"""
-    if len(tools)!=0:
-        system_template += """ context: there is one typical conversation going on between two high school gen z girls.
-        you are one of the high school gen z girl. your voice is edgy and raw. 
-        now you are going to close the conversation.
-        summary of conversation is given to you.
-        summary: {summary}
-        give recommendation of any podcast or app feature based on summary and close the conversation in short.
-        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
-        """
-    else:
-        system_template+= """ context: there is one typical conversation going on between two high school gen z girls.
-        you are one of the high school gen z girl. your voice is edgy and raw. 
-        now you are going to close the conversation.
-        summary of conversation is given to you.
-        summary: {summary}
-        now just end the conversation in 1 sentense in short.
-        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
-        """
-
-
-    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:
-    """ must use For any app features details.
-
-    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}.
-    using this summary give appropriate features suggestions using tools. if you don't find any tool appropriate to summary ask question only.
-    # 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))\
-    set_recommendation_count(SESSION_ID)
-    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))
-
+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
+from database_functions import set_recommendation_count,get_recommendation_count
+
+MEMORY = None
+SESSION_ID= ""
+
+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:
+    """  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. give 3-4 podcast only.
+    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))
+    set_recommendation_count(SESSION_ID)
+    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:
+    """  must use this tool when the conversation about to end.
+    Args:
+        summary (str): summary of whole chat with your friend. must use I for yourself and for user use my friend.
+
+    Returns:
+        str: closing chat statements.
+    """
+    
+    print('close tool starts')
+
+    global SESSION_ID
+    tools = []
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    if (not get_recommendation_count(SESSION_ID)):
+        tools.extend([moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post])
+    
+    if len(tools)!=0:
+        functions = [convert_to_openai_function(f) for f in tools]
+        llm = llm.bind(functions= functions)
+    print('llm is created')
+    print(tools)
+    system_template = """"""
+    if len(tools)!=0:
+        system_template += """ context: there is one typical conversation going on between two high school gen z girls.
+        you are one of the high school girl in the summary and you are giving suggestion to your friend in the conversation. your voice is edgy and raw. 
+        now you are going to close the conversation.
+        summary of conversation is given to you.
+        summary: {summary}
+        based on summary find any usefull app feature from tools, and must give any tool recommendation.
+        if no tool is find for the given summary,then must give any tool recommendation to the user.
+        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
+        """
+    else:
+        system_template+= """ context: there is one typical conversation going on between two high school gen z girls.
+        you are one of the high school gen z girl. your voice is edgy and raw. 
+        now you are going to close the conversation.
+        summary of conversation is given to you.
+        summary: {summary}
+        now just end the conversation in 1 sentense in short.
+        # must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
+        """
+
+
+    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:
+    """ must use For any app features details.
+
+    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}.
+    using this summary give appropriate features suggestions using tools. if you don't find any tool appropriate to summary ask question only.
+    # 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))\
+    set_recommendation_count(SESSION_ID)
+    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