utils.py

import json
from transformers import AutoTokenizer, AutoModel
from langchain_community.chat_models import ChatOpenAI
import pandas as pd
from config import settings
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferWindowMemory
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_mongodb.chat_message_histories import MongoDBChatMessageHistory

from tools import moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name


def get_last_session(user_id="user_1"):
    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
        settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)

    sessions_cursor = mongodb_chatbot_message_collection.find_one(
        {"user_id": user_id})

    print(sessions_cursor)
    sessions_list = sessions_cursor['session_id']

    second_last_session_id = None
    if len(sessions_list) >= 2:
        second_last_session_id = sessions_list[-2]

    return {"last_session_id": sessions_list[-1], "second_last_session_id": second_last_session_id if second_last_session_id else None}


def get_mood_summary(data='''"35","27","mood_tracker","[{""question_id"":1,""question"":""my vibe rn is\u2026"",""answer"":[""Sad""],""time"":""5:12 PM""},{""question_id"":2,""question"":""I feel this way bc of\u2026 "",""answer"":[""SCHOOL""],""time"":""5:12 PM""}]","2022-11-02 17:12:42","2024-03-28 07:27:13"'''):
    system_prompt = """You are an descripting assistant that provides the breif description of the user data which is related to their mood tracking activity. Properly descibe the reason for their mood.Avoid times and dates in description
                     Here is the user data: {data}"""

    llm = ChatOpenAI(model=settings.OPENAI_MODEL,
                     openai_api_key=settings.OPENAI_KEY, temperature=0.7)
    return llm.invoke(system_prompt.format(data=data)).content


def get_chat_history(session_id="bmoxinew"):
    # Set up MongoDB for storing chat history
    chat_history = MongoDBChatMessageHistory(
        connection_string=settings.MONGODB_CONNECTION_STRING,
        database_name=settings.MONGODB_DB_NAME,  # Specify the database name here
        collection_name=settings.MONGODB_DB_CHAT_COLLECTION_NAME,
        session_id=session_id,
    )

    return chat_history


def deanonymizer(input, anonymizer):
    input = anonymizer.deanonymize(input)
    map = anonymizer.deanonymizer_mapping
    if map:
        for k in map["PERSON"]:
            names = k.split(" ")
            for i in names:
                input = input.replace(i, map["PERSON"][k])
    return input


def get_chat_bot_name(user_id="user_1"):
    print(settings.MONGODB_CONNECTION_STRING)
    print(settings.mongodb_chatbot_name_collection)
    result = settings.mongodb_chatbot_name_collection.find_one(
        {"user_id": user_id})

    print("CHATBOT RESULT", result, type(result))
    if result:
        print(result)
        return result['chat_bot_name']
    return settings.CHATBOT_NAME


def get_last_session_summary(last_session_id, second_last_session_id):

    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
        settings.MONGODB_DB_CHAT_COLLECTION_NAME)

    collection_count = mongodb_chatbot_message_collection.count_documents({"SessionId": last_session_id})
    print("******************************** data********************888")
    print(collection_count)
    print(last_session_id)
    print("*********************************")
    if collection_count <=2:
        sessions_cursor = mongodb_chatbot_message_collection.find({"SessionId": second_last_session_id})  # Sort by timestamp descending and limit to 2 results

        print(sessions_cursor)
        sessions_list = list(sessions_cursor)
        print(sessions_list)

        conversation = """"""
        for document in sessions_list:
            print("MY document")
            print(document)
            if "History" in document:
                history = json.loads(document['History'])
                print(history)
                print(history['type'])
                print(history['data'])
                print(history['data']['content'])
                conversation += f"""{history['type']}: {history['data']['content']}\n"""

        print(conversation)
        system_prompt = """You are an descripting assistant that provides that analyze user conversation with AI bot and gives problem user was facing and weather that problem was solved or not and give response in below format.
      
      conversation: {conversation}
      
      problem: 
      is_problem_solved: YES/NO
      """

        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
                         openai_api_key=settings.OPENAI_KEY, temperature=0.7)

        response = llm.invoke(system_prompt.format(conversation=conversation)).content
        print("********************************* PREVIOUS PROBLEM *******************************************")
        print(response)
        return response

    else:
        return ""

def set_chat_bot_name(name, user_id):
    # Insert document into collection
    insert_result = settings.mongodb_chatbot_name_collection.update_one({"user_id": user_id}, { "$set": { "chat_bot_name": name } },  upsert=True)
    print("done successfully...")
    return name

def create_agent(user_id):
    print("get user Id**********************",user_id)
    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name]
    # tools = [moxicast]

    functions = [convert_to_openai_function(f) for f in tools]
    model = ChatOpenAI(model_name=settings.OPENAI_MODEL,
                       openai_api_key=settings.OPENAI_KEY, frequency_penalty= 1, temperature=settings.TEMPERATURE).bind(functions=functions)

    chat_bot_name = get_chat_bot_name(user_id)

    print("CHABT NAME", chat_bot_name)
    mood_summary = get_mood_summary()

    previous_session_id = get_last_session(user_id)
    print(previous_session_id)
    prevous_problem_summary = None
    if previous_session_id['second_last_session_id']:
        prevous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])

    print("**************************************** SUMMARY ***********************************************")
    print(prevous_problem_summary)

    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood=mood_summary, previous_summary=prevous_problem_summary)),
                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
                                               MessagesPlaceholder(variable_name="agent_scratchpad")])



    memory = ConversationBufferWindowMemory(memory_key="chat_history", chat_memory=get_chat_history(
        previous_session_id['last_session_id']), return_messages=True, k=5)

    print("memory created")

    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()

    agent_executor = AgentExecutor(
        agent=chain, tools=tools, memory=memory, verbose=True)

    return agent_executor