import json import time 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 tools import MEMORY, set_chatbot_name, close_chat, recommand_podcast,app_features,joke_teller,SESSION_ID from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data def get_mood_summary(user_id): data = get_mood_data(user_id) 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=0.0) return llm.invoke(system_prompt.format(data=data)).content 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_last_session_summary(last_session_id, second_last_session_id): conversation = get_last_conversion(last_session_id,second_last_session_id) if conversation: system_prompt = """ 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. must use I for AI named BMOXI and for human use my friend. in summary. this is your conversation with your best friend. summerize whole conversation and if you found any question can be asked from chat then return summary and qustion else return summary and append None at last. conversation: {conversation} summary: """ llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.0) response = llm.invoke(system_prompt.format(conversation=conversation)).content return response else: return "" def create_agent(user_id,is_first = False): # print("get user Id**********************",user_id) previous_session_id = get_last_session(user_id) # print(previous_session_id) tools = [set_chatbot_name,close_chat,recommand_podcast,app_features,joke_teller] 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=0.7).bind(functions=functions) chat_bot_name = get_chat_bot_name(user_id) extra_prompt = "" previous_problem_summary = None if is_first: start = time.time() mood_summary = get_mood_summary(user_id) print(previous_session_id) if previous_session_id['second_last_session_id']: previous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id']) print("time require for mood summary: ",time.time()-start) if previous_problem_summary.find('None') == -1: extra_prompt = f"""ask user her previous problem is solved or not.use previous problem summary for framming the question. must include her name which is {user_id} .nothing else.""" else: extra_prompt = f""" Only use this template to start conversation nothing else also not forgot to add {user_id} in question:- hey again {user_id}! What's up today? Need ✨ Advice, ✨ a Mood Boost, ✨ a Chat, ✨ Resource Suggestions, ✨ App Features help? How can I help?" """ prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood="", previous_summary=previous_problem_summary)+extra_prompt), 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") global MEMORY,SESSION_ID MEMORY = memory SESSION_ID = previous_session_id['last_session_id'] 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