app.py

# ===========================================
# ver01.01-5.workload-----app.py
# ===========================================

import asyncio
import os
import re
import time
import json

import chainlit as cl

from langchain import hub
from langchain_openai import OpenAI
from langchain.chains import LLMChain, APIChain
from langchain_core.prompts import PromptTemplate
from langchain.memory.buffer import ConversationBufferMemory

from api_docs_mck import api_docs_str 
from faq_data import help1, ansatte_faq_data, utleiere_faq_data
from personvernspolicy import help2, personvernspolicy_data

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

daysoff_assistant_template = """
You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.no
By default, you respond in Norwegian language, using a warm, direct and professional tone. 
You can provide information associated with a given booking ID, and with {help1} in mind, you
can also answer frequently asked questions (FAQ) about DaysOff firmahytteordning for 
employees: {ansatte_faq_data} and for employers:{utleiere_faq_data}.
To understand how best to answer queries about privacy policy, 
refer to {help2} and {personvernspolicy_data}
Chat History: {chat_history}
Question: {question}
Answer:
"""
daysoff_assistant_prompt = PromptTemplate(
    input_variables=['chat_history', 'question', 'help', 'ansatte_faq_data', 'utleiere_faq_data','personvernspolicy_data', 'help2'],
    template=daysoff_assistant_template
)

api_url_template = """
Given the following API Documentation for Daysoff's official
booking information API: {api_docs}
Your task is to construct the most efficient API URL to answer
the user's question, ensuring the
call is optimized to include only the necessary information.
Question: {question}
API URL:
"""
api_url_prompt = PromptTemplate(input_variables=['api_docs', 'question'],
                                template=api_url_template)

api_response_template = """
With the API Documentation for Daysoff's official API: {api_docs} in mind, 
and user question: {question} in mind,
and given this API URL: {api_url} for querying,
and if the response from Daysoff's API is information associated with 
a booking ID (’bestillingskode’): {api_response}, 
please directly address the user's question (in Norwegian) and focus on delivering the 
the response from Daysoff's API in a markdown table with clarity 
and conciseness. 
Response:
"""

api_response_prompt = PromptTemplate(
    input_variables=['api_docs', 'question', 'api_url', 'api_response'],
    template=api_response_template
)

@cl.on_chat_start
def setup_multiple_chains():

    llm = OpenAI(
        model='gpt-3.5-turbo-instruct',
        temperature=0.7, 
        openai_api_key=OPENAI_API_KEY,
        #max_tokens=512, 
        top_p=0.9,  
        frequency_penalty=0.5,
        presence_penalty=0.3   
    )
    
    conversation_memory = ConversationBufferMemory(memory_key="chat_history",
                                                   max_len=300,
                                                   return_messages=True,
                                                   )
    llm_chain = LLMChain(llm=llm,
                         prompt=daysoff_assistant_prompt,
                         memory=conversation_memory
                        )

    cl.user_session.set("llm_chain", llm_chain)

    api_chain = APIChain.from_llm_and_api_docs(
        llm=llm,
        api_docs=api_docs_str,
        api_url_prompt=api_url_prompt,
        api_response_prompt=api_response_prompt,
        verbose=True,
        limit_to_domains=None 
    )

    cl.user_session.set("api_chain", api_chain)

@cl.on_message
async def handle_message(message: cl.Message):
    user_message = message.content
    llm_chain = cl.user_session.get("llm_chain")
    api_chain = cl.user_session.get("api_chain")
    
    booking_pattern = r'\b[A-Z]{6}\d{6}\b' 
    base_url = "https://670dccd0073307b4ee447f2f.mockapi.io/daysoff/api/V1/booking"


    if re.search(booking_pattern, user_message):  
        bestillingskode = re.search(booking_pattern, user_message)#.group(0)  
        question = f"Retrieve information for booking ID {base_url}?search={bestillingskode}"
   
        response = await api_chain.acall(
            {
                "bestillingskode": bestillingskode,
                "question": question
              
            },
            callbacks=[cl.AsyncLangchainCallbackHandler()])
        
    else:

        help1 = help1 #()
        help2 = help2 #()()
        ansatte_faq_data = ansatte_faq_data
        utleiere_faq_data = utleiere_faq_data
        personvernspolicy_data = personvernspolicy_data

        # --psass required inputs to llm chain
        response = await llm_chain.acall(
            {
                #"chat_history": [],
                "question": user_message,
                "help": help1,
                "help2": help2,
                "ansatte_faq_data": ansatte_faq_data,
                "utleiere_faq_data": utleiere_faq_data,
                "personvernspolicy_data": personvernspolicy_data
            },
            callbacks=[cl.AsyncLangchainCallbackHandler()]
        )

        #response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])

    response_key = "output" if "output" in response else "text"
    await cl.Message(response.get(response_key, "")).send()
    return message.content