app.py

# ===========================================
# "the-very-latest"-----app.py ✍🏽 🎬
# ===========================================

import asyncio
import concurrent.futures
import os
import re
import time
import json

import chainlit as cl
from dotenv import load_dotenv

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

from api_docs import api_docs_str

load_dotenv()
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
auth_token = os.getenv("DAYSOFF_API_TOKEN")

daysoff_assistant_template = """
#You are a customer support assistant (’kundeservice AI assistent’) for Daysoff. 
#By default, you respond in Norwegian language, using a warm, direct, and professional tone. 
Your expertise is exclusively in retrieving booking information for a given booking ID assistance related to
to this.
You do not provide information outside of this scope. If a question is not about this topic, respond with 
"Jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon. Gjelder det andre henvendelser 
må du nok kontakte kundeservice på [email protected]😊"
Chat History: {chat_history}
Question: {question}
Answer:
"""
daysoff_assistant_prompt = PromptTemplate(
    input_variables=['chat_history', 'question'],
    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 the specific user question: {question},
and given this API URL: {api_url} for querying,
and response from Daysoff's API: {api_response},
never refer the user to the API URL as your answer!
You should always provide a clear and concise summary (in Norwegian) of the booking information retrieved.
This way you directly address the user's question in a manner that reflects the professionalism and warmth 
of a human customer service agent.
Summary:
"""
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=2048,
        top_p=0.9,
        frequency_penalty=0.1,
        presence_penalty=0.1
    )

    conversation_memory = ConversationBufferMemory(memory_key="chat_history", 
                                                   max_len=30, 
                                                   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'
    #endpoint_url = "https://aivisions.no/data/daysoff/api/v1/booking/"
    
    if re.search(booking_pattern, user_message):
        bestillingskode = re.search(booking_pattern, user_message).group(0)
        headers = {
            "Authorization": auth_token,
            #"Content-Type": "application/json"
        }
        question = f"Retrieve booking information associated with a booking ID"
        
        # --------------------------------AD VAL.III--------------------------------
        
        response = await api_chain.acall(
            {
            "headers": headers,
            "body": {
                "booking_id": bestillingskode,
            },
            "question": question,
        },
        callbacks=[cl.AsyncLangchainCallbackHandler()]
        )

        # --------------------------------------------------------------------------
    
    else:
        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