app.py

# ===========================================
# ver2-----app.py
# ===========================================



import asyncio
import os
import re
import time
import json
import torch

import logging

from api_docs_mck import api_docs_str 

import chainlit as cl

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

from langchain_openai import OpenAI

from langchain_community.llms import HuggingFaceHub
from langchain_huggingface import HuggingFacePipeline
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
#HF_INFERENCE_ENDPOINT = 
#BOOKING_ID = re.compile(r'\b[A-Z]{6}\d{6}\b')
#HUGGINGFACEHUB_API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")

BOOKING_KEYWORDS = [
    "booking",
    "bestillingsnummer",
    "bookingen",
    "ordrenummer",
    "reservation",
    "rezerwacji",
    "bookingreferanse",
    "rezerwacja",
    "booket",
    "reservation number",
    "bestilling",
    "order number",
    "booking ID",
    "identyfikacyjny płatności"
]

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. 
Your expertise is exclusively in in providing information related to a given booking ID (’bestillingsnummer’)  
and booking-related queries such as firmahytteordning and personvernspolicy. 
You do not provide information outside of this scope. If a question is not about booking or booking-related queries,
respond with, "Ønsker du annen informasjon, må du kontakte oss her 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)

# (..) If {question} contains an alphanumeric identifier consisting of 6 letters followed by 6 digits (e.g., DAGHNS116478)
api_response_template = """
With the API Documentation for Daysoff's official API: {api_docs} in mind, 
and  the specific user question: {question} in mind,
and given this API URL: {api_url} for querying,
here is the response from Daysoff's API: {api_response}.
Please provide an summary (in Norwegian) that directly addresses the user's question, 
omitting technical details like response format, and 
focusing on delivering the answer with clarity and conciseness, 
as if a human customer service agent is providing this information.
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=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):  
        booking_id = re.search(booking_pattern, user_message).group(0)  
        question = f"Retrieve information for booking ID {booking_id}"
            
        url = f"{base_url}?search={booking_id}"

        response = await api_chain.acall(
            {
                "booking_id": booking_id,
                "question": question,
                "url": url
            },
            callbacks=[cl.AsyncLangchainCallbackHandler()])

    else:
        ##await cl.Message("Vi kan desverre ikke finne noen informasjon for det oppgitte bookingnummeret.").send()

        #response = await api_chain.acall({"booking_id": booking_id}, 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