app.py

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

def print_scripts(file_path):
    try:
        with open(file_path, 'r') as file:
            print(f"\n--- Contents of {file_path} ---\n")
            print(file.read())
            print(f"--- End of {file_path} ---\n")
    except Exception as e:
        print(f"Could not read {file_path}: {e}")


if __name__ == "__main__":
    
    file_1 = "/home/user/.local/lib/python3.10/site-packages/chainlit/socket.py"
    file_2 = "/home/user/.local/lib/python3.10/site-packages/socketio/async_server.py"

    print_scripts(file_1)
    print_scripts(file_2)

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_mck import api_docs_str 
from api_docs import api_docs_str, auth_token

load_dotenv()
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
auth = os.environ.get("CHAINLIT_AUTH_SECRET")

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
)

#async def on_chat_start():
    #app_user = cl.user_session.get("user")
    #user_env = cl.user_session.get("env")
    #await cl.Message(f"Hello {app_user.identifier}").send()

#@cl.on_chat_start
#async def on_chat_start():
    #app_user = cl.user_session.get("user")
    #await cl.Message(f"Hello {app_user}").send()
    
@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 #["http://0.0.0.0:7860/"]
    )

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


"""
@cl.on_message
async def handle_message(message: cl.Message):
    user_message = message.content #.lower()
    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/"

    
    auth_token = f"Bearer {auth_token}

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


"""
    # --POST method, booking_id@body
    if re.search(booking_pattern, user_message):  
        bestillingskode = re.search(booking_pattern, user_message).group(0)  
        question = f"Retrieve information for booking ID {bestillingskode}"

        response = await api_chain.acall(
            {
                "url": endpoint_url,  
                "method": "POST",     
                "headers": {
                    "Authorization": f"Bearer {auth_token}",
                    "Content-Type": "application/json"
                },
                "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

    #endpoint_url = "https://670dccd0073307b4ee447f2f.mockapi.io/daysoff/api/V1/booking"
    
    
    
"""
@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/"
    #auth_token = f"Bearer {auth_token}


    if re.search(booking_pattern, user_message):
        bestillingskode = re.search(booking_pattern, user_message).group(0)
        question = f"Retrieve information for booking ID {bestillingskode}"

        response = await api_chain.acall(                           # ~ainvoke
            {
                "url": endpoint_url,
                "method": "POST",
                "headers": {
                    "Authorization": f"Bearer {auth_token}",
                    "Content-Type": "application/json",
                },
                "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

    # --concurrent execution@ThreadPoolExecutor, resolve the ’Could not reach the server' error?
    #loop = asyncio.get_running_loop()
    #with concurrent.futures.ThreadPoolExecutor() as pool:
        #await loop.run_in_executor(pool, lambda: asyncio.run(process_message()))