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import asyncio |
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import os |
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import re |
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import time |
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import json |
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import torch |
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from api_docs_mck import api_docs_str |
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import chainlit as cl |
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from langchain import hub |
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from langchain.chains import LLMChain, APIChain |
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from langchain_core.prompts import PromptTemplate |
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from langchain_huggingface import HuggingFaceEndpoint |
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from langchain.memory.buffer import ConversationBufferMemory |
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HUGGINGFACEHUB_API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN") |
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daysoff_assistant_booking_template = """ |
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You are a customer support assistant for Daysoff.no. Your expertise is |
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retrieving booking information for a given booking ID." |
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Chat History: {chat_history} |
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Question: {question} |
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Answer: |
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""" |
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daysoff_assistant_booking_prompt= PromptTemplate( |
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input_variables=["chat_history", "question"], |
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template=daysoff_assistant_booking_template |
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) |
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api_url_template = """ |
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Given the following API Documentation for Daysoff's official |
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booking information API: {api_docs} |
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Your task is to construct the most efficient API URL to answer |
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the user's question, ensuring the |
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call is optimized to include only the necessary information. |
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Question: {question} |
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""" |
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api_url_prompt = PromptTemplate(input_variables=['api_docs', 'question'], |
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template=api_url_template) |
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api_response_template = """ |
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With the API Documentation for Daysoff's official API: {api_docs} |
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and the specific user question: {question} in mind, |
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and given this API URL: {api_url} for querying, here is the |
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response from Daysoff's API: {api_response}. |
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Please provide user with their booking information, |
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focusing on delivering the answer with clarity and conciseness, |
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as if a human customer service agent is providing this information. |
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Adapt to user's language. By default, you speak Norwegian. |
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Booking Info: |
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""" |
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api_response_prompt = PromptTemplate( |
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input_variables=['api_docs', 'question', 'api_url', 'api_response'], |
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template=api_response_template |
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) |
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@cl.on_chat_start |
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def setup_multiple_chains(): |
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llm = HuggingFaceEndpoint( |
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repo_id="google/gemma-2-2b-it", |
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huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, |
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temperature=0.7, |
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task="text-generation" |
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) |
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conversation_memory = ConversationBufferMemory(memory_key="chat_history", |
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max_len=200, |
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return_messages=True, |
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) |
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llm_chain = LLMChain(llm=llm, |
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prompt=daysoff_assistant_booking_prompt, |
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memory=conversation_memory |
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) |
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cl.user_session.set("llm_chain", llm_chain) |
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api_chain = APIChain.from_llm_and_api_docs( |
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llm=llm, |
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api_docs=api_docs_str, |
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api_url_prompt=api_url_prompt, |
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api_response_prompt=api_response_prompt, |
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verbose=True, |
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limit_to_domains=None |
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) |
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cl.user_session.set("api_chain", api_chain) |
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BOOKING_ID = r'\b[A-Z]{6}\d{6}\b' |
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BOOKING_KEYWORDS = [ |
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"booking", |
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"bestillingsnummer", |
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"bookingen", |
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"ordrenummer", |
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"reservation", |
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"rezerwacji", |
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"bookingreferanse", |
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"rezerwacja", |
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"logg inn", |
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"booket", |
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"reservation number", |
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"bestilling", |
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"order number", |
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"booking ID", |
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"identyfikacyjny płatności" |
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] |
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@cl.on_message |
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async def handle_message(message: cl.Message): |
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user_message = message.content.lower() |
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llm_chain = cl.user_session.get("llm_chain") |
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api_chain = cl.user_session.get("api_chain") |
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is_booking_query = any( |
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re.search(keyword, user_message, re.IGNORECASE) |
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for keyword in BOOKING_KEYWORDS + [BOOKING_ID] |
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) |
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if is_booking_query: |
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response = await api_chain.acall(user_message, |
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callbacks=[cl.AsyncLangchainCallbackHandler()]) |
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else: |
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response = await llm_chain.acall(user_message, |
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callbacks=[cl.AsyncLangchainCallbackHandler()]) |
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response_key = "output" if "output" in response else "text" |
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await cl.Message(response.get(response_key, "")).send() |
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return message.content |
