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Update app.py
Browse files
app.py
CHANGED
@@ -1,14 +1,24 @@
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# ===========================================
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#
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# ===========================================
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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 pandas as pd
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import chainlit as cl
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@@ -23,25 +33,34 @@ from langchain.memory import ConversationTokenBufferMemory
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from langchain.memory import ConversationSummaryMemory
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from api_docs_mck import api_docs_str
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
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daysoff_assistant_template = """
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You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.
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By default, you respond in Norwegian language, using a warm, direct, and professional tone.
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Your expertise is exclusively in retrieving booking information for a given booking
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If a question
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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_prompt = PromptTemplate(
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input_variables=['chat_history', 'question'],
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template=daysoff_assistant_template
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)
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@@ -57,6 +76,7 @@ API URL:
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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} in mind,
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conversation_memory = ConversationBufferMemory(memory_key="chat_history",
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max_len=30, # --retains only the last 30 exchanges
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return_messages=True,
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)
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# --ConversationTokenBufferMemory
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#conversation_memory = ConversationTokenBufferMemory(memory_key="chat_history",
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#max_token_limit=1318,
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#return_messages=True,
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#)
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# --ConversationSummaryMemory
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#conversation_memory = ConversationSummaryMemory(memory_key="chat_history",
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#return_messages=True,
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@@ -125,6 +147,10 @@ def setup_multiple_chains():
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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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cl.user_session.set("api_chain", api_chain)
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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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# --regex
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booking_pattern = r'\b[A-Z]{6}\d{6}\b'
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# --endpoint
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endpoint_url = "https://670dccd0073307b4ee447f2f.mockapi.io/daysoff/api/V1/booking"
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# --dataframe
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personvernspolicy_df = pd.read_csv('personvernspolicy.csv')
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faq_df = pd.read_csv('faq.csv')
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# --dictionaries
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personvernspolicy_qa = personvernspolicy_df.to_dict(orient='records')
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faq_qa = faq_df.to_dict(orient='records')
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# --keywords
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personvernspolicy_keywords = personvernspolicy_df['question'].tolist()
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faq_keywords = faq_df['question'].tolist()
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all_keywords = personvernspolicy_keywords + faq_keywords
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if re.search(booking_pattern, user_message):
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bestillingskode = re.search(booking_pattern, user_message).group(0)
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question = f"Retrieve information for booking ID {endpoint_url}?search={bestillingskode}"
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response = await api_chain.acall(
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{
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"bestillingskode": bestillingskode,
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"question": question
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},
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callbacks=[cl.AsyncLangchainCallbackHandler()]
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elif any(keyword.lower() in user_message.lower() for keyword in all_keywords):
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matched_entry = None
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for qa_entry in personvernspolicy_qa + faq_qa:
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if qa_entry['question'].lower() in user_message.lower():
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matched_entry = qa_entry
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break
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if matched_entry:
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question = f"User Question: {user_message}\nMatched FAQ:\n{matched_entry}"
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response = await llm_chain.acall(
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{
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"question": question
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},
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callbacks=[cl.AsyncLangchainCallbackHandler()]
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)
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else:
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response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])
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else:
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response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])
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# ===========================================
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# ver01.01-5.workload-----app.py
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# ===========================================
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#You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.
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#By default, you respond in Norwegian language, using a warm, direct, and professional tone.
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#Your expertise is exclusively in retrieving booking information for a given booking ID assistance related to
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#to this.
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#You do not provide information outside of this scope. If a question is not about this topic, respond with
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#"Ooops da, jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon. Gjelder det andre henvendelser,
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#må du nok kontakte kundeservice på [email protected]😊"
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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 chainlit as cl
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from langchain.memory import ConversationSummaryMemory
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from api_docs_mck import api_docs_str
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from frequently_asked_questions import instruction_text, frequently_asked_questions
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from personvernspolicy import instruction_text_priv, personvernspolicy_data
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from frequently_asked_questions import instruction_text_faq, faq
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
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# If you don't know the answer, just say that you don't know, don't try to make up an answer.
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daysoff_assistant_template = """
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You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.
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By default, you respond in Norwegian language, using a warm, direct, and professional tone.
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Your expertise is exclusively in retrieving booking information for a given booking id and answering
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questions about firmahytteorning and personvernspolicy.
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If a question does not involve booking information for a given booking id, ask: "Gjelder spørsmålet firmahytteordning?",
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upon user confirmation, do your best to try to answer accordingly by referring to {instruction_text_faq} and {faq}.
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If a query does not involve booking information for a given booking id or firmahytteordning, ask: "Gjelder spørsmålet personvernspolicy?"
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upon user confirmation, do your best to provide a precise privacy-related response by referring to: {instruction_text_priv} and {personvernspolicy_data}.
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If the query does not involve booking information for a given booking id, firmahytteordning or personvernspolicy,
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respond with: "Jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon og ofte-stilte-spørsmål i forbindelse
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med DaysOff firmahytteordning (inkludert personvernspolicyn). Gjelder det andre henvendelser, må du nok kontakte kundeservice på [email protected]😊"
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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_prompt = PromptTemplate(
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input_variables=['chat_history', 'question', "instruction_text_faq", "faq", "instruction_text_priv", "personvernspolicy_data"],
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template=daysoff_assistant_template
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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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# If the response includes booking information, provide the information verbatim (do not summarize it.)
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api_response_template = """
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With the API Documentation for Daysoff's official API: {api_docs} in mind,
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conversation_memory = ConversationBufferMemory(memory_key="chat_history",
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max_len=30, # --retains only the last 30 exchanges
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return_messages=True,
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)
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# --ConversationTokenBufferMemory
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#conversation_memory = ConversationTokenBufferMemory(memory_key="chat_history",
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#max_token_limit=1318,
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#return_messages=True,
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#)
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# --ConversationSummaryMemory
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#conversation_memory = ConversationSummaryMemory(memory_key="chat_history",
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#return_messages=True,
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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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instruction_text_faq=instruction_text_faq,
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faq=faq,
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instruction_text_priv=instruction_text_priv,
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personvernspolicy_data=personvernspolicy_data,
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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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cl.user_session.set("api_chain", api_chain)
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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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booking_pattern = r'\b[A-Z]{6}\d{6}\b'
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endpoint_url = "https://670dccd0073307b4ee447f2f.mockapi.io/daysoff/api/V1/booking"
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if re.search(booking_pattern, user_message):
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bestillingskode = re.search(booking_pattern, user_message).group(0)
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question = f"Retrieve information for booking ID {endpoint_url}?search={bestillingskode}"
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response = await api_chain.acall(
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{
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"bestillingskode": bestillingskode,
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"question": question
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},
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callbacks=[cl.AsyncLangchainCallbackHandler()])
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else:
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response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])
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