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e91d828 bfa8186 e91d828 b15cb21 e91d828 c9dbeb3 e91d828 b15cb21 e91d828 b15cb21 e91d828 b15cb21 e91d828 b15cb21 e91d828 b15cb21 e91d828 b15cb21 e91d828 1b2a100 e91d828 b15cb21 23fe795 b15cb21 e91d828 8d5b35a e91d828 57b007b c9dbeb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | import streamlit as st
from pymongo import MongoClient
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from dotenv import load_dotenv
from streamlit_option_menu import option_menu
import os
import re
from prompt import PROFESSIONAL_PROMPT
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory, ConversationBufferWindowMemory
from datetime import datetime, timezone
from handler import is_feedback_message, extract_feedback_content, OPENAI_KEY, collection, store_last_qna, add_question_ticker
# Initialize Embeddings
embeddings = OpenAIEmbeddings(
model="text-embedding-3-small",
openai_api_key=OPENAI_KEY,
dimensions=1536
)
def init_memory():
return ConversationBufferWindowMemory(memory_key="chat_history", k=3, return_messages=True, output_key='answer')
memory_store = {}
def get_user_memory(user_id: str) -> ConversationBufferWindowMemory:
if user_id not in memory_store:
memory_store[user_id] = ConversationBufferWindowMemory(
memory_key="chat_history",
k=3,
return_messages=True,
output_key="answer"
)
return memory_store[user_id]
# Vector Store Configuration
vector_store = MongoDBAtlasVectorSearch(
collection=collection,
embedding=embeddings,
index_name='vector_index',
text_key="text"
)
# Model Configuration
llm = ChatOpenAI(
model_name="gpt-4",
openai_api_key=OPENAI_KEY,
temperature=0,
max_tokens=800,
)
# @st.cache_resource
qa_chains = {}
def create_conversational_chain(user_id: None):
memory = init_memory()
print(f"🧠 Total memory messages: {len(memory.chat_memory.messages)}")
retriever = vector_store.as_retriever(
search_type="similarity",
search_kwargs={"k": 3, "score_threshold": 0.8}
)
qa_chains[user_id] = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
combine_docs_chain_kwargs={"prompt": PROFESSIONAL_PROMPT},
return_source_documents=True
)
return qa_chains[user_id]
def clean_answer(raw_answer):
formatted = re.sub(r'(\d+\.)\s', r'\n\1 ', raw_answer)
cleaned = re.sub(r'[*_]{2}', '', formatted)
reference_checked = re.sub(r'^.*Read more at(?!.*(https?://|www\.)).*$', '', cleaned, flags=re.MULTILINE)
return reference_checked.strip()
def ask(query, user_id="anonymous"):
try:
qa = create_conversational_chain("anonymous")
add_question_ticker(query)
result = qa({"question": query})
usage = result.get('__raw', {}).get('usage', {})
print(f"🔍🔍🔍🔍🔍🔍 Tokens used: {usage}")
if not result['source_documents']:
return f"Sorry, no relevant information found on the question asked. Please contact immigration customer service through https://www.imigrasi.go.id/hubungi."
else:
answer = clean_answer(result["answer"])
store_last_qna(user_id, query, answer)
return answer
except Exception as e:
error_msg = f"""
<div class="assistant-message">
⚠️ An error occured<br>
An error occured, please try again or contact us:<br>
• Email: galuh.adika@gmail.com<br>
error: {str(e)}<br>
</div>
"""
return error_msg
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