Spaces:
Sleeping
Sleeping
File size: 3,990 Bytes
40d6ead 6e1e8e1 40d6ead |
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 |
import streamlit as st
from ragchatbot import RAGChatBot
from pydantic_models import RequestModel, ChatHistoryItem
def validate_chat_history_item(chat_history_item: ChatHistoryItem):
return ChatHistoryItem.model_validate(chat_history_item.model_dump())
st.set_page_config(page_title="RAG-Chatbot", page_icon=":mag:", layout="wide")
st.title("Test Formatted Text - KCS10-31")
col1, col2 = st.columns(2)
col1.title("Current Model")
col2.title("Formatted Text")
if "formatted_ragchatbot" not in st.session_state:
st.session_state.formatted_ragchatbot = RAGChatBot(vectorstore_path="KCS_formatted_vectorstore")
if "just_ragchatbot" not in st.session_state:
st.session_state.just_ragchatbot = RAGChatBot(vectorstore_path="KCS_current_vectorstore")
if "formatted_chat_history" not in st.session_state:
st.session_state.formatted_chat_history = []
if "just_chat_history" not in st.session_state:
st.session_state.just_chat_history = []
for chat_index in range(0,len(st.session_state.formatted_chat_history)):
assert len(st.session_state.formatted_chat_history) == len(st.session_state.just_chat_history)
for col, chat_history, sources_text in zip(st.columns(2, vertical_alignment="top"), [st.session_state.just_chat_history, st.session_state.formatted_chat_history], ["Current Model", "Formatted Text"]):
chat = chat_history[chat_index]
with col.chat_message("user"):
st.write(chat.get("user_message").replace("\n","\n\n"))
with col.chat_message("assistant"):
st.write(chat.get("assistant_message").replace("\n","\n\n"))
st.write(chat.get("search_phrase"))
for i, doc in enumerate(chat.get("sources_documents")):
with st.expander(f"{sources_text} Sources - {i+1}"):
st.subheader(f"{doc.get('heading')} - {doc.get('relevance_score')}")
if sources_text == "Contextual Chunking":
st.write(doc.get("page_content").replace("\n","\n\n").split("<chunk_content>")[1].split("</chunk_content>")[0])
else:
st.write(doc.get("page_content").replace("\n","\n\n"))
# print_session_state_variables()
if user_query := st.chat_input("Enter your query"):
for col in st.columns(2, vertical_alignment="top"):
with col.chat_message("user"):
st.write(user_query.replace("\n","\n\n"))
with st.spinner("Generating response..."):
just_response = st.session_state.just_ragchatbot.get_response(
RequestModel(user_question=user_query, chat_history=[ChatHistoryItem(user_message=chat.get("user_message"), assistant_message=chat.get("assistant_message")) for chat in st.session_state.just_chat_history])
)
sources_documents = [{"heading":doc.heading, "page_content":doc.page_content, "relevance_score":doc.relevance_score} for doc in just_response.sources_documents]
st.session_state.just_chat_history.append({
"user_message": user_query,
"assistant_message": just_response.answer,
"search_phrase": just_response.search_phrase,
"sources_documents": sources_documents
})
formatted_response = st.session_state.formatted_ragchatbot.get_response(
RequestModel(user_question=user_query, chat_history=[ChatHistoryItem(user_message=chat.get("user_message"), assistant_message=chat.get("assistant_message")) for chat in st.session_state.formatted_chat_history])
)
sources_documents = [{"heading":doc.heading, "page_content":doc.page_content, "relevance_score":doc.relevance_score} for doc in formatted_response.sources_documents]
st.session_state.formatted_chat_history.append({
"user_message": user_query,
"assistant_message": formatted_response.answer,
"search_phrase": formatted_response.search_phrase,
"sources_documents": sources_documents
})
st.rerun() |