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import json
import time
import pandas as pd
from os import environ
import datetime
import streamlit as st
from langchain.schema import Document
from callbacks.arxiv_callbacks import ChatDataSelfSearchCallBackHandler, \
ChatDataSelfAskCallBackHandler, ChatDataSQLSearchCallBackHandler, \
ChatDataSQLAskCallBackHandler
from langchain.schema import BaseMessage, HumanMessage, AIMessage, FunctionMessage, SystemMessage
from auth0_component import login_button
from helper import build_tools, build_agents, build_all, sel_map, display
environ['OPENAI_API_BASE'] = st.secrets['OPENAI_API_BASE']
st.set_page_config(page_title="ChatData", page_icon="https://myscale.com/favicon.ico")
st.header("ChatData")
if 'retriever' not in st.session_state:
st.session_state["sel_map_obj"] = build_all()
st.session_state["tools"] = build_tools()
def on_chat_submit():
ret = st.session_state.agents[st.session_state.sel][st.session_state.ret_type]({"input": st.session_state.chat_input})
print(ret)
def clear_history():
st.session_state.agents[st.session_state.sel][st.session_state.ret_type].memory.clear()
AUTH0_CLIENT_ID = st.secrets['AUTH0_CLIENT_ID']
AUTH0_DOMAIN = st.secrets['AUTH0_DOMAIN']
def login():
if "user_name" in st.session_state or ("jump_query_ask" in st.session_state and st.session_state.jump_query_ask):
return True
st.subheader("🤗 Welcom to [MyScale](https://myscale.com)'s [ChatData](https://github.com/myscale/ChatData)! 🤗 ")
st.write("You can now chat with ArXiv and Wikipedia! 🌟\n")
st.write("Built purely with streamlit 👑 , LangChain 🦜🔗 and love ❤️ for AI!")
st.write("Follow us on [Twitter](https://x.com/myscaledb) and [Discord](https://discord.gg/D2qpkqc4Jq)!")
st.write("For more details, please refer to [our repository on GitHub](https://github.com/myscale/ChatData)!")
st.divider()
col1, col2 = st.columns(2, gap='large')
with col1.container():
st.write("Try out MyScale's Self-query and Vector SQL retrievers!")
st.write("In this demo, you will be able to see how those retrievers "
"**digest** -> **translate** -> **retrieve** -> **answer** to your question!")
st.session_state["jump_query_ask"] = st.button("Query / Ask")
with col2.container():
# st.warning("To use chat, please jump to [https://myscale-chatdata.hf.space](https://myscale-chatdata.hf.space)")
st.write("Now with the power of LangChain's Conversantional Agents, we are able to build "
"an RAG-enabled chatbot within one MyScale instance! ")
st.write("Log in to Chat with RAG!")
login_button(AUTH0_CLIENT_ID, AUTH0_DOMAIN, "auth0")
st.divider()
st.write("- [Privacy Policy](https://myscale.com/privacy/)\n"
"- [Terms of Sevice](https://myscale.com/terms/)")
if st.session_state.auth0 is not None:
st.session_state.user_info = dict(st.session_state.auth0)
if 'email' in st.session_state.user_info:
email = st.session_state.user_info["email"]
else:
email = f"{st.session_state.user_info['nickname']}@{st.session_state.user_info['sub']}"
st.session_state["user_name"] = email
del st.session_state.auth0
st.experimental_rerun()
if st.session_state.jump_query_ask:
st.experimental_rerun()
def back_to_main():
if "user_info" in st.session_state:
del st.session_state.user_info
if "user_name" in st.session_state:
del st.session_state.user_name
if "jump_query_ask" in st.session_state:
del st.session_state.jump_query_ask
if login():
if "user_name" in st.session_state:
st.session_state["agents"] = build_agents(st.session_state.user_name)
with st.sidebar:
st.radio("Retriever Type", ["Self-querying retriever", "Vector SQL"], key="ret_type")
st.selectbox("Knowledge Base", ["ArXiv Papers", "Wikipedia", "ArXiv + Wikipedia"], key="sel")
st.button("Clear Chat History", on_click=clear_history)
st.button("Logout", on_click=back_to_main)
for msg in st.session_state.agents[st.session_state.sel][st.session_state.ret_type].memory.chat_memory.messages:
speaker = "user" if isinstance(msg, HumanMessage) else "assistant"
if isinstance(msg, FunctionMessage):
with st.chat_message("Knowledge Base", avatar="📖"):
print(type(msg.content))
st.write(f"*{datetime.datetime.fromtimestamp(msg.additional_kwargs['timestamp']).isoformat()}*")
st.write("Retrieved from knowledge base:")
try:
st.dataframe(pd.DataFrame.from_records(map(dict, eval(msg.content))))
except:
st.write(msg.content)
else:
if len(msg.content) > 0:
with st.chat_message(speaker):
print(type(msg), msg.dict())
st.write(f"*{datetime.datetime.fromtimestamp(msg.additional_kwargs['timestamp']).isoformat()}*")
st.write(f"{msg.content}")
st.chat_input("Input Message", on_submit=on_chat_submit, key="chat_input")
elif "jump_query_ask" in st.session_state and st.session_state.jump_query_ask:
sel = st.selectbox('Choose the knowledge base you want to ask with:',
options=['ArXiv Papers', 'Wikipedia'])
sel_map[sel]['hint']()
tab_sql, tab_self_query = st.tabs(['Vector SQL', 'Self-Query Retrievers'])
with tab_sql:
sel_map[sel]['hint_sql']()
st.text_input("Ask a question:", key='query_sql')
cols = st.columns([1, 1, 1, 4])
cols[0].button("Query", key='search_sql')
cols[1].button("Ask", key='ask_sql')
cols[2].button("Back", key='back_sql', on_click=back_to_main)
plc_hldr = st.empty()
if st.session_state.search_sql:
plc_hldr = st.empty()
print(st.session_state.query_sql)
with plc_hldr.expander('Query Log', expanded=True):
callback = ChatDataSQLSearchCallBackHandler()
try:
docs = st.session_state.sel_map_obj[sel]["sql_retriever"].get_relevant_documents(
st.session_state.query_sql, callbacks=[callback])
callback.progress_bar.progress(value=1.0, text="Done!")
docs = pd.DataFrame(
[{**d.metadata, 'abstract': d.page_content} for d in docs])
display(docs)
except Exception as e:
st.write('Oops 😵 Something bad happened...')
raise e
if st.session_state.ask_sql:
plc_hldr = st.empty()
print(st.session_state.query_sql)
with plc_hldr.expander('Chat Log', expanded=True):
callback = ChatDataSQLAskCallBackHandler()
try:
ret = st.session_state.sel_map_obj[sel]["sql_chain"](
st.session_state.query_sql, callbacks=[callback])
callback.progress_bar.progress(value=1.0, text="Done!")
st.markdown(
f"### Answer from LLM\n{ret['answer']}\n### References")
docs = ret['sources']
docs = pd.DataFrame(
[{**d.metadata, 'abstract': d.page_content} for d in docs])
display(
docs, ['ref_id'] + sel_map[sel]["must_have_cols"], index='ref_id')
except Exception as e:
st.write('Oops 😵 Something bad happened...')
raise e
with tab_self_query:
st.info("You can retrieve papers with button `Query` or ask questions based on retrieved papers with button `Ask`.", icon='💡')
st.dataframe(st.session_state.sel_map_obj[sel]["metadata_columns"])
st.text_input("Ask a question:", key='query_self')
cols = st.columns([1, 1, 1, 4])
cols[0].button("Query", key='search_self')
cols[1].button("Ask", key='ask_self')
cols[2].button("Back", key='back_self', on_click=back_to_main)
plc_hldr = st.empty()
if st.session_state.search_self:
plc_hldr = st.empty()
print(st.session_state.query_self)
with plc_hldr.expander('Query Log', expanded=True):
call_back = None
callback = ChatDataSelfSearchCallBackHandler()
try:
docs = st.session_state.sel_map_obj[sel]["retriever"].get_relevant_documents(
st.session_state.query_self, callbacks=[callback])
print(docs)
callback.progress_bar.progress(value=1.0, text="Done!")
docs = pd.DataFrame(
[{**d.metadata, 'abstract': d.page_content} for d in docs])
display(docs, sel_map[sel]["must_have_cols"])
except Exception as e:
st.write('Oops 😵 Something bad happened...')
raise e
if st.session_state.ask_self:
plc_hldr = st.empty()
print(st.session_state.query_self)
with plc_hldr.expander('Chat Log', expanded=True):
call_back = None
callback = ChatDataSelfAskCallBackHandler()
try:
ret = st.session_state.sel_map_obj[sel]["chain"](
st.session_state.query_self, callbacks=[callback])
callback.progress_bar.progress(value=1.0, text="Done!")
st.markdown(
f"### Answer from LLM\n{ret['answer']}\n### References")
docs = ret['sources']
docs = pd.DataFrame(
[{**d.metadata, 'abstract': d.page_content} for d in docs])
display(
docs, ['ref_id'] + sel_map[sel]["must_have_cols"], index='ref_id')
except Exception as e:
st.write('Oops 😵 Something bad happened...')
raise e |