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from prompts.arxiv_prompt import combine_prompt_template, _myscale_prompt
from callbacks.arxiv_callbacks import ChatDataSelfSearchCallBackHandler, \
ChatDataSelfAskCallBackHandler, ChatDataSQLSearchCallBackHandler, \
ChatDataSQLAskCallBackHandler
from chains.arxiv_chains import ArXivQAwithSourcesChain, ArXivStuffDocumentChain
from chains.arxiv_chains import VectorSQLRetrieveCustomOutputParser
from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_experimental.retrievers.vector_sql_database import VectorSQLDatabaseChainRetriever
from langchain.utilities.sql_database import SQLDatabase
from langchain.chains import LLMChain
from sqlalchemy import create_engine, MetaData
from langchain.prompts import PromptTemplate, ChatPromptTemplate, \
SystemMessagePromptTemplate, HumanMessagePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain import OpenAI
import re
import pandas as pd
from os import environ
import streamlit as st
import datetime
from helper import build_all, sel_map, display
environ['OPENAI_API_BASE'] = st.secrets['OPENAI_API_BASE']
st.set_page_config(page_title="ChatData")
st.header("ChatData")
if 'retriever' not in st.session_state:
st.session_state["sel_map_obj"] = build_all()
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, 7])
cols[0].button("Query", key='search_sql')
cols[1].button("Ask", key='ask_sql')
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, 7])
cols[0].button("Query", key='search_self')
cols[1].button("Ask", key='ask_self')
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
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