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import setup
import time
import logging
from datetime import datetime
from langchain_openai import OpenAIEmbeddings
from langchain_community.embeddings import VoyageEmbeddings
from ragxplorer import RAGxplorer
import streamlit as st
# Set up the page, enable logging
from dotenv import load_dotenv,find_dotenv
load_dotenv(find_dotenv(),override=True)
logging.basicConfig(filename='app_3_visualize_data.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s', level=logging.DEBUG)
# Set the page title
st.set_page_config(
page_title='Visualize Data',
layout='wide'
)
st.title('Visualize Data')
sb=setup.load_sidebar(config_file='../config/config.json',
index_data_file='../config/index_data.json',
vector_databases=True,
embeddings=True,
index_name=True,
secret_keys=True)
secrets=setup.set_secrets(sb) # Take secrets from .env file first, otherwise from sidebar
# Set up session state variables
if 'client' not in st.session_state:
st.session_state.client = None
# Populate the main screen
logging.info(f'index_type test, {sb["index_type"]}')
if sb["index_type"]=='RAGatouille':
raise Exception('Only index type ChromaDB is supported for this function.')
elif sb["index_type"]=='Pinecone':
raise Exception('Only index type ChromaDB is supported for this function.')
elif sb['query_model']=='Openai' or 'Voyage':
logging.info('Set embeddings model for queries.')
if sb['query_model']=='Openai':
query_model=OpenAIEmbeddings(model=sb['embedding_name'],openai_api_key=secrets['OPENAI_API_KEY'])
elif sb['query_model']=='Voyage':
query_model=VoyageEmbeddings(voyage_api_key=secrets['VOYAGE_API_KEY'])
logging.info('Query model set: '+str(query_model))
st.info('You must have created a database using Document Upload in ChromaDB for this to work.')
# Add an expandable with description of what's going on.
with st.expander("Under the hood",expanded=True):
st.markdown('''
Uses modified version of https://github.com/gabrielchua/RAGxplorer/tree/main?tab=readme-ov-file to connect to existing database created.
Assumes that chroma databases are located in ../db/chroma
Query size in database: Take a random sample of this size from the database to visualize.
''')
with st.expander("Create visualization data",expanded=True):
# Add a button to run the function
vector_qty=st.number_input('Query size in database', min_value=1, step=10, value=50)
export_df = st.checkbox('Export visualization data?', value=True)
if export_df:
current_time = datetime.now().strftime("%Y.%m.%d.%H.%M")
df_export_path = st.text_input('Export file', f'../data/AMS/ams_data-400-0-{vector_qty}.json')
if st.button('Create visualization data'):
start_time = time.time() # Start the timer
st.session_state.client = RAGxplorer(embedding_model=sb['embedding_name'])
st.session_state.client.load_db(path_to_db='../db/chromadb/',index_name=sb['index_name'],
df_export_path=df_export_path,
vector_qty=vector_qty,
verbose=True)
end_time = time.time() # Stop the timer
elapsed_time = end_time - start_time
st.write(f"Elapsed Time: {elapsed_time:.2f} seconds")
with st.expander("Visualize data",expanded=True):
import_data = st.checkbox('Import visualization data?', value=True)
if import_data:
import_file = st.file_uploader("Import file", type="json")
if import_file is None:
# Use a default file
import_file_path=st.text_input('Import file',df_export_path)
else:
# Use the uploaded file
import_file_path=st.text_input('Import file',f'../data/AMS/{import_file.name}')
else:
import_file_path=None
query = st.text_input('Query', 'What are examples of lubricants which should be avoided for space mechanism applications?')
if st.button('Visualize data'):
start_time = time.time() # Start the timer
if st.session_state.client is None:
st.session_state.client = RAGxplorer(embedding_model=sb['embedding_name'])
fig = st.session_state.client.visualize_query(query,
path_to_db='../db/chromadb/', viz_data_df_path=import_file_path,
verbose=True)
st.plotly_chart(fig,use_container_width=True)
end_time = time.time() # Stop the timer
elapsed_time = end_time - start_time |