import re import numpy as np import openai import streamlit_scrollable_textbox as stx import pinecone import streamlit as st st.set_page_config(layout="wide") # isort: split from utils import nltkmodules from utils.entity_extraction import ( extract_entities_docs, year_quarter_range, clean_companies, ticker_year_quarter_tuples_creator, extract_entities_keywords, clean_keywords_all_combs, ) from utils.models import ( get_alpaca_model, get_vicuna_ner_1_model, get_vicuna_ner_2_model, get_vicuna_text_gen_model, get_data, get_instructor_embedding_model_api, gpt_turbo_model, vicuna_text_generate, save_key, ) from utils.prompts import ( generate_prompt_alpaca_style, generate_multi_doc_context, ) from utils.retriever import ( query_pinecone, sentence_id_combine, get_indices_bm25, ) from utils.transcript_retrieval import retrieve_transcript from utils.vector_index import create_dense_embeddings st.title("Question Answering on Earnings Call Transcripts") st.write( "The app uses the quarterly earnings call transcripts for 10 companies (Apple, AMD, Amazon, Cisco, Google, Microsoft, Nvidia, ASML, Intel, Micron) for the years 2016 to 2020." ) # Caching Resources and Model APIs data = get_data() alpaca_model = get_alpaca_model() vicuna_ner_1_model = get_vicuna_ner_1_model() vicuna_ner_2_model = get_vicuna_ner_2_model() vicuna_text_gen_model = get_vicuna_text_gen_model() # Sidebar Options decoder_models_choice = ["GPT-3.5 Turbo", "Vicuna-7B"] with st.sidebar: st.subheader("Select Options:") num_results = int( st.number_input("Number of Results to query", 1, 15, value=4) ) window = int(st.number_input("Sentence Window Size", 0, 10, value=1)) threshold = float( st.number_input( label="Similarity Score Threshold", step=0.05, format="%.2f", value=0.6, ) ) use_bm25 = st.checkbox("Use 2-Stage Retrieval (BM25)") num_candidates = int( st.number_input( "Number of Candidates to Generate:", 25, 200, step=25, value=50, ) ) decoder_model = st.selectbox( "Select Text Generation Model", decoder_models_choice ) col1, col2 = st.columns([3, 3], gap="medium") with col1: query_text = st.text_area( "Input Query", value="What was discussed regarding Wearables revenue performance in Q1 2020?", ) # Extracting Document Entities from Question ( companies, start_quarter, start_year, end_quarter, end_year, ) = extract_entities_docs(query_text, vicuna_ner_1_model) year_quarter_range_list = year_quarter_range( start_quarter, start_year, end_quarter, end_year ) ticker_list = clean_companies(companies) ticker_year_quarter_tuples_list = ticker_year_quarter_tuples_creator( ticker_list, year_quarter_range_list ) # Extract keywords from query all_keywords = extract_entities_keywords(query_text, vicuna_ner_2_model) if all_keywords != []: keywords = clean_keywords_all_combs(all_keywords) else: keywords = None # Connect to PineCone Vector Database - Instructor Model pinecone.init( api_key=st.secrets["pinecone_instructor"], environment="us-west4-gcp-free", ) pinecone_index_name = "week13-instructor-xl" pinecone_index = pinecone.Index(pinecone_index_name) retriever_model = get_instructor_embedding_model_api() instruction = ( "Represent the financial question for retrieving supporting documents:" ) dense_query_embedding = create_dense_embeddings( query_text, retriever_model, instruction ) context_group = [] if ticker_year_quarter_tuples_list != []: for ticker, quarter, year in ticker_year_quarter_tuples_list: if use_bm25 == True: indices = get_indices_bm25( data, ticker, quarter, year, num_candidates ) else: indices = None query_results = query_pinecone( dense_query_embedding, num_results, pinecone_index, year, quarter, ticker, keywords, indices, threshold, ) context = sentence_id_combine(data, query_results, lag=window) context_group.append((context, year, quarter, ticker)) multi_doc_context = generate_multi_doc_context(context_group) else: indices = None query_results = query_pinecone( dense_query_embedding, num_results, pinecone_index, None, None, None, keywords, indices, threshold, ) multi_doc_context = sentence_id_combine(data, query_results, lag=window) prompt = generate_prompt_alpaca_style(query_text, multi_doc_context) with col1: edited_prompt = st.text_area( label="Model Prompt", value=prompt, height=400 ) if decoder_model == "GPT-3.5 Turbo": with col2: with st.form("gpt_form"): openai_key = st.text_input( "Enter OpenAI key", value="", type="password", ) gpt_submitted = st.form_submit_button("Submit") if gpt_submitted: api_key = save_key(openai_key) openai.api_key = api_key generated_text = gpt_turbo_model(edited_prompt) st.subheader("Answer:") regex_pattern_sentences = "(?
  • {answer_text}

  • ", unsafe_allow_html=True, ) if decoder_model == "Vicuna-7B": with col2: st.write("The Vicuna Model is running: ...") st.write("The model takes 10-15 mins to generate the text.") generated_text = vicuna_text_generate(prompt, vicuna_text_gen_model) st.subheader("Answer:") regex_pattern_sentences = "(?
  • {answer_text}

  • ", unsafe_allow_html=True, ) tab1, tab2 = st.tabs(["Retrieved Text", "Retrieved Documents"]) with tab1: with st.expander("See Retrieved Text"): st.subheader("Retrieved Text:") st.write( f"

    {multi_doc_context}

    ", unsafe_allow_html=True, ) with tab2: if ticker_year_quarter_tuples_list != []: for ticker, quarter, year in ticker_year_quarter_tuples_list: file_text = retrieve_transcript(data, year, quarter, ticker) with st.expander(f"See Transcript - {quarter} {year}"): st.subheader("Earnings Call Transcript - {quarter} {year}:") stx.scrollableTextbox( file_text, height=700, border=False, fontFamily="Helvetica", ) else: st.write( "No specific document chosen. Please mention Ticker and Duration in Question." )