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| import os | |
| import string | |
| import time | |
| from typing import Any, Dict, List, Tuple, Union | |
| import chromadb | |
| import numpy as np | |
| import openai | |
| import pandas as pd | |
| import requests | |
| import streamlit as st | |
| from datasets import load_dataset | |
| from langchain.document_loaders import TextLoader | |
| from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from scipy.spatial.distance import cosine | |
| from utils.helper_functions import * | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| # Front-end Design | |
| st.set_page_config(layout="wide") | |
| st.title("YSA|Larkin Chatbot") | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| st.sidebar.markdown( | |
| """ | |
| ### Instructions: | |
| This app guides you through YSA's website, utilizing a RAG-ready Q&A dataset [here](https://huggingface.co/datasets/eagle0504/youthless-homeless-shelter-web-scrape-dataset-qa-formatted) for chatbot assistance. The Larkin domain is processed into QA data [here](https://huggingface.co/datasets/eagle0504/larkin-web-scrape-dataset-qa-formatted). 🤖 Enter a question, and it finds similar ones in the database, offering answers with a distance score to gauge relevance—the lower the score, the closer the match. 🎯 For better accuracy and to reduce errors, user feedback helps refine the database. ✨ | |
| """ | |
| ) | |
| st.sidebar.success("Select a shelter first!") | |
| option = st.sidebar.selectbox("Which website do you want to ask?", ("YSA", "Larkin")) | |
| st.sidebar.warning( | |
| "Runnning AI Judge takes a bit longer so we default this option as 'No'." | |
| ) | |
| run_ai_judge = st.sidebar.selectbox( | |
| "Shall we run AI Judge to provide additional scores?", ("No", "Yes") | |
| ) | |
| special_threshold = st.sidebar.number_input( | |
| "Insert a threshold for distances score to filter data (default 0.2):", | |
| value=0.2, | |
| placeholder="Type a number...", | |
| ) | |
| st.sidebar.success( | |
| "The 'distances' score indicates the proximity of your question to our database questions (lower is better). The 'ai_judge' ranks the similarity between user's question and database answers independently (higher is better)." | |
| ) | |
| clear_button = st.sidebar.button("Clear Conversation", key="clear") | |
| if clear_button: | |
| st.session_state.messages = [] | |
| # Load the dataset from a provided source. | |
| if option == "YSA": | |
| begin_t = time.time() | |
| dataset = load_dataset( | |
| "eagle0504/youthless-homeless-shelter-web-scrape-dataset-qa-formatted" | |
| ) | |
| end_t = time.time() | |
| st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec") | |
| initial_input = "Tell me about YSA" | |
| else: | |
| begin_t = time.time() | |
| dataset = load_dataset("eagle0504/larkin-web-scrape-dataset-qa-formatted") | |
| end_t = time.time() | |
| st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec") | |
| initial_input = "Tell me about Larkin" | |
| # Initialize a new client for ChromeDB. | |
| client = chromadb.Client() | |
| # Generate a random number between 1 billion and 10 billion. | |
| random_number: int = np.random.randint(low=1e9, high=1e10) | |
| # Generate a random string consisting of 10 uppercase letters and digits. | |
| random_string: str = "".join( | |
| np.random.choice(list(string.ascii_uppercase + string.digits), size=10) | |
| ) | |
| # Combine the random number and random string into one identifier. | |
| combined_string: str = f"{random_number}{random_string}" | |
| # Create a new collection in ChromeDB with the combined string as its name. | |
| collection = client.create_collection(combined_string) | |
| # Embed and store the first N supports for this demo | |
| with st.spinner("Loading, please be patient with us ... 🙏"): | |
| L = len(dataset["train"]["questions"]) | |
| begin_t = time.time() | |
| collection.add( | |
| ids=[str(i) for i in range(0, L)], # IDs are just strings | |
| documents=dataset["train"]["questions"], # Enter questions here | |
| metadatas=[{"type": "support"} for _ in range(0, L)], | |
| ) | |
| end_t = time.time() | |
| st.success(f"Add to VectorDB. | Time: {end_t - begin_t} sec") | |
| # React to user input | |
| if prompt := st.chat_input(initial_input): | |
| with st.spinner("Loading, please be patient with us ... 🙏"): | |
| # Display user message in chat message container | |
| st.chat_message("user").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| question = prompt | |
| begin_t = time.time() | |
| results = collection.query(query_texts=question, n_results=5) | |
| end_t = time.time() | |
| st.success(f"Query answser. | Time: {end_t - begin_t} sec") | |
| idx = results["ids"][0] | |
| idx = [int(i) for i in idx] | |
| ref = pd.DataFrame( | |
| { | |
| "idx": idx, | |
| "questions": [dataset["train"]["questions"][i] for i in idx], | |
| "answers": [dataset["train"]["answers"][i] for i in idx], | |
| "distances": results["distances"][0], | |
| } | |
| ) | |
| # special_threshold = st.sidebar.slider('How old are you?', 0, 0.6, 0.1) # 0.3 | |
| filtered_ref = ref[ref["distances"] < special_threshold] | |
| if filtered_ref.shape[0] > 0: | |
| st.success("There are highly relevant information in our database.") | |
| ref_from_db_search = filtered_ref["answers"].str.cat(sep=" ") | |
| final_ref = filtered_ref | |
| else: | |
| st.warning( | |
| "The database may not have relevant information to help your question so please be aware of hallucinations." | |
| ) | |
| ref_from_db_search = ref["answers"].str.cat(sep=" ") | |
| final_ref = ref | |
| if option == "YSA": | |
| try: | |
| begin_t = time.time() | |
| llm_response = llama2_7b_ysa(question) | |
| end_t = time.time() | |
| st.success(f"Running LLM. | Time: {end_t - begin_t} sec") | |
| except: | |
| st.warning("Sorry, the inference endpoint is temporarily down. 😔") | |
| llm_response = "NA." | |
| else: | |
| st.warning( | |
| "Apologies! We are in the progress of fine-tune the model, so it's currently unavailable. ⚙️" | |
| ) | |
| llm_response = "NA" | |
| finetuned_llm_guess = ["from_llm", question, llm_response, 0] | |
| final_ref.loc[-1] = finetuned_llm_guess | |
| final_ref = final_ref.reset_index() | |
| # add ai judge as additional rating | |
| if run_ai_judge == "Yes": | |
| independent_ai_judge_score = [] | |
| begin_t = time.time() | |
| for i in range(final_ref.shape[0]): | |
| this_content = final_ref["answers"][i] | |
| if len(this_content) > 3: | |
| arr1 = openai_text_embedding(question) | |
| arr2 = openai_text_embedding(this_content) | |
| # this_score = calculate_sts_openai_score(question, this_content) | |
| this_score = quantized_influence(arr1, arr2) | |
| else: | |
| this_score = 0 | |
| independent_ai_judge_score.append(this_score) | |
| final_ref["ai_judge"] = independent_ai_judge_score | |
| end_t = time.time() | |
| st.success(f"Using AI Judge. | Time: {end_t - begin_t} sec") | |
| engineered_prompt = f""" | |
| Based on the context: {ref_from_db_search} | |
| answer the user question: {question} | |
| Answer the question directly (don't say "based on the context, ...") | |
| """ | |
| begin_t = time.time() | |
| answer = call_chatgpt(engineered_prompt) | |
| end_t = time.time() | |
| st.success(f"Final API Call. | Time: {end_t - begin_t} sec") | |
| response = answer | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| with st.spinner("Wait for it..."): | |
| st.markdown(response) | |
| with st.expander("See reference:"): | |
| st.table(final_ref) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |