app.py

import gradio as gr
import openai, os

from langchain.chains import LLMChain, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader, WebBaseLoader
from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import OpenAIWhisperParser

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

#openai.api_key = os.environ["OPENAI_API_KEY"]

template = """If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer as concise as possible. Always say 
              "🔥 Thanks for using the app - Bernd Straehle." at the end of the answer. """

llm_template = "Answer the question at the end. " + template + "Question: {question} Helpful Answer: "
rag_template = "Use the following pieces of context to answer the question at the end. " + template + "{context} Question: {question} Helpful Answer: "

LLM_CHAIN_PROMPT = PromptTemplate(input_variables = ["question"], 
                                  template = llm_template)
RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], 
                                  template = rag_template)

CHROMA_DIR  = "/data/chroma"
YOUTUBE_DIR = "/data/youtube"

PDF_URL       = "https://arxiv.org/pdf/2303.08774.pdf"
WEB_URL       = "https://openai.com/research/gpt-4"
YOUTUBE_URL_1 = "https://www.youtube.com/watch?v=--khbXchTeE"
YOUTUBE_URL_2 = "https://www.youtube.com/watch?v=hdhZwyf24mE"
YOUTUBE_URL_3 = "https://www.youtube.com/watch?v=vw-KWfKwvTQ"

MODEL_NAME  = "gpt-4"

def invoke(openai_api_key, use_rag, prompt):
    llm = ChatOpenAI(model_name = MODEL_NAME, 
                     openai_api_key = openai_api_key, 
                     temperature = 0)
    if (use_rag):
        # Document loading
        #docs = []
        # Load PDF
        #loader = PyPDFLoader(PDF_URL)
        #docs.extend(loader.load())
        # Load Web
        #loader = WebBaseLoader(WEB_URL)
        #docs.extend(loader.load())
        # Load YouTube
        #loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,
        #                                           YOUTUBE_URL_2,
        #                                           YOUTUBE_URL_3], YOUTUBE_DIR), 
        #                       OpenAIWhisperParser())
        #docs.extend(loader.load())
        # Document splitting
        #text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
        #                                               chunk_size = 1500)
        #splits = text_splitter.split_documents(docs)
        # Document storage
        #vector_db = Chroma.from_documents(documents = splits, 
        #                                  embedding = OpenAIEmbeddings(disallowed_special = ()), 
        #                                  persist_directory = CHROMA_DIR)
        # Document retrieval
        vector_db = Chroma(embedding_function = OpenAIEmbeddings(),
                           persist_directory = CHROMA_DIR)
        rag_chain = RetrievalQA.from_chain_type(llm, 
                                                chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, 
                                                retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), 
                                                return_source_documents = True)
        result = rag_chain({"query": prompt})
        result = result["result"]
    else:
        chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT)
        result = chain.run({"question": prompt})
    return result

description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model (LLM)</strong> with <strong>Retrieval Augmented Generation (RAG)</strong> 
                 on <strong>external data</strong> (private/public & structured/unstructured).\n\n
                 <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, summarization, translation, etc.) on 
                 <a href='""" + YOUTUBE_URL_1 + """'>YouTube</a>, <a href='""" + PDF_URL + """'>PDF</a>, and <a href='""" + WEB_URL + """'>Web</a> 
                 <strong>GPT-4 data</strong> (created after training cutoff).
                 <ul style="list-style-type:square;">
                 <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "What is GPT-4?" The LLM <strong>without</strong> RAG does not know the answer.</li>
                 <li>Set "Retrieval Augmented Generation" to "<strong>True</strong>" and submit prompt "What is GPT-4?" The LLM <strong>with</strong> RAG knows the answer.</li>
                 <li>Experiment with prompts, e.g. "What are GPT-4's exam capabilities?" or "What is the cost and rate limit of the GPT-4 API? Answer in English, Chinese, and Swahili in JSON format."</li>
                 <li>Experiment more, for example "What languages does GPT-4 support?" or "Write a Python program calling the GPT-4 API."</li>
                 </ul>\n\n
                 <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://openai.com/'>OpenAI</a> API via AI-first 
                 <a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='""" + WEB_URL + """'>GPT-4</a> foundation model and AI-native 
                 <a href='https://www.trychroma.com/'>Chroma</a> embedding database."""

gr.close_all()
demo = gr.Interface(fn=invoke, 
                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), 
                              gr.Radio([True, False], label="Retrieval Augmented Generation", value = False), 
                              gr.Textbox(label = "Prompt", value = "What is GPT-4?", lines = 1)],
                    outputs = [gr.Textbox(label = "Completion", lines = 1)],
                    title = "Generative AI - LLM & RAG",
                    description = description)
demo.launch()