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.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  = "docs/chroma"
YOUTUBE_DIR = "docs/youtube"

YOUTUBE_URL = "https://www.youtube.com/watch?v=--khbXchTeE"

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):
        if (os.path.isdir(CHROMA_DIR)):
            vector_db = Chroma(embedding_function = OpenAIEmbeddings(),
                               persist_directory = CHROMA_DIR)
        else:
            loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL], YOUTUBE_DIR), 
                                   OpenAIWhisperParser())
            docs = loader.load()
            text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
                                                           chunk_size = 1500)
            splits = text_splitter.split_documents(docs)
            vector_db = Chroma.from_documents(documents = splits, 
                                              embedding = 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 Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data 
                 (in this case a YouTube video, but it could be PDFs, URLs, or other structured/unstructured private/public 
                 <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
                 <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases on a <a href='https://www.youtube.com/watch?v=--khbXchTeE'>short video about GPT-4</a> 
                 (semantic search, sentiment analysis, summarization, translation, etc.)
                 <ul style="list-style-type:square;">
                 <li>Set "Retrieval Augmented Generation" to "False" 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 "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
                 <li>Experiment with different prompts, for example "what is gpt-4, answer in german" or "write a poem about gpt-4".</li>
                 </ul>
                 In a production system, processing external data would be done in a batch process. An idea for a production system would be to perform LLM use cases on the 
                 <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n
                 <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API via AI-first 
                 <a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='https://openai.com/research/whisper'>Whisper</a> (speech-to-text) and 
                 <a href='https://openai.com/research/gpt-4'>GPT-4</a> (LLM) foundation models as well as 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()