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| 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): | |
| if (openai_api_key == ""): | |
| raise gr.Error("OpenAI API Key is required.") | |
| if (use_rag is None): | |
| raise gr.Error("Retrieval Augmented Generation is required.") | |
| if (prompt == ""): | |
| raise gr.Error("Prompt is required.") | |
| try: | |
| 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_1) | |
| #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}) | |
| except Exception as e: | |
| raise gr.Error(e) | |
| return result | |
| description = """<strong>Overview:</strong> Reasoning application that demonstrates a <strong>Large Language Model (LLM)</strong> with | |
| <strong>Retrieval Augmented Generation (RAG)</strong> on <strong>external data</strong>.\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 LLM 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 image capabilities in one word and sentence?", "List GPT-4's exam scores and benchmark results.", or "Compare GPT-4 to GPT-3.5 in markdown table format."</li> | |
| <li>Experiment some more, for example "What is the GPT-4 API's cost and rate limit? Answer in English, Arabic, Chinese, Hindi, and Russian in JSON format." or "Write a Python program that calls 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. Speech-to-text via <a href='https://openai.com/research/whisper'>Whisper</a> | |
| foundation model.""" | |
| 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() |