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| import gradio as gr | |
| import openai, os, shutil | |
| from langchain.chains import 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 = """Use the following pieces of context to answer the question at the end. 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. | |
| {context} Question: {question} Helpful Answer: """ | |
| QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template) | |
| CHROMA_DIR = "docs/chroma" | |
| YOUTUBE_DIR = "docs/youtube" | |
| MODEL_NAME = "gpt-4" | |
| def invoke(openai_api_key, youtube_url, process_video, prompt): | |
| if (process_video): | |
| if (os.path.isdir(CHROMA_DIR)): | |
| shutil.rmtree(CHROMA_DIR) | |
| if (os.path.isdir(YOUTUBE_DIR)): | |
| shutil.rmtree(YOUTUBE_DIR) | |
| loader = GenericLoader(YoutubeAudioLoader([youtube_url], YOUTUBE_DIR), OpenAIWhisperParser()) | |
| docs = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150) | |
| splits = text_splitter.split_documents(docs) | |
| vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR) | |
| else: | |
| vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings()) | |
| llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0) | |
| qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT}) | |
| result = qa_chain({"query": prompt}) | |
| #print(result) | |
| return result["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 YouTube videos, 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 YouTube video (semantic search, sentiment analysis, summarization, | |
| translation, etc.) The example is a short video about GPT-4. | |
| <ul style="list-style-type:square;"> | |
| <li>Set "Process Video" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li> | |
| <li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li> | |
| <li>Set "Process Video" to "False" and experiment with different prompts, for example "what is gpt-4, answer in german" or "write a haiku 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</a> playlist.\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.Textbox(label = "YouTube URL", value = "https://www.youtube.com/watch?v=--khbXchTeE", lines = 1), gr.Radio([True, False], label="Process Video", 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.queue().launch() |