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import gradio as gr |
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import openai, os, shutil |
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from langchain.chains import RetrievalQA |
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from langchain.chat_models import ChatOpenAI |
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from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader |
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from langchain.document_loaders.generic import GenericLoader |
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from langchain.document_loaders.parsers import OpenAIWhisperParser |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.prompts import PromptTemplate |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import Chroma |
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from dotenv import load_dotenv, find_dotenv |
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_ = load_dotenv(find_dotenv()) |
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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 |
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an answer. Keep the answer as concise as possible. Always say "🔥 Thanks for using the app - Bernd Straehle." at the end of the answer. |
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{context} Question: {question} Helpful Answer: """ |
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QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template) |
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CHROMA_DIR = "docs/chroma" |
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YOUTUBE_DIR = "docs/youtube" |
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YOUTUBE_URL = " playlist" |
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MODEL_NAME = "gpt-4" |
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def invoke(openai_api_key, use_rag, prompt): |
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if (os.path.isdir(CHROMA_DIR)): |
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shutil.rmtree(CHROMA_DIR) |
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if (os.path.isdir(YOUTUBE_DIR)): |
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shutil.rmtree(YOUTUBE_DIR) |
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if (use_rag): |
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loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL], YOUTUBE_DIR), OpenAIWhisperParser()) |
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docs = loader.load() |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150) |
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splits = text_splitter.split_documents(docs) |
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vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR) |
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llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0) |
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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}) |
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result = qa_chain({"query": prompt}) |
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return result["result"] |
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description = """<strong>Overview:</strong> The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data |
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(in this case YouTube videos, but it could be PDFs, URLs, or other structured/unstructured private/public |
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<a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n |
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<strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases on a YouTube video (semantic search, sentiment analysis, summarization, |
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translation, etc.) The example is a <a href='c'>short video about GPT-4</a>. |
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<ul style="list-style-type:square;"> |
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<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> |
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<li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li> |
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<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> |
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</ul> |
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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 |
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<a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n |
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<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 |
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<a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='https://openai.com/research/whisper'>Whisper</a> (speech-to-text) and |
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<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> |
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embedding database.""" |
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gr.close_all() |
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demo = gr.Interface(fn=invoke, |
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inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Radio([True, False], label="Use RAG", value = False), gr.Textbox(label = "Prompt", value = "what is gpt-4", lines = 1)], |
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outputs = [gr.Textbox(label = "Completion", lines = 1)], |
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title = "Generative AI - LLM & RAG", |
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description = description) |
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demo.queue().launch() |
