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import os, sys, json |
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import gradio as gr |
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import openai |
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from langchain.chains import LLMChain, RetrievalQA |
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from langchain.chat_models import ChatOpenAI |
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from langchain.document_loaders import PyPDFLoader, WebBaseLoader |
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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 langchain.vectorstores import MongoDBAtlasVectorSearch |
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from pymongo import MongoClient |
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from dotenv import load_dotenv, find_dotenv |
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_ = load_dotenv(find_dotenv()) |
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openai.api_key = os.getenv["OPENAI_API_KEY"] |
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MONGODB_URI = os.environ["MONGODB_ATLAS_CLUSTER_URI"] |
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client = MongoClient(MONGODB_URI) |
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MONGODB_DB_NAME = "langchain_db" |
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MONGODB_COLLECTION_NAME = "gpt-4" |
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MONGODB_COLLECTION = client[MONGODB_DB_NAME][MONGODB_COLLECTION_NAME] |
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MONGODB_INDEX_NAME = "default" |
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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 |
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"🧠 Thanks for using the app - Bernd" at the end of the answer. """ |
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llm_template = "Answer the question at the end. " + template + "Question: {question} Helpful Answer: " |
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rag_template = "Use the following pieces of context to answer the question at the end. " + template + "{context} Question: {question} Helpful Answer: " |
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LLM_CHAIN_PROMPT = PromptTemplate(input_variables = ["question"], |
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template = llm_template) |
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RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], |
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template = rag_template) |
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CHROMA_DIR = "/data/chroma" |
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YOUTUBE_DIR = "/data/youtube" |
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PDF_URL = "https://arxiv.org/pdf/2303.08774.pdf" |
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WEB_URL = "https://openai.com/research/gpt-4" |
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YOUTUBE_URL_1 = "https://www.youtube.com/watch?v=--khbXchTeE" |
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YOUTUBE_URL_2 = "https://www.youtube.com/watch?v=hdhZwyf24mE" |
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YOUTUBE_URL_3 = "https://www.youtube.com/watch?v=vw-KWfKwvTQ" |
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MODEL_NAME = "gpt-4" |
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def document_loading_splitting(): |
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docs = [] |
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loader = PyPDFLoader(PDF_URL) |
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docs.extend(loader.load()) |
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loader = WebBaseLoader(WEB_URL) |
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docs.extend(loader.load()) |
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loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1, |
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YOUTUBE_URL_2, |
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YOUTUBE_URL_3], YOUTUBE_DIR), |
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OpenAIWhisperParser()) |
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docs.extend(loader.load()) |
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text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150, |
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chunk_size = 1500) |
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splits = text_splitter.split_documents(docs) |
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return splits |
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def document_storage_chroma(splits): |
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Chroma.from_documents(documents = splits, |
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embedding = OpenAIEmbeddings(disallowed_special = ()), |
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persist_directory = CHROMA_DIR) |
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def document_storage_mongodb(splits): |
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MongoDBAtlasVectorSearch.from_documents(documents = splits, |
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embedding = OpenAIEmbeddings(disallowed_special = ()), |
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collection = MONGODB_COLLECTION, |
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index_name = MONGODB_INDEX_NAME) |
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def document_retrieval_chroma(llm, prompt): |
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db = Chroma(embedding_function = OpenAIEmbeddings(), |
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persist_directory = CHROMA_DIR) |
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return db |
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def document_retrieval_mongodb(llm, prompt): |
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db = MongoDBAtlasVectorSearch.from_connection_string(MONGODB_URI, |
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MONGODB_DB_NAME + "." + MONGODB_COLLECTION_NAME, |
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OpenAIEmbeddings(disallowed_special = ()), |
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index_name = MONGODB_INDEX_NAME) |
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return db |
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def llm_chain(llm, prompt): |
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llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT) |
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result = llm_chain.run({"question": prompt}) |
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return result |
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def rag_chain(llm, prompt, db): |
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rag_chain = RetrievalQA.from_chain_type(llm, |
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chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, |
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retriever = db.as_retriever(search_kwargs = {"k": 3}), |
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return_source_documents = True) |
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result = rag_chain({"query": prompt}) |
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return result["result"] |
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def invoke(openai_api_key, rag_option, prompt): |
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if (openai_api_key == ""): |
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raise gr.Error("OpenAI API Key is required.") |
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if (rag_option is None): |
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raise gr.Error("Retrieval Augmented Generation is required.") |
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if (prompt == ""): |
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raise gr.Error("Prompt is required.") |
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try: |
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llm = ChatOpenAI(model_name = MODEL_NAME, |
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openai_api_key = openai_api_key, |
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temperature = 0) |
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if (rag_option == "Chroma"): |
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db = document_retrieval_chroma(llm, prompt) |
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result = rag_chain(llm, prompt, db) |
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elif (rag_option == "MongoDB"): |
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db = document_retrieval_mongodb(llm, prompt) |
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result = rag_chain(llm, prompt, db) |
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else: |
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result = llm_chain(llm, prompt) |
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except Exception as e: |
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raise gr.Error(e) |
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return result |
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description = """<strong>Overview:</strong> Reasoning application that demonstrates a <strong>Large Language Model (LLM)</strong> with |
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<strong>Retrieval Augmented Generation (RAG)</strong> on <strong>external data</strong>.\n\n |
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<strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, summarization, translation, etc.) on |
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<a href='""" + YOUTUBE_URL_1 + """'>YouTube</a>, <a href='""" + PDF_URL + """'>PDF</a>, and <a href='""" + WEB_URL + """'>Web</a> |
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data on GPT-4, published after LLM knowledge cutoff. |
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<ul style="list-style-type:square;"> |
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<li>Set "Retrieval Augmented Generation" to "<strong>Off</strong>" and submit prompt "What is GPT-4?" The <strong>LLM without RAG</strong> does not know the answer.</li> |
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<li>Set "Retrieval Augmented Generation" to "<strong>Chroma</strong>" or "<strong>MongoDB</strong>" and submit prompt "What is GPT-4?" The <strong>LLM with RAG</strong> knows the answer.</li> |
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<li>Experiment with prompts, e.g. "What are GPT-4's media capabilities in 5 emojis and 1 sentence?", "List GPT-4's exam scores and benchmark results.", or "Compare GPT-4 to GPT-3.5 in markdown table format."</li> |
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<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> |
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</ul>\n\n |
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<strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using the <a href='https://openai.com/'>OpenAI</a> API and |
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AI-native <a href='https://www.trychroma.com/'>Chroma</a> embedding database / |
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<a href='https://www.mongodb.com/blog/post/introducing-atlas-vector-search-build-intelligent-applications-semantic-search-ai'>MongoDB</a> vector search. |
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<strong>Speech-to-text</strong> (STT) via <a href='https://openai.com/research/whisper'>whisper-1</a> model, <strong>text embedding</strong> via |
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<a href='https://openai.com/blog/new-and-improved-embedding-model'>text-embedding-ada-002</a> model, and <strong>text generation</strong> via |
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<a href='""" + WEB_URL + """'>gpt-4</a> model. Implementation via AI-first <a href='https://www.langchain.com/'>LangChain</a> toolkit.\n\n |
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In addition to the OpenAI API version, see also the <a href='https://aws.amazon.com/bedrock/'>Amazon Bedrock</a> API and |
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<a href='https://cloud.google.com/vertex-ai'>Google Vertex AI</a> API versions on |
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<a href='https://github.com/bstraehle/ai-ml-dl/tree/main/hugging-face'>GitHub</a>.""" |
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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), |
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gr.Radio(["Off", "Chroma", "MongoDB"], label="Retrieval Augmented Generation", value = "Off"), |
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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.launch() |
