import os, sys, json import gradio as gr import openai from openai import OpenAI 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 langchain.vectorstores import MongoDBAtlasVectorSearch #from pymongo import MongoClient from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) # Schnittstellen hinzubinden und OpenAI Key holen aus den Secrets #client = OpenAI( #api_key=os.getenv("OPENAI_API_KEY"), #) #openai.api_key = os.getenv["OPENAI_API_KEY"] #Für MongoDB statt Chroma als Vektorstore #MONGODB_URI = os.environ["MONGODB_ATLAS_CLUSTER_URI"] #client = MongoClient(MONGODB_URI) #MONGODB_DB_NAME = "langchain_db" #MONGODB_COLLECTION_NAME = "gpt-4" #MONGODB_COLLECTION = client[MONGODB_DB_NAME][MONGODB_COLLECTION_NAME] #MONGODB_INDEX_NAME = "default" 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. Answer in german if not asked otherwise """ 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) OAI_API_KEY=os.getenv("OPENAI_API_KEY") #Pfad, wo Docs abgelegt werden können - lokal, also hier im HF Space (sonst auf eigenem Rechner) PATH_WORK = "." CHROMA_DIR = "/chroma" YOUTUBE_DIR = "/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-3.5-turbo-16k" def document_loading_splitting(): # Document loading docs = [] # Load PDF loader = PyPDFLoader(PDF_URL) docs.extend(loader.load()) # Load Web loader = WebBaseLoader(WEB_URL) docs.extend(loader.load()) # Load YouTube loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1, YOUTUBE_URL_2, YOUTUBE_URL_3], PATH_WORK + YOUTUBE_DIR), OpenAIWhisperParser()) docs.extend(loader.load()) # Document splitting text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150, chunk_size = 1500) splits = text_splitter.split_documents(docs) return splits def document_storage_chroma(splits): Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(disallowed_special = ()), persist_directory = CHROMA_DIR) def document_storage_mongodb(splits): MongoDBAtlasVectorSearch.from_documents(documents = splits, embedding = OpenAIEmbeddings(disallowed_special = ()), collection = MONGODB_COLLECTION, index_name = MONGODB_INDEX_NAME) def document_retrieval_chroma(llm, prompt): embeddings = OpenAIEmbeddings() #Alternative Embedding - für Vektorstore, um Ähnlichkeitsvektoren zu erzeugen #embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}) db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR) return db def document_retrieval_mongodb(llm, prompt): db = MongoDBAtlasVectorSearch.from_connection_string(MONGODB_URI, MONGODB_DB_NAME + "." + MONGODB_COLLECTION_NAME, OpenAIEmbeddings(disallowed_special = ()), index_name = MONGODB_INDEX_NAME) return db def llm_chain(llm, prompt): llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT) result = llm_chain.run({"question": prompt}) return result def rag_chain(llm, prompt, db): rag_chain = RetrievalQA.from_chain_type(llm, chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, retriever = db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True) result = rag_chain({"query": prompt}) return result["result"] def invoke(openai_api_key, rag_option, prompt): if (openai_api_key == ""): #raise gr.Error("OpenAI API Key is required.") openai_api_key= OAI_API_KEY if (rag_option 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 (rag_option == "Chroma"): #splits = document_loading_splitting() #document_storage_chroma(splits) db = document_retrieval_chroma(llm, prompt) result = rag_chain(llm, prompt, db) elif (rag_option == "MongoDB"): #splits = document_loading_splitting() #document_storage_mongodb(splits) db = document_retrieval_mongodb(llm, prompt) result = rag_chain(llm, prompt, db) else: result = llm_chain(llm, prompt) except Exception as e: raise gr.Error(e) return result description = """Überblick: Hier wird ein Large Language Model (LLM) mit Retrieval Augmented Generation (RAG) auf externen Daten demonstriert.\n\n Genauer: Folgende externe Daten sind als Beispiel gegeben: YouTube, PDF, and Web Alle neueren Datums!. \n\n Verwendete Technology: Gradio UI using the OpenAI API and AI-native Chroma embedding database / MongoDB vector search. Speech-to-text (STT) via whisper-1 model, text embedding via text-embedding-ada-002 model, and text generation via gpt-4 model. Implementation via AI-first LangChain toolkit.\n\n In addition to the OpenAI API version, see also the Amazon Bedrock API and Google Vertex AI API versions on GitHub.""" gr.close_all() demo = gr.Interface(fn=invoke, inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Radio(["Off", "Chroma", "MongoDB"], label="Retrieval Augmented Generation", value = "Off"), 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()