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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"), # this is also the default, it can be omitted
)
#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. Always say
"🧠 Thanks for using the app - Bernd" 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)
#Pfad, wo Docs abgelegt werden können - lokal, also hier im HF Space (sonst auf eigenem Rechner)
PATH_WORK = "."
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-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], 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 = CHROMA_DIR)
persist_directory = path_work + '/chroma',
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.")
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 = """<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>
data on GPT-4, published after LLM knowledge cutoff.
<ul style="list-style-type:square;">
<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>
<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>
<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>
<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 the <a href='https://openai.com/'>OpenAI</a> API and
AI-native <a href='https://www.trychroma.com/'>Chroma</a> embedding database /
<a href='https://www.mongodb.com/blog/post/introducing-atlas-vector-search-build-intelligent-applications-semantic-search-ai'>MongoDB</a> vector search.
<strong>Speech-to-text</strong> (STT) via <a href='https://openai.com/research/whisper'>whisper-1</a> model, <strong>text embedding</strong> via
<a href='https://openai.com/blog/new-and-improved-embedding-model'>text-embedding-ada-002</a> model, and <strong>text generation</strong> via
<a href='""" + WEB_URL + """'>gpt-4</a> model. Implementation via AI-first <a href='https://www.langchain.com/'>LangChain</a> toolkit.\n\n
In addition to the OpenAI API version, see also the <a href='https://aws.amazon.com/bedrock/'>Amazon Bedrock</a> API and
<a href='https://cloud.google.com/vertex-ai'>Google Vertex AI</a> API versions on
<a href='https://github.com/bstraehle/ai-ml-dl/tree/main/hugging-face'>GitHub</a>."""
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() |