How to Build a RAG-Powered Multi-Document Q&A Chatbot Using Llama2, Streamlit and Vector Database on E2E Cloud

November 29, 2023

Open Source AI and Document Q&A

Document Q&A is an important class of problems to tackle in the field of NLP. Documents are a common form of storing information. Hence the collective value of the data available is immense. Recent advancements in open-source AI and LLMs offer great promises on what AI is capable of given the right information and enough amounts of it. Harnessing information from documents can improve open-source AI and its benefits multi-fold.

The Promise of E2E Cloud

To build this multi-document Q&A chatbot, we will leverage E2E Cloud, which offers instant access to advanced cloud GPUs like A100, and the newly launched AI supercomputer, H100, in a highly cost-effective model.

To explore, sign up at E2E Cloud, and then head to the ‘Compute’ tab. There, you will find a range of advanced GPUs on offer.

The Llama 2 LLM Model

Llama 2 is an open-source model developed by Meta which succeeds their Llama 1 LLM. The Llama 2 family consists of models of size 7 Billion, 13 Billion and 70 Billion parameters. The Llama 2 models are trained on 40% more tokens than the preceding Llama 1 with the context length of the model being much longer at 4K.

The model has been built for both text and code generation and trained on a massive corpus of text from various sources and domains, covering 101 languages. Llama 2 can perform tasks such as translation, summarization, question-answering, and text generation in a zero-shot or few-shot manner, meaning that it does not require any fine-tuning or additional data for each task or language.

Open Source Vector Databases

A vector database is a type of database that stores data as high-dimensional vectors. These vectors are mathematical representations of some attribute / feature. For example, a vector can represent human speech, textual information, and so on. Similarity searches are used in vector databases to find information.

Vector databases are increasingly becoming powerful, and have been created as a database category of their own since the growth of Generative AI. Developers now have a range of choices – ChromaDB, Weaviate, Milvus, PGVector, Qdrant, Pinecone and others.

In this tutorial, we will use Qdrant as it is not only open source, but outperforms its competitors in offering semantics-based search, recommendation systems, and so on. Qdrant supports high-dimensional data in the form of images, text, audio or video while providing production-ready vector database services with APIs to store, search and manage data. It supports complex filtering, ranking and aggregation operations on vector data while being very distributed and scalable.

Explaining RAG - Pipeline

Retrieval Augmented Generation (RAG) is a technique used in Large Language Models to increase the amount of contextual data used by the model in generating its responses. Usually, LLMs answer queries by looking at its training data and query requirements in generating responses. However this can have various limitations.

For example, ChatGPT at its launch was famously limited to just information till 2021 while generating responses. Such limitations can pose major constraints on how these models can be utilized. Such lack of information while generating responses can be solved if the entire context can be given in one prompt.

For example, if an LLM is required to answer a question based on a recent book, the entire content of the book can be given along with the question to be answered as the prompt. This, however, will require a massive context window which isn’t available with most models. This is where a RAG pipeline can help out.

RAG technique will supplement LLM models in real-time using data retrieved from vector databases. This data retrieval can be further enhanced with the best-in-class retrieval techniques so that the best contextual information with respect to the given prompt can be supplied to the model when generating a response.

In other words, in the context of our book based answering example, the vector database can fetch the most contextually relevant information from the book instead of the entire book information and feed it to the model along with the question as a prompt to generate the response as required.

What Is Streamlit?

Streamlit is an open-source Python library used to create interactive web apps for various applications of machine learning like data exploration, data visualization, etc. Data can be visualized and explored as shareable web apps with very minimal code. Streamlit is fast and simple, allowing users to have dashboards, reports, build presentations and prototypes along with maintaining all of them easily with quick deployment and updates. The service allows one to build very interactive web apps for projects while not spending too much time on web development. Streamlit supports a wide range of libraries including pandas, matplotlib, scikit-learn, PyTorch, and TensorFlow.

Configuring Node on E2E

To build our pipeline on a locally running Llama 2 chat model (7B version), I used the GDC.V100-8.120GB plan which gives me a v100 GPU and 8 CPUs paired with 120 GB of memory. The plan price was 100 INR per hour. This was paired with 50 GB of disk space giving me enough headroom to operate. This is, however, not a bare minimum requirement and was chosen by me considering some extra headroom I might require while experimenting and setting up the pipeline.

Once you launch the node on E2E Cloud, don’t forget to add your ssh keys, so that you can start with the below-mentioned steps.

Also, we recommend using VSCode’s Remote Explorer extension to turn the E2E Cloud’s GPU node into a local development environment.

Building the RAG Pipeline

Initially before building, save a set of PDFs in a directory. In our case, that would be in the folder ‘PDFs/’. All queries will be based on that. Now let us take a look at how the pipeline can be built using Llama 2 and Vector DB.

Installing Packages

Note: Protobuf 3.20.* has to be installed initially for setting up the Llama 2 chat model locally.


!pip install -U transformers accelerate einops langchain xformers bitsandbytes faiss-gpu sentence_transformers
!pip install --upgrade huggingface_hub
!pip install protobuf==3.20.*
!pip -q install PyPDF2

Obtaining Llama 2 Chat Model (7B) and the Tokenizer


from torch import cuda, bfloat16
import transformers


model_id = 'meta-llama/Llama-2-7b-chat-hf'


device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'


# set quantization configuration to load large model with less GPU memory
# this requires the `bitsandbytes` library
bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)


# begin initializing HF items, you need an access token
hf_auth = ‘’
model_config = transformers.AutoConfig.from_pretrained(
    model_id,
    use_auth_token=hf_auth
)


model = transformers.LlamaForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map='auto',
    use_auth_token=hf_auth,
)	


tokenizer = transformers.AutoTokenizer.from_pretrained(
                        model_id,
                        use_auth_token=hf_auth)


# enable evaluation mode to allow model inference
model.eval()


print(f"Model loaded on {device}")

Setting Up a Query Pipeline

The query pipeline will be responsible for querying the Llama 2 model to obtain the response.


"""
Query Pipeline
"""


import torch


query_pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.float16,
        device_map="auto",)




# Testing


def test_model(tokenizer, pipeline, prompt_to_test):
    """
    Perform a query
    print the result
    Args:
        tokenizer: the tokenizer
        pipeline: the pipeline
        prompt_to_test: the prompt
    Returns
        None
    """
    # adapted from https://huggingface.co/blog/llama2#using-transformers
    sequences = pipeline(
        prompt_to_test,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        max_length=200,)
   
    for seq in sequences:
        print(f"Result: {seq['generated_text']}")


test_model(tokenizer,
           query_pipeline,
           "Who is the President of India?")

Converting the Pipeline to an Instance of HuggingFacePipeline

The HuggingFacePipeline is a part of the Langchain library, which provides an interface to Hugging Face’s Transformers library. It will be used to set up the RAG pipeline.


from langchain.llms import HuggingFacePipeline


llm = HuggingFacePipeline(pipeline=query_pipeline)
# checking again that everything is working fine
llm(prompt="How old is the previous president of USA and what will be his eldest child be 15 years from now?")

Setting Up Embeddings

Now we’ll generate embeddings for text data using a specific pre-trained model from Hugging Face’s Sentence Transformers, and it specifies that the computations should be performed on a GPU if one is available.


from langchain.embeddings import HuggingFaceEmbeddings


model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {"device": "cuda"}


embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

6. Managing Protobuf Installs

Existing installations of Protobuf will be deleted. Installing quadrant-client will install the latest version of Protobuf, which has to be downgraded later on.


!echo "y" | pip -q uninstall python3-protobuf
!echo "y" | pip -q uninstall protobuf
!pip install qdrant-client

Setting up the Pipeline to Read PDF Docs and Break into Chunks

All files in the PDF directory are read and the text data is read too.This is then broken into chunks, which will be vectorized later.


"""
Here we are using PDFs as a knowledge source to LLMs
"""


!pip -q install PyPDF2


# from llama_index import VectorStoreIndex, SimpleDirectoryReader
from langchain.text_splitter import CharacterTextSplitter
from PyPDF2 import PdfReader
import os


root = 'PDFs/'
text = "" # for storing the extracted text


for f in os.listdir(root):
    pdf_path = os.path.join(root, f)
    with open(pdf_path, 'rb') as file:
        pdf_reader = PdfReader(file)
        for page in pdf_reader.pages:
            text += page.extract_text()


# """
# Creating Text Chunks to divide longer text into smaller chunk using seperators
# """


text_splitter = CharacterTextSplitter(
    separator=" ",
    chunk_size=1024,
    chunk_overlap=20,
    length_function=len,)


docs = text_splitter.split_text(text)

‍

Protobuf Is Installed Again


!pip install --upgrade protobuf==4.21.7

Initializing the Vector Database


from langchain.vectorstores import Qdrant
from langchain.chains import VectorDBQA


doc_store = Qdrant.from_texts(
    docs,
    embeddings,
    path="/vectors",
    collection_name="my_documents",
)


"""Initialise chain"""


qa = VectorDBQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    vectorstore=doc_store,
)

Querying the Pipeline Based on Data in Vector DB

All files:


def test_rag(qa, query):
    print(f"Query: {query}\n")
    result = qa.run(query)
    print("\nResult: ", result)


query = "What were atomic habits?"
test_rag(qa, query)

Moving the Entire Pipeline to Steamlit

The pipeline built above has been moved to Streamlit in the following code section:


"""
With Streamlit
"""


import streamlit as st
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.vectorstores import Qdrant
from langchain.chains import VectorDBQA


import pickle
import os
#load api key lib
from dotenv import load_dotenv
import base64




#Background images add function
def add_bg_from_local(image_file):
    with open(image_file, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
    st.markdown(
    f"""
    
    """,
    unsafe_allow_html=True
    )


with st.sidebar:
    st.title('🦜️🔗VK - PDF BASED LLM-LANGCHAIN CHATBOT🤗')
    st.markdown('''
    ## About APP:


    The app's primary resource is utilised to create::


    - [streamlit](https://streamlit.io/)
    - [Langchain](https://docs.langchain.com/docs/)
    - [Llama 2](https://ai.meta.com/llama/)


    ''')


load_dotenv()


def main():
    st.header("Chat with your PDF!")


    #upload a your pdf file
    pdf = st.file_uploader("Upload the PDF", type='pdf')
    st.write(pdf.name)


    if pdf is not None:
        text = "" # for storing the extracted text


        for f in os.listdir(root):
            pdf_path = os.path.join(root, f)
            with open(pdf_path, 'rb') as file:
                pdf_reader = PdfReader(file)
                for page in pdf_reader.pages:
                    text += page.extract_text()


        #langchain_textspliter
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size = 1000,
            chunk_overlap = 200,
            length_function = len
        )


        chunks = text_splitter.split_text(text=text)
       
        text_splitter = CharacterTextSplitter(
        separator=" ",
        chunk_size=1024,
        chunk_overlap=20,
        length_function=len,)


        docs = text_splitter.split_text(text)
       
        #store pdf name
        store_name = pdf.name[:-4]
       
        if os.path.exists(f"{store_name}.pkl"):
            with open(f"{store_name}.pkl","rb") as f:
                vectorstore = pickle.load(f)
            #st.write("Already, Embeddings loaded from the your folder (disks)")
        else:
            #embedding (Openai methods)
            embeddings = OpenAIEmbeddings()


            #Store the chunks part in db (vector)
            vectorstore = Qdrant.from_texts(
                docs,
                embeddings,
                path="/vectors",
                collection_name="my_documents",
            )


            with open(f"{store_name}.pkl","wb") as f:
                pickle.dump(vectorstore,f)
           
            #st.write("Embedding computation completed")


        #st.write(chunks)
       
        #Accept user questions/query
       
        qa = VectorDBQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            vectorstore=doc_store,
            )
        query = st.text_input("Ask questions related to the uploaded pdf ")
        #st.write(query)


        if query:
           
            docs = qa.run(query)
            #st.write(docs)
           
            #openai rank lnv process
            llm = OpenAI(temperature=0)
            chain = load_qa_chain(llm=llm, chain_type= "stuff")
           
            with get_openai_callback() as cb:
                response = chain.run(input_documents = docs, question = query)
                print(cb)
            st.write(response)






if __name__=="__main__":
    main()

Conclusion

In this article, we have shown how to use Llama2, Streamlit and Qdrant Vector DB to build a RAG Pipeline for a multi-document Q&A chatbot. We have explained the main features and benefits of these open-source tools, and how they can facilitate document Q&A tasks. We have also demonstrated the steps to set up the environment, split and ingest documents, and query them using the RAG pipeline on Llama 2. We have presented the output of the chatbot, which can answer complex questions by retrieving relevant passages from multiple documents. We hope this article has inspired you to try out this approach and explore its potential for your own use cases.

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December 20, 2024

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

‍

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

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What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

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What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

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Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html