Monthly Archives: May 2024

In the world of online retail, creating high-quality product descriptions for millions of products is a crucial, but time-consuming task. Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. One of the main advantages of high-quality product descriptions is the improvement in searchability. Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. For example, a product that has a description that includes words such as “long sleeve” and “cotton neck” will be returned if a consumer is looking for a “long sleeve cotton shirt.” Furthermore, having factoid product descriptions can increase customer satisfaction by enabling a more personalized buying experience and improving the algorithms for recommending more relevant products to users, which raise the probability that users will make a purchase.

With the advancement of Generative AI, we can use vision-language models (VLMs) to predict product attributes directly from images. Pre-trained image captioning or visual question answering (VQA) models perform well on describing every-day images but can’t to capture the domain-specific nuances of ecommerce products needed to achieve satisfactory performance in all product categories. To solve this problem, this post shows you how to predict domain-specific product attributes from product images by fine-tuning a VLM on a fashion dataset using Amazon SageMaker, and then using Amazon Bedrock to generate product descriptions using the predicted attributes as input. So you can follow along, we’re sharing the code in a GitHub repository.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

You can use a managed service, such as Amazon Rekognition, to predict product attributes as explained in Automating product description generation with Amazon Bedrock. However, if you’re trying to extract specifics and detailed characteristics of your product or your domain (industry), fine-tuning a VLM on Amazon SageMaker is necessary.

Vision-language models

Since 2021, there has been a rise in interest in vision-language models (VLMs), which led to the release of solutions such as Contrastive Language-Image Pre-training (CLIP) and Bootstrapping Language-Image Pre-training (BLIP). When it comes to tasks such as image captioning, text-guided image generation, and visual question-answering, VLMs have demonstrated state-of-the art performance.

In this post, we use BLIP-2, which was introduced in BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models, as our VLM. BLIP-2 consists of three models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model (LLM). We use a version of BLIP-2, that contains Flan-T5-XL as the LLM.

The following diagram illustrates the overview of BLIP-2:

Figure 1: BLIP-2 overview

The pre-trained version of the BLIP-2 model has been demonstrated in Build an image-to-text generative AI application using multimodality models on Amazon SageMaker and Build a generative AI-based content moderation solution on Amazon SageMaker JumpStart. In this post, we demonstrate how to fine-tune BLIP-2 for a domain-specific use case.

Solution overview

The following diagram illustrates the solution architecture.

Figure 2: High-level solution architecture

The high-level overview of the solution is:

• An ML scientist uses Sagemaker notebooks to process and split the data into training and validation data.
• The datasets are uploaded to Amazon Simple Storage Service (Amazon S3) using the S3 client (a wrapper around an HTTP call).
• Then the Sagemaker client is used to launch a Sagemaker Training job, again a wrapper for an HTTP call.
• The training job manages the copying of the datasets from S3 to the training container, the training of the model, and the saving of its artifacts to S3.
• Then, through another call of the Sagemaker client, an endpoint is generated, copying the model artifacts into the endpoint hosting container.
• The inference workflow is then invoked through an AWS Lambda request, which first makes an HTTP request to the Sagemaker endpoint, and then uses that to make another request to Amazon Bedrock.

In the following sections, we demonstrate how to:

• Set up the development environment
• Load and prepare the dataset
• Fine-tune the BLIP-2 model to learn product attributes using SageMaker
• Deploy the fine-tuned BLIP-2 model and predict product attributes using SageMaker
• Generate product descriptions from predicted product attributes using Amazon Bedrock

Set up the development environment

An AWS account is needed with an AWS Identity and Access Management (IAM) role that has permissions to manage resources created as part of the solution. For details, see Creating an AWS account.

We use Amazon SageMaker Studio with the ml.t3.medium instance and the Data Science 3.0 image. However, you can also use an Amazon SageMaker notebook instance or any integrated development environment (IDE) of your choice.

Note: Be sure to set up your AWS Command Line Interface (AWS CLI) credentials correctly. For more information, see Configure the AWS CLI.

An ml.g5.2xlarge instance is used for SageMaker Training jobs, and an ml.g5.2xlarge instance is used for SageMaker endpoints. Ensure sufficient capacity for this instance in your AWS account by requesting a quota increase if required. Also check the pricing of the on-demand instances.

You need to clone this GitHub repository for replicating the solution demonstrated in this post. First, launch the notebook main.ipynb in SageMaker Studio by selecting the Image as Data Science and Kernel as Python 3. Install all the required libraries mentioned in the requirements.txt.

Load and prepare the dataset

For this post, we use the Kaggle Fashion Images Dataset, which contain 44,000 products with multiple category labels, descriptions, and high resolution images. In this post we want to demonstrate how to fine-tune a model to learn attributes such as fabric, fit, collar, pattern, and sleeve length of a shirt using the image and a question as inputs.

Each product is identified by an ID such as 38642, and there is a map to all the products in styles.csv. From here, we can fetch the image for this product from images/38642.jpg and the complete metadata from styles/38642.json. To fine-tune our model, we need to convert our structured examples into a collection of question and answer pairs. Our final dataset has the following format after processing for each attribute:

Id | Question | Answer
38642 | What is the fabric of the clothing in this picture? | Fabric: Cotton

Fine-tune the BLIP-2 model to learn product attributes using SageMaker

To launch a SageMaker Training job, we need the HuggingFace Estimator. SageMaker starts and manages all of the necessary Amazon Elastic Compute Cloud (Amazon EC2) instances for us, supplies the appropriate Hugging Face container, uploads the specified scripts, and downloads data from our S3 bucket to the container to /opt/ml/input/data.

We fine-tune BLIP-2 using the Low-Rank Adaptation (LoRA) technique, which adds trainable rank decomposition matrices to every Transformer structure layer while keeping the pre-trained model weights in a static state. This technique can increase training throughput and reduce the amount of GPU RAM required by 3 times and the number of trainable parameters by 10,000 times. Despite using fewer trainable parameters, LoRA has been demonstrated to perform as well as or better than the full fine-tuning technique.

We prepared entrypoint_vqa_finetuning.py which implements fine-tuning of BLIP-2 with the LoRA technique using Hugging Face Transformers, Accelerate, and Parameter-Efficient Fine-Tuning (PEFT). The script also merges the LoRA weights into the model weights after training. As a result, you can deploy the model as a normal model without any additional code.

from peft import LoraConfig, get_peft_model
from transformers import Blip2ForConditionalGeneration
 
model = Blip2ForConditionalGeneration.from_pretrained(
        "Salesforce/blip2-flan-t5-xl",
        device_map="auto",
        cache_dir="/tmp",
        load_in_8bit=True,
    )

config = LoraConfig(
    r=8, # Lora attention dimension.
    lora_alpha=32, # the alpha parameter for Lora scaling.
    lora_dropout=0.05, # the dropout probability for Lora layers.
    bias="none", # the bias type for Lora.
    target_modules=["q", "v"],
)

model = get_peft_model(model, config)

from sagemaker.huggingface import HuggingFace

hyperparameters = {
    'epochs': 10,
    'file-name': "vqa_train.csv",
}

estimator = HuggingFace(
    entry_point="entrypoint_vqa_finetuning.py",
    source_dir="../src",
    role=role,
    instance_count=1,
    instance_type="ml.g5.2xlarge", 
    transformers_version='4.26',
    pytorch_version='1.13',
    py_version='py39',
    hyperparameters = hyperparameters,
    base_job_name="VQA",
    sagemaker_session=sagemaker_session,
    output_path=f"{output_path}/models",
    code_location=f"{output_path}/code",
    volume_size=60,
    metric_definitions=[
        {'Name': 'batch_loss', 'Regex': 'Loss: ([0-9\.]+)'},
        {'Name': 'epoch_loss', 'Regex': 'Epoch Loss: ([0-9\.]+)'}
    ],
)

We can start our training job by running with the .fit() method and passing our Amazon S3 path for images and our input file.

estimator.fit({"images": images_input, "input_file": input_file})

Deploy the fine-tuned BLIP-2 model and predict product attributes using SageMaker

We deploy the fine-tuned BLIP-2 model to the SageMaker real time endpoint using the HuggingFace Inference Container. You can also use the large model inference (LMI) container, which is described in more detail in Build a generative AI-based content moderation solution on Amazon SageMaker JumpStart, which deploys a pre-trained BLIP-2 model. Here, we reference our fine-tuned model in Amazon S3 instead of the pre-trained model available in the Hugging Face hub. We first create the model and deploy the endpoint.

from sagemaker.huggingface import HuggingFaceModel

model = HuggingFaceModel(
   model_data=estimator.model_data,
   role=role,
   transformers_version="4.28",
   pytorch_version="2.0",
   py_version="py310",
   model_server_workers=1,
   sagemaker_session=sagemaker_session
)

endpoint_name = "endpoint-finetuned-blip2"
model.deploy(initial_instance_count=1, instance_type="ml.g5.2xlarge", endpoint_name=endpoint_name )

When the endpoint status becomes in service, we can invoke the endpoint for the instructed vision-to-language generation task with an input image and a question as a prompt:

inputs = {
    "prompt": "What is the sleeve length of the shirt in this picture?",
    "image": image # image encoded in Base64
}

The output response looks like the following:

{"Sleeve Length": "Long Sleeves"}

Generate product descriptions from predicted product attributes using Amazon Bedrock

To get started with Amazon Bedrock, request access to the foundational models (they are not enabled by default). You can follow the steps in the documentation to enable model access. In this post, we use Anthropic’s Claude in Amazon Bedrock to generate product descriptions. Specifically, we use the model anthropic.claude-3-sonnet-20240229-v1 because it provides good performance and speed.

After creating the boto3 client for Amazon Bedrock, we create a prompt string that specifies that we want to generate product descriptions using the product attributes.

You are an expert in writing product descriptions for shirts. Use the data below to create product description for a website. The product description should contain all given attributes.
Provide some inspirational sentences, for example, how the fabric moves. Think about what a potential customer wants to know about the shirts. Here are the facts you need to create the product descriptions:
[Here we insert the predicted attributes by the BLIP-2 model]

The prompt and model parameters, including maximum number of tokens used in the response and the temperature, are passed to the body. The JSON response must be parsed before the resulting text is printed in the final line.

bedrock = boto3.client(service_name='bedrock-runtime', region_name='us-west-2')

model_id = "anthropic.claude-3-sonnet-20240229-v1"

body = json.dumps(
    {"system": prompt, "messages": attributes_content, "max_tokens": 400, "temperature": 0.1, "anthropic_version": "bedrock-2023-05-31"}
)

response = bedrock.invoke_model(
    body=body,
    modelId=model_id,
    accept='application/json',
    contentType='application/json'
)

The generated product description response looks like the following:

"Classic Striped Shirt Relax into comfortable casual style with this classic collared striped shirt. With a regular fit that is neither too slim nor too loose, this versatile top layers perfectly under sweaters or jackets."

Conclusion

We’ve shown you how the combination of VLMs on SageMaker and LLMs on Amazon Bedrock present a powerful solution for automating fashion product description generation. By fine-tuning the BLIP-2 model on a fashion dataset using Amazon SageMaker, you can predict domain-specific and nuanced product attributes directly from images. Then, using the capabilities of Amazon Bedrock, you can generate product descriptions from the predicted product attributes, enhancing the searchability and personalization of ecommerce platforms. As we continue to explore the potential of generative AI, LLMs and VLMs emerge as a promising avenue for revolutionizing content generation in the ever-evolving landscape of online retail. As a next step, you can try fine-tuning this model on your own dataset using the code provided in the GitHub repository to test and benchmark the results for your use cases.

About the Authors 

Antonia Wiebeler is a Data Scientist at the AWS Generative AI Innovation Center, where she enjoys building proofs of concept for customers. Her passion is exploring how generative AI can solve real-world problems and create value for customers. While she is not coding, she enjoys running and competing in triathlons.

Daniel Zagyva is a Data Scientist at AWS Professional Services. He specializes in developing scalable, production-grade machine learning solutions for AWS customers. His experience extends across different areas, including natural language processing, generative AI, and machine learning operations.

Lun Yeh is a Machine Learning Engineer at AWS Professional Services. She specializes in NLP, forecasting, MLOps, and generative AI and helps customers adopt machine learning in their businesses. She graduated from TU Delft with a degree in Data Science & Technology.

Fotinos Kyriakides is an AI/ML Consultant at AWS Professional Services specializing in developing production-ready ML solutions and platforms for AWS customers. In his free time Fotinos enjoys running and exploring.

Read More

Retrieval Augmented Generation (RAG) models have emerged as a promising approach to enhance the capabilities of language models by incorporating external knowledge from large text corpora. However, despite their impressive performance in various natural language processing tasks, RAG models still face several limitations that need to be addressed.

Naive RAG models face limitations such as missing content, reasoning mismatch, and challenges in handling multimodal data. Although they can retrieve relevant information, they may struggle to generate complete and coherent responses when required information is absent, leading to incomplete or inaccurate outputs. Additionally, even with relevant information retrieved, the models may have difficulty correctly interpreting and reasoning over the content, resulting in inconsistencies or logical errors. Furthermore, effectively understanding and reasoning over multimodal data remains a significant challenge for these primarily text-based models.

In this post, we present a new approach named multimodal RAG (mmRAG) to tackle those existing limitations in greater detail. The solution intends to address these limitations for practical generative artificial intelligence (AI) assistant use cases. Additionally, we examine potential solutions to enhance the capabilities of large language models (LLMs) and visual language models (VLMs) with advanced LangChain capabilities, enabling them to generate more comprehensive, coherent, and accurate outputs while effectively handling multimodal data. The solution uses Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, providing a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Solution architecture

The mmRAG solution is based on a straightforward concept: to extract different data types separately, you generate text summarization using a VLM from different data types, embed text summaries along with raw data accordingly to a vector database, and store raw unstructured data in a document store. The query will prompt the LLM to retrieve relevant vectors from both the vector database and document store and generate meaningful and accurate answers.

The following diagram illustrates the solution architecture.

The architecture diagram depicts the mmRAG architecture that integrates advanced reasoning and retrieval mechanisms. It combines text, table, and image (including chart) data into a unified vector representation, enabling cross-modal understanding and retrieval. The process begins with diverse data extractions from various sources such as URLs and PDF files by parsing and preprocessing text, table, and image data types separately, while table data is converted into raw text and image data into captions.

These parsed data streams are then fed into a multimodal embedding model, which encodes the various data types into uniform, high dimensional vectors. The resulting vectors, representing the semantic content regardless of original format, are indexed in a vector database for efficient approximate similarity searches. When a query is received, the reasoning and retrieval component performs similarity searches across this vector space to retrieve the most relevant information from the vast integrated knowledge base.

The retrieved multimodal representations are then used by the generation component to produce outputs such as text, images, or other modalities. The VLM component generates vector representations specifically for textual data, further enhancing the system’s language understanding capabilities. Overall, this architecture facilitates advanced cross-modal reasoning, retrieval, and generation by unifying different data modalities into a common semantic space.

Developers can access mmRAG source codes on the GitHub repo.

Configure Amazon Bedrock with LangChain

You start by configuring Amazon Bedrock to integrate with various components from the LangChain Community library. This allows you to work with the core FMs. You use the BedrockEmbeddings class to create two different embedding models: one for text (embedding_bedrock_text) and one for images (embeddings_bedrock_image). These embeddings represent textual and visual data in a numerical format, which is essential for various natural language processing (NLP) tasks.

Additionally, you use the LangChain Bedrock and BedrockChat classes to create a VLM model instance (llm_bedrock_claude3_haiku) from Anthropic Claude 3 Haiku and a chat instance based on a different model, Sonnet (chat_bedrock_claude3_sonnet). These instances are used for advanced query reasoning, argumentation, and retrieval tasks. See the following code snippet:

from langchain_community.embeddings import BedrockEmbeddings
from langchain_community.chat_models.bedrock import BedrockChat

embedding_bedrock_text = BedrockEmbeddings(client=boto3_bedrock, model_id="amazon.titan-embed-g1-text-02")
embeddings_bedrock_image = BedrockEmbeddings(client=boto3_bedrock, model_id="amazon.titan-embed-image-v1")

model_kwargs =  { 
    "max_tokens": 2048,
    "temperature": 0.0,
    "top_k": 250,
    "top_p": 1,
    "stop_sequences": ["nnn"],
}
chat_bedrock_claude3_haiku = BedrockChat(
        model_id="anthropic:claude-3-haiku-20240307-v1:0", 
        client=boto3_bedrock,
        model_kwargs=model_kwargs,
    )
 
chat_bedrock_claude3_sonnet = BedrockChat(
        model_id="anthropic.claude-3-sonnet-20240229-v1:0", 
        client=boto3_bedrock,
        model_kwargs=model_kwargs,
    )

Parse content from data sources and embed both text and image data

In this section, we explore how to harness the power of Python to parse text, tables, and images from URLs and PDFs efficiently, using two powerful packages: Beautiful Soup and PyMuPDF. Beautiful Soup, a library designed for web scraping, makes it straightforward to sift through HTML and XML content, allowing you to extract the desired data from web pages. PyMuPDF offers an extensive set of functionalities for interacting with PDF files, enabling you to extract not just text but also tables and images with ease. See the following code:

from bs4 import BeautifulSoup as Soup
import fitz

def parse_tables_images_from_urls(url:str):
    ...
     # Parse the HTML content using BeautifulSoup
    soup = Soup(response.content, 'html.parser')

    # Find all table elements
    tables = soup.find_all('table')
    # Find all image elements
    images = soup.find_all('img')
    ...
 
def parse_images_tables_from_pdf(pdf_path:str):
    ...
    pdf_file = fitz.open(pdf_path)

    # Iterate through each page
    for page_index in range(len(pdf_file)): 
        # Select the page
        page = pdf_file[page_index]

        # Search for tables on the page
        tables = page.find_tables()
        df = table.to_pandas()
        
        # Search for images on the page
        images = page.get_images()
        image_info = pdf_file.extract_image(xref)
        image_data = image_info["image"]
       ...

The following code snippets demonstrate how to generate image captions using Anthropic Claude 3 by invoking the bedrock_get_img_description utility function. Additionally, they showcase how to embed image pixels along with image captioning using the Amazon Titan image embedding model amazon.titan_embeding_image_v1 by calling the get_text_embedding function.

image_caption = bedrock_get_img_description(model_id, 
            prompt='You are an expert at analyzing images in great detail. Your task is to carefully examine the provided 
                    mage and generate a detailed, accurate textual description capturing all of the important elements and 
                    context present in the image. Pay close attention to any numbers, data, or quantitative information visible, 
                    and be sure to include those numerical values along with their semantic meaning in your description. 
                    Thoroughly read and interpret the entire image before providing your detailed caption describing the 
                    image content in text format. Strive for a truthful and precise representation of what is depicted',
            image=image_byteio, 
            max_token=max_token, 
            temperature=temperature, 
            top_p=top_p, 
            top_k=top_k, 
            stop_sequences='Human:')    
            
image_sum_vectors = get_text_embedding(image_base64=image_base64, text_description=image_caption,  embd_model_id=embd_model_id)        

Embedding and vectorizing multimodality data

You can harness the capabilities of the newly released Anthropic Claude 3 Sonnet and Haiku on Amazon Bedrock, combined with the Amazon Titan image embedding model and LangChain. This powerful combination allows you to generate comprehensive text captions for tables and images, seamlessly integrating them into your content. Additionally, you can store vectors, objects, raw image file names, and source documents in an Amazon OpenSearch Serverless vector store and object store. Use the following code snippets to create image captions by invoking the utility function bedrock_get_img_description. Embed image pixels along with image captions using the Amazon Titan image embedding model amazon.titan_embeding_image_v1 by calling the get_text_embedding functions.

def get_text_embedding(image_base64=None, text_description=None,  embd_model_id:str="amazon.titan-embed-image-v1"):
    input_data = {}
    if image_base64 is not None:
        input_data["inputImage"] = image_base64
    if text_description is not None:
        input_data["inputText"] = text_description
    if not input_data:
        raise ValueError("At least one of image_base64 or text_description must be provided")
    body = json.dumps(input_data)
    response = boto3_bedrock.invoke_model(
        body=body,
        modelId=embd_model_id,
        accept="application/json",
        contentType="application/json"
    )
    response_body = json.loads(response.get("body").read())
    return response_body.get("embedding")
    
image_caption = bedrock_get_img_description(model_id, 
            prompt='You are an expert at analyzing images in great detail. Your task is to carefully examine the provided 
                    mage and generate a detailed, accurate textual description capturing all of the important elements and 
                    context present in the image. Pay close attention to any numbers, data, or quantitative information visible, 
                    and be sure to include those numerical values along with their semantic meaning in your description. 
                    Thoroughly read and interpret the entire image before providing your detailed caption describing the 
                    image content in text format. Strive for a truthful and precise representation of what is depicted',
            image=image_byteio, 
            max_token=max_token, 
            temperature=temperature, 
            top_p=top_p, 
            top_k=top_k, 
            stop_sequences='Human:')    
            
image_sum_vectors = get_text_embedding(image_base64=image_base64, text_description=image_sum,  embd_model_id=embd_model_id) 

You can consult the provided code examples for more information on how to embed multimodal and insert vector documents into the OpenSearch Serverless vector store. For more information about data access, refer to Data access control for Amazon OpenSearch Serverless.

# Form a data dictionary with image metatadata, raw image object store location and base64 encoded image data
document = {
    "doc_source": image_url,
    "image_filename": s3_image_path,
    "embedding": image_base64
}
# Parse out only the iamge name from the full temp path
filename = f"jsons/{image_path.split('/')[-1].split('.')[0]}.json"

# Writing the data dict into JSON data
with open(filename, 'w') as file:
    json.dump(document, file, indent=4)

#Load all json files from the temp directory  
loader = DirectoryLoader("./jsons", glob='**/*.json', show_progress=False, loader_cls=TextLoader)

#loader = DirectoryLoader("./jsons", glob='**/*.json', show_progress=True, loader_cls=JSONLoader, loader_kwargs = {'jq_schema':'.content'})
new_documents = loader.load()
new_docs = text_splitter.split_documents(new_documents)
   
# Insert into AOSS
new_docsearch = OpenSearchVectorSearch.from_documents(
    new_docs,
    bedrock_embeddings,
    opensearch_url=host,
    http_auth=auth,
    timeout = 100,
    use_ssl = True,
    verify_certs = True,
    connection_class = RequestsHttpConnection,
    index_name=new_index_name,
    engine="faiss",
)

Advanced RAG with fusion and decomposition

Fusion in RAG presents an innovative search strategy designed to transcend the limitations of conventional search techniques, aligning more closely with the complex nature of human inquiries. This initiative elevates the search experience by integrating multi-faceted query generation and using Reciprocal Rank Fusion for an enhanced re-ranking of search outcomes. This approach offers a more nuanced and effective way to navigate the vast expanse of available information, catering to the intricate and varied demands of users’ searches.

The following diagram illustrates this workflow.

We use the Anthropic Claude 3 Sonnet and Haiku models, which possess the capability to process visual and language data, which enables them to handle the query decomposition (Haiku) and answer fusion (Sonnet) stages effectively. The following code snippet demonstrates how to create a retriever using OpenSearch Serverless:

from langchain.vectorstores import OpenSearchVectorSearch
retriever  =  OpenSearchVectorSearch(
    opensearch_url = "{}.{}.aoss.amazonaws.com".format(<collection_id>, <my_region>),
    index_name = <index_name>,
    embedding_function = embd)

The combination of decomposition and fusion intend to address the limitations of the chain-of-thought (CoT) method in language models. It involves breaking down complex problems into simpler, sequential sub-problems, where each sub-problem builds upon the solution of the previous one. This technique significantly enhances the problem-solving abilities of language models in areas such as symbolic manipulation, compositional generalization, and mathematical reasoning.

The RAG-decomposition approach, which uses the decomposition step (see the following code), underscores the potential of a technique called least-to-most prompting. This technique not only improves upon existing methods but also paves the way for more advanced, interactive learning frameworks for language models. The ultimate goal is to move towards a future where language models can learn from bidirectional conversations, enabling more effective reasoning and problem-solving capabilities.

# Decomposition
prompt_rag = hub.pull("rlm/rag-prompt")
template = """You are a helpful assistant that generates multiple sub-questions related to an input question. n
The goal is to break down the input into a set of sub-problems / sub-questions that can be answers in isolation. n
Generate multiple search queries semantically related to: {question} n
Output (5 queries):"""
prompt_decomposition = ChatPromptTemplate.from_template(template)
generate_queries_decomposition = ( prompt_decomposition | llm_bedrock | StrOutputParser() | (lambda x: x.split("n")))
questions = generate_queries_decomposition.invoke({"question":question})

def reciprocal_rank_fusion(results: list[list], k=60):

    # Initialize a dictionary to hold fused scores for each unique document
    fused_scores = {}

    # Iterate through each list of ranked documents
    for docs in results:
        # Iterate through each document in the list, with its rank (position in the list)
        for rank, doc in enumerate(docs):
            # Convert the document to a string format to use as a key (assumes documents can be serialized to JSON)
            doc_str = dumps(doc)
            # If the document is not yet in the fused_scores dictionary, add it with an initial score of 0
            if doc_str not in fused_scores:
                fused_scores[doc_str] = 0
            # Retrieve the current score of the document, if any
            previous_score = fused_scores[doc_str]
            # Update the score of the document using the RRF formula: 1 / (rank + k)
            fused_scores[doc_str] += 1 / (rank + k)
    # Sort the documents based on their fused scores in descending order to get the final reranked results
    reranked_results = [
        (loads(doc), score)
        for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
    ]
    # Return the reranked results as a list of tuples, each containing the document and its fused score
    return reranked_results
    
def retrieve_and_rag(question,prompt_rag,sub_question_generator_chain):
    sub_questions = sub_question_generator_chain.invoke({"question":question})
    # Initialize a list to hold RAG chain results
    rag_results = []
    for sub_question in sub_questions:   
        # Retrieve documents for each sub-question with reciprocal reranking
        retrieved_docs = retrieval_chain_rag_fusion.invoke({"question": sub_question})
        # Use retrieved documents and sub-question in RAG chain
        answer = (prompt_rag 
            | chat_bedrock
            | StrOutputParser()
            | reciprocal_rank_fusion
            ).invoke({"context": retrieved_docs,"question": sub_question} 
        rag_results.append(answer)
    return rag_results,sub_questions
    
def format_qa_pairs(questions, answers):
    """Format Q and A pairs"""
    
    formatted_string = ""
    for i, (question, answer) in enumerate(zip(questions, answers), start=1):
        formatted_string += f"Question {i}: {question}nAnswer {i}: {answer}nn"
    return formatted_string.strip()

context = format_qa_pairs(questions, answers)

# Prompt
template = """Here is a set of Q+A pairs:

{context}

Use these to synthesize an answer to the question: {question}
"""
prompt_fusion = ChatPromptTemplate.from_template(template)
final_rag_chain = (prompt_fusion | llm_bedrock| StrOutputParser())

# Decompsing and reciprocal reranking
retrieval_chain_rag_fusion = generate_queries_decomposition | retriever.map() | reciprocal_rank_fusion
 
# Wrap the retrieval and RAG process in a RunnableLambda for integration into a chain
answers, questions = retrieve_and_rag(question, prompt_rag, generate_queries_decomposition)
final_rag_chain.invoke({"context":context,"question":question})

The RAG process is further enhanced by integrating a reciprocal re-ranker, which uses sophisticated NLP techniques. This makes sure the retrieved results are relevant and also semantically aligned with the user’s intended query. This multimodal retrieval approach seamlessly operates across vector databases and object stores, marking a significant advancement in the quest for more efficient, accurate, and contextually aware search mechanisms.

Multimodality retrievals

The mmRAG architecture enables the system to understand and process multimodal queries, retrieve relevant information from various sources, and generate multimodal answers by combining textual, tabular, and visual information in a unified manner. The following diagram highlights the data flows from queries to answers by using an advanced RAG and a multimodal retrieval engine powered by a multimodal embedding model (amazon.titan-embed-image-v1), an object store (Amazon S3), and a vector database (OpenSearch Serverless). For tables, the system retrieves relevant table locations and metadata, and computes the cosine similarity between the multimodal embedding and the vectors representing the table and its summary. Similarly, for images, the system retrieves relevant image locations and metadata, and computes the cosine similarity between the multimodal embedding and the vectors representing the image and its caption.

# Connect to the AOSS with given host and index name
docsearch = OpenSearchVectorSearch(
    index_name=index_name,  # TODO: use the same index-name used in the ingestion script
    embedding_function=bedrock_embeddings,
    opensearch_url=host,  # TODO: e.g. use the AWS OpenSearch domain instantiated previously
    http_auth=auth,
    timeout = 100,
    use_ssl = True,
    verify_certs = True,
    connection_class = RequestsHttpConnection,
    engine="faiss",
)

# Query for images with text
query = "What is the math and reasoning score MMMU (val) for Anthropic Claude 3 Sonnet ?"
t2i_results = docsearch.similarity_search_with_score(query, k=3)  # our search query  # return 3 most relevant docs

# Or Query AOSS with image aka image-to-image
with open(obj_image_path, "rb") as image_file:
    image_data = image_file.read()
    image_base64 = base64.b64encode(image_data).decode('utf8')
    image_vectors = get_image_embedding(image_base64=image_base64)
    i2i_results = docsearch.similarity_search_with_score_by_vector(image_vectors, k=3)  # our search query  # return 3 most relevant docs

The following screenshot illustrates the improved accuracy and comprehensive understanding of the user’s query with multimodality capability. The mmRAG approach is capable of grasping the intent behind the query, extracting relevant information from the provided chart, and estimating the overall costs, including the estimated output token size. Furthermore, it can perform mathematical calculations to determine the cost difference. The output includes the source chart and a link to its original location.

Use cases and limitations

Amazon Bedrock offers a comprehensive set of generative AI models for enhancing content comprehension across various modalities. By using the latest advancements in VLMs, such as Anthropic Claude 3 Sonnet and Haiku, as well as the Amazon Titan image embedding model, Amazon Bedrock enables you to expand your document understanding beyond text to include tables, charts, and images. The integration of OpenSearch Serverless provides enterprise-grade vector storage and approximate k-NN search capabilities, enabling efficient retrieval of relevant information. With advanced LangChain decomposition and fusion techniques, you can use multi-step querying across different LLMs to improve accuracy and gain deeper insights. This powerful combination of cutting-edge technologies allows you to unlock the full potential of multimodal content comprehension, enabling you to make informed decisions and drive innovation across various data sources.

The reliance on visual language models and image embedding models for comprehensive and accurate image captions has its limitations. Although these models excel at understanding visual and textual data, the multi-step query decomposition, reciprocal ranking, and fusion processes involved can lead to increased inference latency. This makes such solutions less suitable for real-time applications or scenarios that demand instantaneous responses. However, these solutions can be highly beneficial in use cases where higher accuracy and less time-sensitive responses are required, allowing for more detailed and accurate analysis of complex visual and textual data.

Conclusion

In this post, we discussed how you can use multimodal RAG to address limitations in multimodal generative AI assistants. We invite you to explore mmRAG and take advantage of the advanced features of Amazon Bedrock. These powerful tools can assist your business in gaining deeper insights, making well-informed decisions, and fostering innovation driven by more accurate data. Ongoing research efforts are focused on developing an agenic and graph-based pipeline to streamline the processes of parsing, injection, and retrieval. These approaches hold the promise of enhancing the reliability and reusability of the mmRAG system.

Acknowledgement

Authors would like to expression sincere gratitude to Nausheen Sayed, Karen Twelves, Li Zhang, Sophia Shramko, Mani Khanuja, Santhosh Kuriakose, and Theresa Perkins for their comprehensive reviews.

About the Authors

Alfred Shen is a Senior AI/ML Specialist at AWS. He has been working in Silicon Valley, holding technical and managerial positions in diverse sectors including healthcare, finance, and high-tech. He is a dedicated applied AI/ML researcher, concentrating on CV, NLP, and multimodality. His work has been showcased in publications such as EMNLP, ICLR, and Public Health.

Changsha Ma is an generative AI Specialist at AWS. She is a technologist with a PhD in Computer Science, a master’s degree in Education Psychology, and years of experience in data science and independent consulting in AI/ML. She is passionate about researching methodological approaches for machine and human intelligence. Outside of work, she loves hiking, cooking, hunting food, mentoring college students for entrepreneurship, and spending time with friends and families.

Julianna Delua is a Principal Specialist for AI/ML and generative AI. She serves the financial services industry customers including those in Capital Markets, Fintech and Payments. Julianna enjoys helping businesses turn new ideas into solutions and transform the organizations with AI-powered solutions.

Read More

This post is co-written with Aurélien Capdecomme and Bertrand d’Aure from 20 Minutes.

With 19 million monthly readers, 20 Minutes is a major player in the French media landscape. The media organization delivers useful, relevant, and accessible information to an audience that consists primarily of young and active urban readers. Every month, nearly 8.3 million 25–49-year-olds choose 20 Minutes to stay informed. Established in 2002, 20 Minutes consistently reaches more than a third (39 percent) of the French population each month through print, web, and mobile platforms.

As 20 Minutes’s technology team, we’re responsible for developing and operating the organization’s web and mobile offerings and driving innovative technology initiatives. For several years, we have been actively using machine learning and artificial intelligence (AI) to improve our digital publishing workflow and to deliver a relevant and personalized experience to our readers. With the advent of generative AI, and in particular large language models (LLMs), we have now adopted an AI by design strategy, evaluating the application of AI for every new technology product we develop.

One of our key goals is to provide our journalists with a best-in-class digital publishing experience. Our newsroom journalists work on news stories using Storm, our custom in-house digital editing experience. Storm serves as the front end for Nova, our serverless content management system (CMS). These applications are a focus point for our generative AI efforts.

In 2023, we identified several challenges where we see the potential for generative AI to have a positive impact. These include new tools for newsroom journalists, ways to increase audience engagement, and a new way to ensure advertisers can confidently assess the brand safety of our content. To implement these use cases, we rely on Amazon Bedrock.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon Web Services (AWS) through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. We dive into the technical aspects of our implementation and explain our decision to choose Amazon Bedrock as our foundation model provider.

Identifying challenges and use cases

Today’s fast-paced news environment presents both challenges and opportunities for digital publishers. At 20 Minutes, a key goal of our technology team is to develop new tools for our journalists that automate repetitive tasks, improve the quality of reporting, and allow us to reach a wider audience. Based on this goal, we have identified three challenges and corresponding use cases where generative AI can have a positive impact.

The first use case is to use automation to minimize the repetitive manual tasks that journalists perform as part of the digital publishing process. The core work of developing a news story revolves around researching, writing, and editing the article. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles.

While these tasks can feel like a chore, they are critical to search engine optimization (SEO) and therefore the audience reach of the article. If we can automate some of these repetitive tasks, this use case has the potential to free up time for our newsroom to focus on core journalistic work while increasing the reach of our content.

The second use case is how we republish news agency dispatches at 20 Minutes. Like most news outlets, 20 Minutes subscribes to news agencies, such as the Agence France-Presse (AFP) and others, that publish a feed of news dispatches covering national and international news. 20 Minutes journalists select stories relevant to our audience and rewrite, edit, and expand on them to fit the editorial standards and unique tone our readership is used to. Rewriting these dispatches is also necessary for SEO, as search engines rank duplicate content low. Because this process follows a repeatable pattern, we decided to build an AI-based tool to simplify the republishing process and reduce the time spent on it.

The third and final use case we identified is to improve transparency around the brand safety of our published content. As a digital publisher, 20 Minutes is committed to providing a brand-safe environment for potential advertisers. Content can be classified as brand-safe or not brand-safe based on its appropriateness for advertising and monetization. Depending on the advertiser and brand, different types of content might be considered appropriate. For example, some advertisers might not want their brand to appear next to news content about sensitive topics such as military conflicts, while others might not want to appear next to content about drugs and alcohol.

Organizations such as the Interactive Advertising Bureau (IAB) and the Global Alliance for Responsible Media (GARM) have developed comprehensive guidelines and frameworks for classifying the brand safety of content. Based on these guidelines, data providers such as the IAB and others conduct automated brand safety assessments of digital publishers by regularly crawling websites such as 20minutes.fr and calculating a brand safety score.

However, this brand safety score is site-wide and doesn’t break down the brand safety of individual news articles. Given the reasoning capabilities of LLMs, we decided to develop an automated per-article brand safety assessment based on industry-standard guidelines to provide advertisers with a real-time, granular view of the brand safety of 20 Minutes content.

Our technical solution

At 20 Minutes, we’ve been using AWS since 2017, and we aim to build on top of serverless services whenever possible.

The digital publishing frontend application Storm is a single-page application built using React and Material Design and deployed using Amazon Simple Storage Service (Amazon S3) and Amazon CloudFront. Our CMS backend Nova is implemented using Amazon API Gateway and several AWS Lambda functions. Amazon DynamoDB serves as the primary database for 20 Minutes articles. New articles and changes to existing articles are captured using DynamoDB Streams, which invokes processing logic in AWS Step Functions and feeds our search service based on Amazon OpenSearch.

We integrate Amazon Bedrock using AWS PrivateLink, which allows us to create a private connection between our Amazon Virtual Private Cloud (VPC) and Amazon Bedrock without traversing the public internet.

When working on articles in Storm, journalists have access to several AI tools implemented using Amazon Bedrock. Storm is a block-based editor that allows journalists to combine multiple blocks of content, such as title, lede, text, image, social media quotes, and more, into a complete article. With Amazon Bedrock, journalists can use AI to generate an article summary suggestion block and place it directly into the article. We use a single-shot prompt with the full article text in context to generate the summary.

Storm CMS also gives journalists suggestions for article metadata. This includes recommendations for appropriate categories, tags, and even in-text links. These references to other 20 Minutes content are critical to increasing audience engagement, as search engines rank content with relevant internal and external links higher.

To implement this, we use a combination of Amazon Comprehend and Amazon Bedrock to extract the most relevant terms from an article’s text and then perform a search against our internal taxonomic database in OpenSearch. Based on the results, Storm provides several suggestions of terms that should be linked to other articles or topics, which users can accept or reject.

News dispatches become available in Storm as soon as we receive them from our partners such as AFP. Journalists can browse the dispatches and select them for republication on 20minutes.fr. Every dispatch is manually reworked by our journalists before publication. To do so, journalists first invoke a rewrite of the article by an LLM using Amazon Bedrock. For this, we use a low-temperature single-shot prompt that instructs the LLM not to reinterpret the article during the rewrite, and to keep the word count and structure as similar as possible. The rewritten article is then manually edited by a journalist in Storm like any other article.

To implement our new brand safety feature, we process every new article published on 20minutes.fr. Currently, we use a single shot prompt that includes both the article text and the IAB brand safety guidelines in context to get a sentiment assessment from the LLM. We then parse the response, store the sentiment, and make it publicly available for each article to be accessed by ad servers.

Lessons learned and outlook

When we started working on generative AI use cases at 20 Minutes, we were surprised at how quickly we were able to iterate on features and get them into production. Thanks to the unified Amazon Bedrock API, it’s easy to switch between models for experimentation and find the best model for each use case.

For the use cases described above, we use Anthropic’s Claude in Amazon Bedrock as our primary LLM because of its overall high quality and, in particular, its quality in recognizing French prompts and generating French completions. Because 20 Minutes content is almost exclusively French, these multilingual capabilities are key for us. We have found that careful prompt engineering is a key success factor and we closely adhere to Anthropic’s prompt engineering resources to maximize completion quality.

Even without relying on approaches like fine-tuning or retrieval-augmented generation (RAG) to date, we can implement use cases that deliver real value to our journalists. Based on data collected from our newsroom journalists, our AI tools save them an average of eight minutes per article. With around 160 pieces of content published every day, this is already a significant amount of time that can now be spent reporting the news to our readers, rather than performing repetitive manual tasks.

The success of these use cases depends not only on technical efforts, but also on close collaboration between our product, engineering, newsroom, marketing, and legal teams. Together, representatives from these roles make up our AI Committee, which establishes clear policies and frameworks to ensure the transparent and responsible use of AI at 20 Minutes. For example, every use of AI is discussed and approved by this committee, and all AI-generated content must undergo human validation before being published.

We believe that generative AI is still in its infancy when it comes to digital publishing, and we look forward to bringing more innovative use cases to our platform this year. We’re currently working on deploying fine-tuned LLMs using Amazon Bedrock to accurately match the tone and voice of our publication and further improve our brand safety analysis capabilities. We also plan to use Bedrock models to tag our existing image library and provide automated suggestions for article images.

Why Amazon Bedrock?

Based on our evaluation of several generative AI model providers and our experience implementing the use cases described above, we selected Amazon Bedrock as our primary provider for all our foundation model needs. The key reasons that influenced this decision were:

1. Choice of models: The market for generative AI is evolving rapidly, and the AWS approach of working with multiple leading model providers ensures that we have access to a large and growing set of foundational models through a single API.
2. Inference performance: Amazon Bedrock delivers low-latency, high-throughput inference. With on-demand and provisioned throughput, the service can consistently meet all of our capacity needs.
3. Private model access: We use AWS PrivateLink to establish a private connection to Amazon Bedrock endpoints without traversing the public internet, ensuring that we maintain full control over the data we send for inference.
4. Integration with AWS services: Amazon Bedrock is tightly integrated with AWS services such as AWS Identity and Access Management (IAM) and the AWS Software Development Kit (AWS SDK). As a result, we were able to quickly integrate Bedrock into our existing architecture without having to adapt any new tools or conventions.

Conclusion and outlook

In this blog post, we described how 20 Minutes is using generative AI on Amazon Bedrock to empower our journalists in the newsroom, reach a broader audience, and make brand safety transparent to our advertisers. With these use cases, we’re using generative AI to bring more value to our journalists today, and we’ve built a foundation for promising new AI use cases in the future.

To learn more about Amazon Bedrock, start with Amazon Bedrock Resources for documentation, blog posts, and more customer success stories.

About the authors

Aurélien Capdecomme is the Chief Technology Officer at 20 Minutes, where he leads the IT development and infrastructure teams. With over 20 years of experience in building efficient and cost-optimized architectures, he has a strong focus on serverless strategy, scalable applications and AI initiatives. He has implemented innovation and digital transformation strategies at 20 Minutes, overseeing the complete migration of digital services to the cloud.

Bertrand d’Aure is a software developer at 20 Minutes. An engineer by training, he designs and implements the backend of 20 Minutes applications, with a focus on the software used by journalists to create their stories. Among other things, he is responsible for adding generative AI features to the software to simplify the authoring process.

Dr. Pascal Vogel is a Solutions Architect at Amazon Web Services. He collaborates with enterprise customers across EMEA to build cloud-native solutions with a focus on serverless and generative AI. As a cloud enthusiast, Pascal loves learning new technologies and connecting with like-minded customers who want to make a difference in their cloud journey.

Read More

In the rapidly evolving landscape of artificial intelligence (AI), the rise of generative AI models has ushered in a new era of personalized and intelligent experiences. Organizations are increasingly using the power of these language models to drive innovation and enhance their services, from natural language processing to content generation and beyond.

Using generative AI models in the enterprise environment, however, requires taming their intrinsic power and enhancing their skills to address specific customer needs. In cases where an out-of-the-box model is missing knowledge of domain- or organization-specific terminologies, a custom fine-tuned model, also called a domain-specific large language model (LLM), might be an option for performing standard tasks in that domain or micro-domain. BloombergGPT is an example of LLM that was trained from scratch to have a better understanding of highly specialized vocabulary found in the financial domain. In the same sense, domain specificity can be addressed through fine-tuning at a smaller scale. Customers are fine-tuning generative AI models based on domains including finance, sales, marketing, travel, IT, HR, finance, procurement, healthcare and life sciences, customer service, and many more. Additionally, independent software vendors (ISVs) are building secure, managed, multi-tenant, end-to-end generative AI platforms with models that are customized and personalized based on their customer’s datasets and domains. For example, Forethought introduced SupportGPT, a generative AI platform for customer support.

As the demands for personalized and specialized AI solutions grow, businesses often find themselves grappling with the challenge of efficiently managing and serving a multitude of fine-tuned models across diverse use cases and customer segments. With the need to serve a wide range of AI-powered use cases, from resume parsing and job skill matching, domain-specific to email generation and natural language understanding, these businesses are often left with the daunting task of managing hundreds of fine-tuned models, each tailored to specific customer needs or use cases. The complexities of this challenge are compounded by the inherent scalability and cost-effectiveness concerns that come with deploying and maintaining such a diverse model ecosystem. Traditional approaches to model serving can quickly become unwieldy and resource intensive, leading to increased infrastructure costs, operational overhead, and potential performance bottlenecks.

Fine-tuning enormous language models is prohibitively expensive in terms of the hardware required and the storage and switching cost for hosting independent instances for different tasks. LoRA (Low-Rank Adaptation) is an efficient adaptation strategy that neither introduces inference latency nor reduces input sequence length while retaining high model quality. Importantly, it allows for quick task switching when deployed as a service by sharing the vast majority of the model parameters.

In this post, we explore a solution that addresses these challenges head-on using LoRA serving with Amazon SageMaker. By using the new performance optimizations of LoRA techniques in SageMaker large model inference (LMI) containers along with inference components, we demonstrate how organizations can efficiently manage and serve their growing portfolio of fine-tuned models, while optimizing costs and providing seamless performance for their customers.

The latest SageMaker LMI container offers unmerged-LoRA inference, sped up with our LMI-Dist inference engine and OpenAI style chat schema. To learn more about LMI, refer to LMI Starting Guide, LMI handlers Inference API Schema, and Chat Completions API Schema.

New LMI features for serving LoRA adapters at scale on SageMaker

There are two kinds of LoRA that can be put onto various engines:

• Merged LoRA – This applies the adapter by modifying the base model in place. It has zero added latency while running, but has a cost to apply or unapply the merge. It works best for cases with only a few adapters. It is best for single-adapter batches, and doesn’t support multi-adapter batches.
• Unmerged LoRA – This alters the model operators to factor in the adapters without changing the base model. It has a higher inference latency for the additional adapter operations. However, it does support multi-adapter batches. It works best for use cases with a large number of adapters.

The new LMI container offers out-of-box integration and abstraction with SageMaker for hosting multiple unmerged LoRA adapters with higher performance (low latency and high throughput) using the vLLM backend LMI-Dist backend that uses vLLM, which in-turn uses S-LORA and Punica. The LMI container offers two backends for serving LoRA adapters: the LMI-Dist backend (recommended) and the vLLM Backend. Both backends are based on the open source vLLM library for serving LoRA adapters, but the LMI-Dist backend provides additional optimized continuous (rolling) batching implementation. You are not required to configure these libraries separately; the LMI container provides the higher-level abstraction through the vLLM and LMI-Dist backends. We recommend you start with the LMI-Dist backend because it has additional performance optimizations related to continuous (rolling) batching.

S-LoRA stores all adapters in the main memory and fetches the adapters used by the currently running queries to the GPU memory. To efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes unified paging. Unified paging uses a unified memory pool to manage dynamic adapter weights with different ranks and KV cache tensors with varying sequence lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogeneous batching of LoRA computation. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead.

Punica is designed to efficiently serve multiple LoRA models on a shared GPU cluster. It achieves this by following three design guidelines:

• Consolidating multi-tenant LoRA serving workloads to a small number of GPUs to increase overall GPU utilization
• Enabling batching for different LoRA models to improve performance and GPU utilization
• Focusing on the decode stage performance, which is the predominant factor in the cost of model serving

Punica uses a new CUDA kernel design called Segmented Gather Matrix-Vector Multiplication (SGMV) to batch GPU operations for concurrent runs of multiple LoRA models, significantly improving GPU efficiency in terms of memory and computation. Punica also implements a scheduler that routes requests to active GPUs and migrates requests for consolidation, optimizing GPU resource allocation. Overall, Punica achieves high throughput and low latency in serving multi-tenant LoRA models on a shared GPU cluster. For more information, read the Punica whitepaper.

The following figure shows the multi LoRA adapter serving stack of the LMI container on SageMaker.

As shown in the preceding figure, the LMI container provides the higher-level abstraction through the vLLM and LMI-Dist backends to serve LoRA adapters at scale on SageMaker. As a result, you’re not required to configure the underlying libraries (S-LORA, Punica, or vLLM) separately. However, there might be cases where you want to control some of the performance driving parameters depending on your use case and application performance requirements. The following are the common configuration options the LMI container provides to tune LoRA serving. For more details on configuration options specific to each backend, refer to vLLM Engine User Guide and LMI-Dist Engine User Guide.

option.enable_lora: This config enables support for LoRA adapters.
option.max_loras: This config determines the maximum number of LoRA adapters that can be run at once. Allocates GPU memory for those number adapters.
option.max_lora_rank: This config determines the maximum rank allowed for a LoRA adapter. Set this value to maximum rank of your adapters. Setting a larger value will enable more adapters at a greater memory usage cost.
option.lora_extra_vocab_size: This config determines the maximum additional vocabulary that can be added through a LoRA adapter.
option.max_cpu_loras: This config determines the maximum number of LoRA adapters to cache in memory. All others will be evicted to disk.

Design patterns for serving fine-tuned LLMs at scale

Enterprises grappling with the complexities of managing generative AI models often encounter scenarios where a robust and flexible design pattern is crucial. One common use case involves a single base model with multiple LoRA adapters, each tailored to specific customer needs or use cases. This approach allows organizations to use a foundational language model while maintaining the agility to fine-tune and deploy customized versions for their diverse customer base.

Single-base model with multiple fine-tuned LoRA adapters

An enterprise offering a resume parsing and job skill matching service may use a single high-performance base model, such as Mistral 7B. The Mistral 7B base model is particularly well-suited for job-related content generation tasks, such as creating personalized job descriptions and tailored email communications. Mistral’s strong performance in natural language generation and its ability to capture industry-specific terminology and writing styles make it a valuable asset for such an enterprise’s customers in the HR and recruitment space. By fine-tuning Mistral 7B with LoRA adapters, enterprises can make sure the generated content aligns with the unique branding, tone, and requirements of each customer, delivering a highly personalized experience.

Multi-base models with multiple fine-tuned LoRA adapters

On the other hand, the same enterprise may use the Llama 3 base model for more general natural language processing tasks, such as resume parsing, skills extraction, and candidate matching. Llama 3’s broad knowledge base and robust language understanding capabilities enable it to handle a wide range of documents and formats, making sure their services can effectively process and analyze candidate information, regardless of the source. By fine-tuning Llama 3 with LoRA adapters, such enterprises can tailor the model’s performance to specific customer requirements, such as regional dialects, industry-specific terminology, or unique data formats. By employing a multi-base model, multi-adapter design pattern, enterprises can take advantage of the unique strengths of each language model to deliver a comprehensive and highly personalized job profile to a candidate resume matching service. This approach allows enterprises to cater to the diverse needs of their customers, making sure each client receives tailored AI-powered solutions that enhance their recruitment and talent management processes.

Effectively implementing and managing these design patterns, where multiple base models are coupled with numerous LoRA adapters, is a key challenge that enterprises must address to unlock the full potential of their generative AI investments. A well-designed and scalable approach to model serving is crucial in delivering cost-effective, high-performance, and personalized experiences to customers.

Solution overview

The following sections outline the coding steps to deploy a base LLM, TheBloke/Llama-2-7B-Chat-fp16, with LoRA adapters on SageMaker. It involves preparing a compressed archive with the base model files and LoRA adapter files, uploading it to Amazon Simple Storage Service (Amazon S3), selecting and configuring the SageMaker LMI container to enable LoRA support, creating a SageMaker endpoint configuration and endpoint, defining an inference component for the model, and sending inference requests specifying different LoRA adapters like Spanish (“es”) and French (“fr”) in the request payload to use those fine-tuned language capabilities. For more information on deploying models using SageMaker inference components, see Amazon SageMaker adds new inference capabilities to help reduce foundation model deployment costs and latency.

To showcase multi-base models with their LoRA adapters, we add another base model, mistralai/Mistral-7B-v0.1, and its LoRA adapter to the same SageMaker endpoint, as shown in the following diagram.

Prerequisites

You need to complete some prerequisites before you can run the notebook:

• Have a Hugging Face user access token and authorization to access to mistralai/Mistral-7B-v0.1 model
• Have a SageMaker quota for one ml.g5.12xlarge instance for endpoint usage

Upload your LoRA adapters to Amazon S3

To prepare the LoRA adapters, create a adapters.tar.gz compressed archive containing the LoRA adapters directory. The adapters directory should contain subdirectories for each of the LoRA adapters, with each adapter subdirectory containing the adapter_model.bin file (the adapter weights) and the adapter_config.json file (the adapter configuration). We typically obtain these adapter files by using the PeftModel.save_pretrained() method from the Peft library. After you assemble the adapters directory with the adapter files, you compress it into a adapters.tar.gz archive and upload it to an S3 bucket for deployment or sharing. We include the LoRA adapters in the adapters directory as follows:

|- model_dir
    |- adapters/
        |--- <adapter_1>/
        |--- <adapter_2>/
        |--- ...
        |--- <adapter_n>/

Download LoRA adapters, compress them, and upload the compressed file to Amazon S3:

snapshot_download("UnderstandLing/llama-2-7b-chat-es", local_dir="llama-lora-multi-adapter/adapters/es", local_dir_use_symlinks=False)
snapshot_download("UnderstandLing/llama-2-7b-chat-fr", local_dir="llama-lora-multi-adapter/adapters/fr", local_dir_use_symlinks=False)
snapshot_download("UnderstandLing/llama-2-7b-chat-ru", local_dir="llama-lora-multi-adapter/adapters/ru", local_dir_use_symlinks=False)
!tar czvf adapters.tar.gz -C llama-lora-multi-adapter .
s3_code_artifact_accelerate = sess.upload_data("adapters.tar.gz", model_bucket, s3_code_prefix)

Select and LMI container and configure LMI to enable LoRA

SageMaker provides optimized containers for LMI that support different frameworks for model parallelism, allowing the deployment of LLMs across multiple GPUs. For this post, we employ the DeepSpeed container, which encompasses frameworks such as DeepSpeed and vLLM, among others. See the following code:

deepspeed_image_uri = image_uris.retrieve(
    framework="djl-deepspeed",
    region=sess.boto_session.region_name,
    version="0.27.0"
)

env_generation = {"OPTION_MODEL_ID": "TheBloke/Llama-2-7B-Chat-fp16",
                  "OPTION_TRUST_REMOTE_CODE": "true",
                  "OPTION_TENSOR_PARALLEL_DEGREE": "2",
                  "OPTION_ROLLING_BATCH": "lmi-dist",
                  "OPTION_MAX_ROLLING_BATCH_SIZE": "32",
                  "OPTION_DTYPE": "fp16",
                  "OPTION_ENABLE_LORA": "true",
                  "OPTION_GPU_MEMORY_UTILIZATION": "0.8",
                  "OPTION_MAX_LORA_RANK": "64",
                  "OPTION_MAX_CPU_LORAS": "4"
                 }

Create a SageMaker endpoint configuration

Create an endpoint configuration using the appropriate instance type. Set ContainerStartupHealthCheckTimeoutInSeconds to account for the time taken to download the LLM weights from Amazon S3 or the model hub, and the time taken to load the model on the GPUs:

endpoint_config_response = sm_client.create_endpoint_config(
    EndpointConfigName=endpoint_config_name,
    ExecutionRoleArn=role,
    ProductionVariants=[
        {
            "VariantName": variant_name,
            "InstanceType": instance_type,
            "InitialInstanceCount": initial_instance_count,
            "ModelDataDownloadTimeoutInSeconds": model_data_download_timeout_in_seconds,
            "ContainerStartupHealthCheckTimeoutInSeconds": container_startup_health_check_timeout_in_seconds,
            "ManagedInstanceScaling": {
                "Status": "ENABLED",
                "MinInstanceCount": initial_instance_count,
                "MaxInstanceCount": max_instance_count,
            },
            "RoutingConfig": {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'
            },
        },
    ],
)

Create a SageMaker endpoint

Create a SageMaker endpoint based on the endpoint configuration defined in the previous step. You use this endpoint for hosting the inference component (model) inference and make invocations.

create_endpoint_response = sm_client.create_endpoint(
    EndpointName=f"{endpoint_name}", EndpointConfigName=endpoint_config_name
)

Create a SageMaker inference component (model)

Now that you have created a SageMaker endpoint, let’s create our model as an inference component. The SageMaker inference component enables you to deploy one or more foundation models (FMs) on the same SageMaker endpoint and control how many accelerators and how much memory is reserved for each FM. See the following code:

model_name = sagemaker.utils.name_from_base("lmi-llama2-7b")
print(model_name)

create_model_response = sm_client.create_model(
    ModelName=model_name,
    ExecutionRoleArn=role,
    PrimaryContainer={
        "Image": inference_image_uri,
        "Environment": env_generation,
        "ModelDataUrl": s3_code_artifact_accelerate,
    }
)

prefix = sagemaker.utils.unique_name_from_base("lmi-llama2-7b")
inference_component_name = f"{prefix}-inference-component"

sm_client.create_inference_component(
    InferenceComponentName=inference_component_name,
    EndpointName=endpoint_name,
    VariantName=variant_name,
    Specification={
        "ModelName": model_name,
        # "Container": {
        #     "Image": inference_image_uri,
        #     "ArtifactUrl": s3_code_artifact,
        # },
        "StartupParameters": {
            "ModelDataDownloadTimeoutInSeconds": 1200,
            "ContainerStartupHealthCheckTimeoutInSeconds": 1200,
        },
        "ComputeResourceRequirements": {
            "NumberOfAcceleratorDevicesRequired": 2,
            "MinMemoryRequiredInMb": 7*2*1024,
        },
    },
    RuntimeConfig={"CopyCount": 1},
)

Make inference requests using different LoRA adapters

With the endpoint and inference model ready, you can now send requests to the endpoint using the LoRA adapters you fine-tuned for Spanish and French languages. The specific LoRA adapter is specified in the request payload under the "adapters" field. We use "es" for the Spanish language adapter and "fr" for the French language adapter, as shown in the following code:

# Testing Spanish (es) adapter
response_model = smr_client.invoke_endpoint(
    InferenceComponentName=inference_component_name,
    EndpointName=endpoint_name,
    Body=json.dumps({"inputs": ["Piensa en una excusa creativa para decir que no necesito ir a la fiesta."],
                     "adapters": ["es"]}),
    ContentType="application/json",
)

response_model["Body"].read().decode("utf8")

# Testing French (fr) adapter
response_model = smr_client.invoke_endpoint(
    InferenceComponentName=inference_component_name,
    EndpointName=endpoint_name,
    Body=json.dumps({"inputs": ["Pensez à une excuse créative pour dire que je n'ai pas besoin d'aller à la fête."],
                     "adapters": ["fr"]}),
    ContentType="application/json",
)

response_model["Body"].read().decode("utf8")

# Testing Russian (ru) adapter
response_model = smr_client.invoke_endpoint(
    InferenceComponentName=inference_component_name,
    EndpointName=endpoint_name,
    Body=json.dumps({"inputs": ["Придумайте креативное "],
                     "parameters": params,
                     "adapters": ["ru"]}),
    ContentType="application/json",
)

response_model["Body"].read().decode("utf8")

Add another base model and inference component and its LoRA adapter

Let’s add another base model and its LoRA adapter to the same SageMaker endpoint for multi-base models with multiple fine-tuned LoRA adapters. The code is very similar to the previous code for creating the Llama base model and its LoRA adapter.

Configure the SageMaker LMI container to host the base model (mistralai/Mistral-7B-v0.1) and its LoRA adapter (mistral-lora-multi-adapter/adapters/fr):

deepspeed_image_uri = image_uris.retrieve(
    framework="djl-deepspeed",
    region=sess.boto_session.region_name,
    version="0.27.0"
)

my_hf_token = "<YOUR_HuggingFacePersonalAccessToken_HERE>"

env_generation = {"HF_TOKEN": my_hf_token,
                  "OPTION_MODEL_ID": "mistralai/Mistral-7B-v0.1",
                  "OPTION_TRUST_REMOTE_CODE": "true",
                  "OPTION_TENSOR_PARALLEL_DEGREE": "2",
                  "OPTION_ENABLE_LORA": "true",
                  "OPTION_GPU_MEMORY_UTILIZATION": "0.8",
                  "OPTION_MAX_LORA_RANK": "64",
                  "OPTION_MAX_CPU_LORAS": "4"
                 }

Create a new SageMaker model and inference component for the base model (mistralai/Mistral-7B-v0.1) and its LoRA adapter (mistral-lora-multi-adapter/adapters/fr):

model_name2 = sagemaker.utils.name_from_base("lmi-mistral-7b")

create_model_response = sm_client.create_model(
    ModelName=model_name2,
    ExecutionRoleArn=role,
    PrimaryContainer={
        "Image": inference_image_uri,
        "Environment": env,
        "ModelDataUrl": s3_code_artifact_accelerate,
    }
)

sm_client.create_inference_component(
    InferenceComponentName=inference_component_name2,
    EndpointName=endpoint_name,
    VariantName=variant_name,
    Specification={
        "ModelName": model_name2,
        # "Container": {
        #     "Image": inference_image_uri,
        #     "ArtifactUrl": s3_code_artifact,
        # },
        "StartupParameters": {
            "ModelDataDownloadTimeoutInSeconds": 3600,
            "ContainerStartupHealthCheckTimeoutInSeconds": 1200,
        },
        "ComputeResourceRequirements": {
            "NumberOfAcceleratorDevicesRequired": 2,
            "MinMemoryRequiredInMb": 7*2*1024,
        },
    },
    RuntimeConfig={"CopyCount": 1},
)

Invoke the same SageMaker endpoint for the newly created inference component for the base model (mistralai/Mistral-7B-v0.1) and its LoRA adapter (mistral-lora-multi-adapter/adapters/fr):

# Testing French (fr) adapter
response_model = smr_client.invoke_endpoint(
    InferenceComponentName=inference_component_name2,
    EndpointName=endpoint_name,
    Body=json.dumps({"inputs": ["Pensez à une excuse créative pour dire que je n'ai pas besoin d'aller à la fête."],
                     "adapters": ["fr"]}),
    ContentType="application/json",
)

response_model["Body"].read().decode("utf8")

Clean up

Delete the SageMaker inference components, models, endpoint configuration, and endpoint to avoid incurring unnecessary costs:

sm_client.delete_inference_component(InferenceComponentName=inference_component_name)
sm_client.delete_inference_component(InferenceComponentName=inference_component_name2)
sm_client.delete_endpoint(EndpointName=endpoint_name)
sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
sm_client.delete_model(ModelName=model_name)
sm_client.delete_model(ModelName=model_name2)

Conclusion

The ability to efficiently manage and serve a diverse portfolio of fine-tuned generative AI models is paramount if you want your organization to deliver personalized and intelligent experiences at scale in today’s rapidly evolving AI landscape. With the inference capabilities of SageMaker LMI coupled with the performance optimizations of LoRA techniques, you can overcome the challenges of multi-tenant fine-tuned LLM serving. This solution enables you to consolidate AI workloads, batch operations across multiple models, and optimize resource utilization for cost-effective, high-performance delivery of tailored AI solutions to your customers. As demand for specialized AI experiences continues to grow, we’ve shown how the scalable infrastructure and cutting-edge model serving techniques of SageMaker position AWS as a powerful platform for unlocking generative AI’s full potential. To start exploring the benefits of this solution for yourself, we encourage you to use the code example and resources we’ve provided in this post.

About the authors

Michael Nguyen is a Senior Startup Solutions Architect at AWS, specializing in leveraging AI/ML to drive innovation and develop business solutions on AWS. Michael holds 12 AWS certifications and has a BS/MS in Electrical/Computer Engineering and an MBA from Penn State University, Binghamton University, and the University of Delaware.

Dhawal Patel is a Principal Machine Learning Architect at AWS. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence. He focuses on Deep learning including NLP and Computer Vision domains. He helps customers achieve high performance model inference on SageMaker.

Vivek Gangasani is a AI/ML Startup Solutions Architect for Generative AI startups at AWS. He helps emerging GenAI startups build innovative solutions using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference performance of Large Language Models. In his free time, Vivek enjoys hiking, watching movies and trying different cuisines.

Qing Lan is a Software Development Engineer in AWS. He has been working on several challenging products in Amazon, including high performance ML inference solutions and high performance logging system. Qing’s team successfully launched the first Billion-parameter model in Amazon Advertising with very low latency required. Qing has in-depth knowledge on the infrastructure optimization and Deep Learning acceleration.

Read More