AWS Big Data Blog

Enrich your serverless data lake with Amazon Bedrock

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset. For many organizations, this centralized data store follows a data lake architecture.  Although data lakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging. End-users often struggle to find relevant information buried within extensive documents housed in data lakes, leading to inefficiencies and missed opportunities.

Surfacing relevant information to end-users in a concise and digestible format is crucial for maximizing the value of data assets. Automatic document summarization, natural language processing (NLP), and data analytics powered by generative AI present innovative solutions to this challenge. By generating concise summaries of large documents, performing sentiment analysis, and identifying patterns and trends, end-users can quickly grasp the essence of the information without the need to sift through vast amounts of raw data, streamlining information consumption and enabling more informed decision-making.

This is where Amazon Bedrock comes into play. 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, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. This post shows how to integrate Amazon Bedrock with the AWS Serverless Data Analytics Pipeline architecture using Amazon EventBridge, AWS Step Functions, and AWS Lambda to automate a wide range of data enrichment tasks in a cost-effective and scalable manner.

Solution overview

The AWS Serverless Data Analytics Pipeline reference architecture provides a comprehensive, serverless solution for ingesting, processing, and analyzing data. At its core, this architecture features a centralized data lake hosted on Amazon Simple Storage Service (Amazon S3), organized into raw, cleaned, and curated zones. The raw zone stores unmodified data from various ingestion sources, the cleaned zone stores validated and normalized data, and the curated zone contains the final, enriched data products.

Building upon this reference architecture, this solution demonstrates how enterprises can use Amazon Bedrock to enhance their data assets through automated data enrichment. Specifically, it showcases the integration of the powerful FMs available in Amazon Bedrock for generating concise summaries of unstructured documents, enabling end-users to quickly grasp the essence of information without sifting through extensive content.

The enrichment process begins when a document is ingested into the raw zone, invoking an Amazon S3 event that initiates a Step Functions workflow. This serverless workflow orchestrates Lambda functions to extract text from the document based on its file type (text, PDF, Word). A Lambda function then constructs a payload with the document’s content and invokes the Amazon Bedrock Runtime service, using state-of-the-art FMs to generate concise summaries. These summaries, encapsulating key insights, are stored alongside the original content in the curated zone, enriching the organization’s data assets for further analysis, visualization, and informed decision-making. Through this seamless integration of serverless AWS services, enterprises can automate data enrichment, unlocking new possibilities for knowledge extraction from their valuable unstructured data.

The serverless nature of this architecture provides inherent benefits, including automatic scaling, seamless updates and patching, comprehensive monitoring capabilities, and robust security measures, enabling organizations to focus on innovation rather than infrastructure management.

The following diagram illustrates the solution architecture.

Let’s walk through the architecture chronologically for a closer look at each step.

Initiation

The process is initiated when an object is written to the raw zone. In this example, the raw zone is a prefix, but it could also be a bucket. Amazon S3 emits an object created event and matches an EventBridge rule. The event invokes a Step Functions state machine. The state machine runs for each object in parallel, so the architecture scales horizontally.

Workflow

The Step Functions state machine provides a workflow to handle different file types for text summarization.  Files are first preprocessed based on the file extension and corresponding Lambda function.  Next, the files are processed by another Lambda function that summarizes the preprocessed content. If the file type is not supported, the workflow fails with an error. The workflow consists of the following states:

  • CheckFileType – The workflow starts with a Choice state that checks the file extension of the uploaded object. Based on the file extension, it routes the workflow to different paths:
    • If the file extension is .txt, it goes to the IngestTextFile state.
    • If the file extension is .pdf, it goes to the IngestPDFFile state.
    • If the file extension is .docx, it goes to the IngestDocFile state.
    • If the file extension doesn’t match any of these options, it goes to the UnsupportedFileType state and fails with an error.
  • IngestTextFile, IngestPDFFile, and IngestDocFile – These are Task states that invoke their respective Lambda functions to ingest (or process) the file based on its type. After ingesting the file, the job moves to the SummarizeTextFile state.
  • SummarizeTextFile – This is another Task state that invokes a Lambda function to summarize the ingested text file. The function takes the source key (object key) and bucket name as input parameters. This is the final state of the workflow.

You can extend this code sample to account for different types of files, including audio, pictures, and video files, by using services like Amazon Transcribe or Amazon Rekognition.

Preprocessing

Lambda enables you to run code without provisioning or managing servers. This solution contains a Lambda function for each file type. These three functions are part of a larger workflow that processes different types of files (Word documents, PDFs, and text files) uploaded to an S3 bucket. The functions are designed to extract text content from these files, handle any encoding issues, and store the extracted text as new text files in the same S3 bucket with a different prefix. The functions are as follows:

  • Word document processing function:
    • Downloads a Word document (.docx) file from the S3 bucket
    • Uses the python-docx library to extract text content from the Word document by iterating over its paragraphs
    • Stores the extracted text as a new text file (.txt) in the same S3 bucket with a cleaned prefix
  • PDF processing function:
    • Downloads a PDF file from the S3 bucket
    • Uses the PyPDF2 library to extract text content from the PDF by iterating over its pages
    • Stores the extracted text as a new text file (.txt) in the same S3 bucket with a cleaned prefix
  • Text file processing function:
    • Downloads a text file from the S3 bucket
    • Uses the chardet library to detect the encoding of the text file
    • Decodes the text content using the detected encoding (or UTF-8 if encoding can’t be detected)
    • Encodes the decoded text content as UTF-8
    • Stores the UTF-8 encoded text as a new text file (.txt) in the same S3 bucket with a cleaned prefix

All three functions follow a similar pattern:

  1. Download the source file from the S3 bucket.
  2. Process the file to extract or convert the text content.
  3. Store the extracted and converted text as a new text file in the same S3 bucket with a different prefix.
  4. Return a response indicating the success of the operation and the location of the output text file.

Processing

After the content has been extracted to the cleaned prefix, the Step Functions state machine initiates the Summarize_text Lambda function. This function acts as an orchestrator in a workflow designed to generate summaries for text files stored in an S3 bucket. When it’s invoked by a Step Functions event, the function retrieves the source file’s path and bucket location, reads the text content using the Boto3 library, and generates a concise summary using Anthropic Claude 3 on Amazon Bedrock. After obtaining the summary, the function encapsulates the original text, generated summary, model details, and a timestamp into a JSON file, which is uploaded back to the same S3 bucket with a specified prefix, providing organized storage and accessibility for further processing or analysis.

Summarization

Amazon Bedrock provides a straightforward way to build and scale generative AI applications with FMs. The Lambda function sends the content to Amazon Bedrock with directions to summarize it. The Amazon Bedrock Runtime service plays a crucial role in this use case by enabling the Lambda function to integrate with the Anthropic Claude 3 model seamlessly. The function constructs a JSON payload containing the prompt, which includes a predefined prompt stored in an environment variable and the input text content, along with parameters like maximum tokens to sample, temperature, and top-p. This payload is sent to the Amazon Bedrock Runtime service, which invokes the Anthropic Claude 3 model and generates a concise summary of the input text. The generated summary is then received by the Lambda function and incorporated into the final JSON file.

If you use this solution for your own use case, you can customize the following parameters:

  • modelId – The model you want Amazon Bedrock to run. We recommend testing your use case and data with different models. Amazon Bedrock has a lot of models to offer, each with their own strengths. Models also vary by context window, which is how much data you can send with a single prompt.
  • prompt – The prompt that you want Anthropic Claude 3 to complete. Customize the prompt for your use case. You can set the prompt in the initial deployment steps as described in the following section.
  • max_tokens_to_sample – The maximum number of tokens to generate before stopping. This sample is currently set at 300 to manage cost, but you will likely want to increase it.
  • Temperature – The amount of randomness injected into the response.
  • top_p – In nucleus sampling, Anthropic’s Claude 3 computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off when it reaches a particular probability specified by top_p.

The best way to determine the best parameters for a specific use case is to prototype and test. Fortunately, this can be a quick process by using the following code example or the Amazon Bedrock console. For more details about models and parameters available, refer to Anthropic Claude Text Completions API.

AWS SAM template

This sample is built and deployed with AWS Serverless Application Model (AWS SAM) to streamline development and deployment. AWS SAM is an open source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. You define the application you want with just a few lines per resource and model it using YAML. In the following sections, we guide you through the process of a sample deployment using AWS SAM that exemplifies the reference architecture.

Prerequisites

For this walkthrough, you should have the following prerequisites:

Set up the environment

This walkthrough uses AWS CloudShell to deploy the solution. CloudShell is a browser-based shell environment provided by AWS that allows you to interact with and manage your AWS resources directly from the AWS Management Console. It offers a pre-authenticated command line interface with popular tools and utilities pre-installed, such as the AWS Command Line Interface (AWS CLI), Python, Node.js, and git. CloudShell eliminates the need to set up and configure your local development environments or manage SSH keys, because it provides secure access to AWS services and resources through a web browser. You can run scripts, run AWS CLI commands, and manage your cloud infrastructure without leaving the AWS console. CloudShell is free to use and comes with 1 GB of persistent storage for each AWS Region, allowing you to store your scripts and configuration files. This tool is particularly useful for quick administrative tasks, troubleshooting, and exploring AWS services without the need for additional setup or local resources.

Complete the following steps to set up the CloudShell environment:

  1. Open the CloudShell console.

If this is your first time using CloudShell, you may see a “Welcome to AWS CloudShell” page.

  1. Choose the option to open an environment in your Region (the Region listed may vary based on your account’s primary Region).

It may take several minutes for the environment to fully initialize if this is your first time using CloudShell.

The display resembles a CLI suitable for deploying AWS SAM sample code.

Download and deploy the solution

This code sample is available on Serverless Land and GitHub. Deploy it according to the directions in the GitHub README on the CloudShell console:

git clone https://github.com/aws-samples/step-functions-workflows-collection

cd step-functions-workflows-collection/s3-sfn-lambda-bedrock

sam build

sam deploy –-guided

For the guided deployment process, use the default values. Also, enter a stack name. AWS SAM will deploy the sample code.

Run the following code to set up the required prefix structure:

bucket=$(aws s3 ls | grep sam-app | cut -f 3 -d ' ') && for each in raw cleaned curated; do aws s3api put-object --bucket $bucket --key $each/; done

The sample application has now been deployed and you’re ready to begin testing.

Test the solution

In this demo, we can initiate the workflow by uploading documents to the raw prefix. In our example, we use PDF files from the AWS Prescriptive Guidance portal. Download the article Prompt engineering best practices to avoid prompt injection attacks on modern LLMs and upload it to the raw prefix.

EventBridge will monitor for new file additions to the raw S3 bucket, invoking the Step Functions workflow.

You can navigate to the Step Functions console and view the state machine. You can observe the status of the job and when it’s complete.

The Step Functions workflow verifies the file type, subsequently invoking the appropriate Lambda function for processing or raising an error if the file type is unsupported. Upon successful content extraction, a second Lambda function is invoked to summarize the content using Amazon Bedrock.

The workflow employs two distinct functions: the first function extracts content from various file types, and the second function processes the extracted information with the assistance of Amazon Bedrock, receiving data from the initial Lambda function.

Upon completion, the processed data is stored back in the curated S3 bucket in JSON format.

The process creates a JSON file with the original_content and summary fields.  The following screenshot shows an example of the process using the Containers On AWS whitepaper.  Results can vary depending on the large language model (LLM) and prompt strategies selected.

Clean up

To avoid incurring future charges, delete the resources you created. Run sam delete from CloudShell.

Solution benefits

Integrating Amazon Bedrock into the AWS Serverless Data Analytics Pipeline for data enrichment offers numerous benefits that can drive significant value for organizations across various industries:

  • Scalability – This serverless approach inherently scales resources up or down as data volumes and processing requirements fluctuate, providing optimal performance and cost-efficiency. Organizations can handle spikes in demand seamlessly without manual capacity planning or infrastructure provisioning.
  • Cost-effectiveness – With the pay-per-use pricing model of AWS serverless services, organizations only pay for the resources consumed during data enrichment. This avoids upfront costs and ongoing maintenance expenses of traditional deployments, resulting in substantial cost savings.
  • Ease of maintenance – AWS handles the provisioning, scaling, and maintenance of serverless services, reducing operational overhead. Organizations can focus on developing and enhancing data enrichment workflows rather than managing infrastructure.
  • Across industries, this solution unlocks numerous use cases:
  • Research and academia – Summarizing research papers, journals, and publications to accelerate literature reviews and knowledge discovery
  • Legal and compliance – Extracting key information from legal documents, contracts, and regulations to support compliance efforts and risk management
    • Healthcare – Summarizing medical records, studies, and patient reports for better patient care and informed decision-making by healthcare professionals
    • Enterprise knowledge management – Enriching internal documents and repositories with summaries, topic modeling, and sentiment analysis to facilitate information sharing and collaboration
  • Customer experience management – Analyzing customer feedback, reviews, and social media data to identify sentiment, issues, and trends for proactive customer service
  • Marketing and sales – Summarizing customer data, sales reports, and market analysis to uncover insights, trends, and opportunities for optimized campaigns and strategies

With Amazon Bedrock and the AWS Serverless Data Analytics Pipeline, organizations can unlock their data assets’ potential, driving innovation, enhancing decision-making, and delivering exceptional user experiences across industries.

The serverless nature of the solution provides scalability, cost-effectiveness, and reduced operational overhead, empowering organizations to focus on data-driven innovation and value creation.

Conclusion

Organizations are inundated with vast information buried within documents, reports, and complex datasets. Unlocking the value of these assets requires innovative solutions that transform raw data into actionable insights.

This post demonstrated how to use Amazon Bedrock, a service providing access to state-of-the-art LLMs, within the AWS Serverless Data Analytics Pipeline. By integrating Amazon Bedrock, organizations can automate data enrichment tasks like document summarization, named entity recognition, sentiment analysis, and topic modeling. Because the solution utilizes a serverless approach, it handles fluctuating data volumes without manual capacity planning, paying only for resources consumed during enrichment and avoiding upfront infrastructure costs.

This solution empowers organizations to unlock their data assets’ potential across industries like research, legal, healthcare, enterprise knowledge management, customer experience, and marketing. By providing summaries, extracting insights, and enriching with metadata, you efficiency add innovative features that provide differentiated user experiences.

Explore the AWS Serverless Data Analytics Pipeline reference architecture and take advantage of the power of Amazon Bedrock. By embracing serverless computing and advanced NLP, organizations can transform data lakes into valuable sources of actionable insights.

About the Authors

Dave Horne is a Sr. Solutions Architect supporting Federal System Integrators at AWS. He is based in Washington, DC, and has 15 years of experience building, modernizing, and integrating systems for public sector customers. Outside of work, Dave enjoys playing with his kids, hiking, and watching Penn State football!

Robert Kessler is a Solutions Architect at AWS supporting Federal Partners, with a recent focus on generative AI technologies. Previously, he worked in the satellite communications segment supporting operational infrastructure globally. Robert is an enthusiast of boats and sailing (despite not owning a vessel), and enjoys tackling house projects, playing with his kids, and spending time in the great outdoors.