Skip to main content

Document Summarization Pipeline

A serverless AWS solution that accepts documents in multiple formats (txt, markdown, PDF), generates AI-powered summaries using Amazon Bedrock's Claude Sonnet 4.5, creates semantic embeddings using Amazon Nova Multimodal Embeddings, and stores the results in AWS S3 Vectors for efficient semantic search capabilities.

Architecture​

The pipeline leverages the CDK AppMod Catalog Blueprints library, specifically the AgenticDocumentProcessing construct, to orchestrate an AI agent that processes documents intelligently.

Processing Flow​

  1. Documents are uploaded to an S3 bucket (supports .txt, .md, .pdf)
  2. The QueuedS3Adapter triggers processing via SQS queue
  3. The AI agent (Claude Sonnet 4.5) analyzes the document:
    • For text/markdown files: reads content directly
    • For PDF files: uses the custom extract_pdf_text tool to extract text
  4. The agent generates a concise summary in JSON format
  5. The Post Processor Lambda:
    • Generates embeddings using Amazon Nova Multimodal Embeddings (3072 dimensions)
    • Stores the summary and embeddings in S3 Vectors with original filename as metadata
  6. Processed documents are moved to the processed/ prefix; failures go to failed/

Prerequisites​

  • Node.js 18.x or later
  • AWS CLI configured with appropriate credentials
  • AWS CDK CLI (npm install -g aws-cdk)

Project Structure​

.
├── bin/                          # CDK app entry point
├── lib/                          # CDK stack definitions
│   └── document-summarization-stack.ts  # Main infrastructure stack
├── resources/                    # Lambda function code and assets
│   ├── post_processor.py         # Post-processing Lambda (embeddings + S3 Vectors storage)
│   ├── requirements.txt          # Python dependencies (pypdf, boto3)
│   ├── system_prompt.txt         # Agent system prompt for summarization
│   └── tools/                    # Custom agent tools
│       └── pdf_extractor.py      # PDF text extraction tool using pypdf
├── specs/                        # Feature specifications
├── test/                         # Test files
├── cdk.json                      # CDK configuration
├── package.json                  # Node.js dependencies
└── tsconfig.json                 # TypeScript configuration

Installation​

Install dependencies:

npm install

Deployment​

Deploy the stack to your AWS account:

cdk deploy

Usage​

Upload documents to the created S3 bucket. Supported formats:

  • .txt - Plain text files
  • .md - Markdown files
  • .pdf - PDF documents

The pipeline will automatically process uploaded documents and store summaries with embeddings in S3 Vectors for semantic search.

Useful Commands​

  • npm run test - Run unit tests
  • cdk deploy - Deploy this stack to your default AWS account/region
  • cdk diff - Compare deployed stack with current state
  • cdk synth - Emit the synthesized CloudFormation template

Technology Stack​

  • Infrastructure as Code: AWS CDK (TypeScript)
  • AI Models:
    • Claude Sonnet 4.5 (cross-region inference to US) for summarization
    • Amazon Nova Multimodal Embeddings (amazon.nova-2-multimodal-embeddings-v1:0) for vector generation
  • Storage:
    • AWS S3 for document storage (KMS encrypted)
    • AWS S3 Vectors for semantic search (cosine distance, float32)
  • Compute: AWS Lambda (Python 3.13) for custom processing
  • Orchestration: CDK AppMod Catalog Blueprints - AgenticDocumentProcessing construct
  • Observability: CloudWatch Logs with structured JSON logging, custom metrics

Key Components​

Post Processor Lambda​

Handles embedding generation and vector storage:

  • Invokes Amazon Nova Multimodal Embeddings via Bedrock
  • Stores vectors in S3 Vectors index with metadata
  • Implements retry logic with exponential backoff
  • Structured JSON logging for observability

PDF Extractor Tool​

Custom agent tool for PDF processing:

  • Uses pypdf library for text extraction
  • Handles multi-page PDFs
  • Returns structured results with page count and extracted text

S3 Vectors Configuration​

  • Dimension: 3072 (Nova embeddings)
  • Distance metric: Cosine
  • Data type: float32
  • Metadata: originalFilename, summary (non-filterable)

License​

This project is licensed under the MIT License.