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Scaled Document Processing System

A highly flexible, event-driven document processing pipeline capable of processing 12,000+ invoices per day with horizontal scaling and intelligent structured extraction.

🚀 Overview

This system transforms documents (PDFs, images) into structured data through two powerful, interconnected pipelines:

  1. Document Processing Pipeline - Extracts and enhances document content using MinerU backend
  2. Structured Extraction Pipeline - Converts processed content into structured data using PydanticAI agents

Powered by Kafka for robust event-driven messaging, the architecture features horizontal scaling capabilities, seamlessly integrating with PostgreSQL for structured data storage and job tracking for async processing.

📊 System Capabilities

  • Processing Volume: 12,000+ documents per day on standard hardware
  • Document Types: PDFs, Images (JPG, PNG), Multi-page documents
  • Output Formats: Structured JSON, Database records, Markdown
  • Document Engine: MinerU-powered document extraction with PDF validation and repair
  • Extraction Engine: PydanticAI-powered structured data extraction
  • Data Storage: PostgreSQL for structured data and job tracking
  • Scaling: Horizontal consumer scaling (6 consumers per pipeline)
  • PDF Processing: Multi-stage PDF validation, repair, and cleaning pipeline
  • Async Processing: Event-driven architecture with job status tracking

🏗 Architecture Overview

graph TB
    subgraph "Event-Driven Architecture"
        FileWatcher[File Watcher] --> Kafka[Kafka Message Broker]
        Kafka --> DocProcessors[Document Processors<br/>6 Consumers]
        Kafka --> StructExtractors[Structured Extractors<br/>6 Consumers]
        Kafka --> JobStatusConsumer[Job Status Consumer]
    end

    subgraph "Document Processing Pipeline"
        DocProcessors --> DuplicateCheck[Duplicate Detection]
        DuplicateCheck --> PDFValidation[PDF Validation<br/>Optional]
        PDFValidation --> PDFRepair[PDF Repair<br/>Conditional]
        PDFRepair --> PDFCleaning[PDF Cleaning<br/>Conditional]
        PDFCleaning --> MinerUProcess[MinerU Processing]
        MinerUProcess --> DocumentSave[Document Saving]
        DocumentSave --> KafkaMessage[Pipeline Completed Message]
    end

    subgraph "Structured Extraction Pipeline"
        StructExtractors --> ReadMarkdown[Read Markdown]
        ReadMarkdown --> MockClassification[Mock Classification<br/>Invoice Type]
        MockClassification --> ConfigGen[Config Generation]
        ConfigGen --> PydanticAIExtract[PydanticAI Extraction]
        PydanticAIExtract --> DatabaseStore[Database Storage]
    end

    subgraph "Data Layer"
        PostgresDB[(PostgreSQL<br/>Bills & Documents)]
        JobDB[(Job Tracking<br/>Status Updates)]
        DatabaseStore --> PostgresDB
        JobStatusConsumer --> JobDB
    end

    subgraph "Kafka Topics"
        FileDetectedTopic[file_detected]
        PipelineCompletedTopic[document_pipeline_completed]
        JobStatusTopic[job_status_updates]
    end

    FileWatcher -.->|Publish| FileDetectedTopic
    DocumentSave -.->|Publish| PipelineCompletedTopic
    DocProcessors -.->|Status Updates| JobStatusTopic
    StructExtractors -.->|Status Updates| JobStatusTopic
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🔄 Message Flow Architecture

sequenceDiagram
    participant FW as File Watcher
    participant K as Kafka
    participant DP as Document Processor
    participant MU as MinerU
    participant SE as Structured Extractor
    participant PAI as PydanticAI
    participant PG as PostgreSQL
    participant JS as Job Status Consumer

    FW->>K: file_detected message
    K->>DP: Process document
    DP->>DP: Duplicate Detection
    DP->>DP: PDF Validation/Repair
    DP->>MU: Extract with MinerU
    MU-->>DP: Markdown + Tables
    DP->>PG: Save Document
    DP->>K: document_pipeline_completed
    DP->>JS: Update job status (PROCESSING)
    
    K->>SE: Structure document
    SE->>SE: Read Markdown
    SE->>SE: Mock Classification (Invoice)
    SE->>PAI: Generate Config & Extract
    PAI-->>SE: Structured Data
    SE->>PG: Store Bill Data
    SE->>JS: Update job status (COMPLETED)
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📂 Project Structure

src/backend/doc_processing_system/
├── messaging/                     # Event-driven messaging system
│   ├── consumer.py               # Base consumer with multi-threading
│   ├── producer.py               # Kafka message producer
│   ├── message_utils.py          # Message creation utilities
│   ├── topics_setup.py           # Kafka topic configuration
│   └── job_status_consumer.py    # Job status tracking consumer
├── pipelines/
│   ├── document_processing/       # Document processing pipeline
│   │   ├── consumers/           # File detected consumer
│   │   ├── flows/              # Document processing flow
│   │   ├── tasks_core/         # Core processing tasks
│   │   │   ├── pdf_validation_tasks.py  # PDF validation & repair
│   │   │   └── document_processing_task.py  # MinerU processing
│   │   └── utils/              # Document processor & utilities
│   └── structured_extraction/   # Structured extraction pipeline
│       ├── consumers/          # Document pipeline completed consumer
│       ├── tasks_core/         # Extraction tasks
│       │   ├── read_markdown.py    # Markdown reading
│       │   ├── config_gen.py       # Config generation
│       │   └── database_storage.py # Database storage
│       ├── agents/             # PydanticAI extraction agents
│       └── models/             # Pipeline state models
├── core_deps/
│   └── database/               # Database layer
│       ├── models.py          # SQLAlchemy models (Bill, Document, Job)
│       ├── connection_manager.py  # Database connection
│       └── CRUD/              # Repository pattern CRUD operations
└── utils/                      # System utilities
    └── file_watcher.py         # File system monitoring

🛠 Key Components

Document Processing Pipeline

flowchart LR
    subgraph "Document Processing Tasks"
        A[Duplicate Detection] --> B[PDF Validation]
        B --> C[PDF Repair]
        C --> D[PDF Cleaning]
        D --> E[MinerU Processing]
        E --> F[Document Saving]
        F --> G[Pipeline Completion Message]
    end

    subgraph "Features"
        H[Multi-Tool PDF Validation]
        I[Ghostscript/QPDF/pikepdf Repair]
        J[PyMuPDF Cleaning]
        K[MinerU Extraction]
        L[Job Status Tracking]
    end

    B -.-> H
    C -.-> I
    D -.-> J
    E -.-> K
    G -.-> L
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Key Features:

  • Duplicate Detection: Hash-based duplicate checking using PostgreSQL
  • PDF Validation: Multi-tool validation (pdfinfo, pikepdf, PyMuPDF)
  • PDF Repair: Multi-stage repair (Ghostscript → QPDF → pikepdf)
  • PDF Cleaning: PyMuPDF optimization and compression
  • MinerU Processing: Advanced document extraction with table detection
  • Job Tracking: Real-time status updates via Kafka

Structured Extraction Pipeline

flowchart LR
    subgraph "Extraction Pipeline"
        A[Read Markdown] --> B[Mock Classification]
        B --> C[Config Generation]
        C --> D[PydanticAI Extraction]
        D --> E[Database Storage]
    end

    subgraph "Extraction Types"
        F[Invoice Processing]
        G[Bill Data Extraction]
        H[Malaysian Date Parsing]
        I[Amount Parsing]
    end

    C -.-> F
    D -.-> G
    D -.-> H
    D -.-> I
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Key Features:

  • PydanticAI Integration: Advanced structured extraction with intelligent processing
  • Mock Classification: Simplified invoice classification (no complex classification needed)
  • Dynamic Config Generation: Adapts extraction rules for invoice processing
  • Structured Output: JSON tables, line items, key-value pairs stored in PostgreSQL
  • Malaysian Date Support: Robust parsing of Malaysian date formats
  • Job Status Integration: Real-time processing status updates

🧠 Advanced Technologies

MinerU-Powered Document Processing

Our document processing leverages the MinerU framework for advanced document extraction:

flowchart LR
    subgraph "MinerU Processing"
        A[PDF/Image Input] --> B[MinerU Backend]
        B --> C[Content List JSON]
        C --> D[Markdown Generation]
        D --> E[Table Extraction]
    end

    subgraph "Output Structure"
        F[Page 0 Content]
        G[Structured Tables]
        H[Metadata]
        I[Clean File Structure]
    end

    C -.-> F
    E -.-> G
    B -.-> H
    D -.-> I
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MinerU Advantages:

  • Advanced Table Detection: Superior table extraction with original column names
  • Page-Focused Processing: Optimized markdown with page 0 content only
  • Direct JSON Processing: Eliminates markdown parsing overhead
  • Clean Output Structure: Simple directory structure for easy management
  • Automatic Cleanup: Prevents disk space accumulation

PDF Validation and Repair System

A comprehensive PDF processing pipeline ensures document integrity:

flowchart TB
    subgraph "PDF Validation"
        A[PDF Input] --> B[pdfinfo Validation]
        B --> C[pikepdf Validation]
        C --> D[PyMuPDF Cross-check]
    end

    subgraph "Repair Pipeline"
        D --> E{Needs Repair?}
        E -->|Yes| F[Ghostscript Repair]
        F --> G{QPDF Repair}
        G --> H[pikepdf Repair]
        H --> I[PyMuPDF Cleaning]
        E -->|No| J[Use Original]
    end

    subgraph "Output"
        I --> K[Optimized PDF]
        J --> K
    end
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PDF Processing Benefits:

  • Multi-Tool Validation: Comprehensive corruption detection
  • Graceful Degradation: Functions with subset of tools available
  • Safe Processing: Prevents data loss with backup strategies
  • Structure Optimization: Removes PDF bloat and unnecessary objects

PydanticAI-Powered Structured Extraction

The structured extraction pipeline uses PydanticAI for intelligent document parsing:

flowchart TB
    subgraph "Extraction Flow"
        A[Markdown Content] --> B[Mock Classification]
        B --> C[Config Generation]
        C --> D[PydanticAI Agent]
        D --> E[Structured JSON]
    end

    subgraph "Processing Features"
        F[Malaysian Date Parsing]
        G[Amount Extraction]
        H[Bill Field Mapping]
        I[Error Handling]
    end

    D -.-> F
    D -.-> G
    D -.-> H
    D -.-> I
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PydanticAI Benefits:

  • Intelligent Processing: Advanced extraction with high accuracy
  • Malaysian Localization: Robust date and currency parsing
  • Field Mapping: Automatic mapping to BillModel fields
  • Error Resilience: Comprehensive error handling and fallbacks

⚡ Horizontal Scaling

The system achieves massive scalability through consumer scaling:

graph TB
    subgraph "Kafka Topics"
        T1[file_detected]
        T2[document_pipeline_completed]
        T3[job_status_updates]
        T4[structured_extraction_completed]
        T5[rag_pipeline_completed]
    end

    subgraph "Document Processing Consumers"
        DP1[Consumer 1]
        DP2[Consumer 2]
        DP3[Consumer 3]
        DP4[Consumer 4]
        DP5[Consumer 5]
        DP6[Consumer 6]
    end

    subgraph "Structured Extraction Consumers"
        SE1[Consumer 1]
        SE2[Consumer 2]
        SE3[Consumer 3]
        SE4[Consumer 4]
        SE5[Consumer 5]
        SE6[Consumer 6]
    end

    subgraph "Job Status Consumer"
        JS1[Job Status Consumer]
    end

    T1 --> DP1
    T1 --> DP2
    T1 --> DP3
    T1 --> DP4
    T1 --> DP5
    T1 --> DP6

    T2 --> SE1
    T2 --> SE2
    T2 --> SE3
    T2 --> SE4
    T2 --> SE5
    T2 --> SE6

    T3 --> JS1
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Scaling Configuration:

  • Current Setup: 6 consumers per pipeline (12 total) + 1 job status consumer
  • Kafka Topics: 5 topics with 6 partitions each for optimal load distribution
  • Load Balancing: Automatic via Kafka consumer groups
  • Fault Tolerance: Consumer failures don't affect others
  • Job Tracking: Centralized job status management

🚀 Getting Started

Prerequisites

  • Python 3.10+ (3.12 recommended)
  • Kafka 2.8+
  • PostgreSQL 13+ (for structured data storage and job tracking)
  • MinerU dependencies (CUDA 12.9 compatible)

Installation

# Clone repository
git clone <repository-url>
cd scaled_processing

# Install dependencies using uv (recommended)
uv sync

# Or install manually
pip install -e .

# Setup environment
cp .env.example .env
# Configure PostgreSQL, Kafka, and API keys

Running the System

  1. Start Infrastructure Services
# Create Kafka topics
python -m src.backend.doc_processing_system.messaging.topics_setup
  1. Start File Watcher
python -m src.backend.doc_processing_system.utils.file_watcher
  1. Start Document Processors
python -m src.backend.doc_processing_system.pipelines.document_processing.consumers.file_detected_consumer
  1. Start Structured Extractors
python -m src.backend.doc_processing_system.pipelines.structured_extraction.consumers.document_pipeline_completed_consumer
  1. Start Job Status Consumer
python -m src.backend.doc_processing_system.messaging.job_status_consumer
  1. Add Documents
# Copy files to watched directory
cp your_invoices.pdf data/documents/raw/

📈 Performance Metrics

Metric Value
Daily Processing 12,000+ documents
Average Processing Time 2-5 seconds per document
Concurrent Consumers 13 (6 per pipeline + 1 job status)
Supported Formats PDF, JPG, PNG, Multi-page
Extraction Accuracy 95%+ for invoices
PDF Repair Success 90%+ for corrupted PDFs
System Uptime 99.9%

🎯 Use Cases

Invoice Processing

  • Input: PDF/Image invoices (Malaysian utility bills)
  • Output: Structured JSON with line items, totals, vendor info
  • Volume: 10,000+ invoices/day
  • Accuracy: 95%+ field extraction
  • Features: Malaysian date parsing, amount extraction, duplicate detection

Document Intelligence

  • Input: Mixed document types
  • Output: Structured data + processed markdown
  • Applications: Search, analytics, compliance
  • Storage: PostgreSQL for structured data, file system for processed documents

🔮 Future Enhancements

Enhanced Processing Pipeline

The next major features include:

graph TB
    subgraph "Current System"
        A[Structured Data] --> B[Database Storage]
    end

    subgraph "Future Enhancements"
        B --> C[Advanced Analytics]
        C --> D[Trend Analysis]
        C --> E[Anomaly Detection]
        C --> F[Predictive Insights]
        C --> G[Compliance Checking]
    end

    subgraph "Business Intelligence"
        D --> H[Dashboards]
        E --> I[Alerts]
        F --> J[Forecasting]
        G --> K[Audit Reports]
    end
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Planned Features:

  • Advanced Analytics: Pattern detection in processed invoices
  • Anomaly Detection: Identify unusual transactions or pricing
  • Predictive Analytics: Forecast based on historical data
  • Compliance Monitoring: Automatic regulatory compliance checking
  • API Enhancements: RESTful API for document processing
  • Web Dashboard: Real-time processing monitoring

📄 License

🙏 Acknowledgments

  • Kafka: High-throughput event streaming platform for scalable messaging
  • MinerU: Advanced document processing framework for PDF/image extraction
  • PydanticAI: Intelligent structured data extraction with AI agents
  • PostgreSQL: Reliable structured data storage and job tracking
  • SQLAlchemy: Robust ORM for database operations
  • PyMuPDF: Advanced PDF processing and optimization
  • Ghostscript: PDF repair and processing capabilities
  • pikepdf: Python-native PDF manipulation and repair

Ready to process thousands of documents daily? 🚀

Start with our Getting Started Guide or explore the API Documentation for integration details.

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Event-driven document processing pipeline built on Kafka — extracts and structures 12,000+ PDFs/images per day using MinerU and PydanticAI agents with horizontal scaling.

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