Document Processing Pipeline Design

1. The Question

Design a document processing pipeline that ingests documents (PDFs, images, Office files), extracts text and structured fields using OCR and NLP, classifies documents, and saves structured data for search and downstream use. The pipeline should handle high volume, support accuracy monitoring and human-in-the-loop correction, and be scalable and secure.

2. Clarifying Questions

• What types of documents? (scanned images, native PDFs, Word, email)
• Which languages and scripts must be supported?
• Expected volume: documents/day and peak ingestion rate?
• Latency requirements: real-time (<5s), near real-time (<minutes), or batch?
• Accuracy targets for OCR and extraction, and SLA for human review?
• Do we need PII redaction, encryption at rest/in transit, audit logs?
• What downstream uses: search, analytics, indexing, RPA, databases?
• Are there existing storage or tooling constraints (S3, Azure Blob, Databricks)?

3. Requirements

Functional:

• Ingest documents from multiple sources (upload, email, SFTP, connectors).
• Detect document type and language.
• Extract raw text (OCR for scans, native text extraction for digital files).
• Classify documents and extract structured fields/entities.
• Provide storage for raw docs, extracted text, structured records, and annotations.
• Provide APIs for ingestion, retrieval, search, and human review.
• Support human corrections and feedback loop to improve models.

Non-functional:

• Scalable to tens or hundreds of thousands of documents/day.
• Reasonable latency options (real-time for small docs, batch for large volumes).
• High availability, secure storage, and auditability.
• Monitoring, alerting, and model performance tracking.

4. Scale Estimates

Example baseline and peak to guide design:

• Baseline: 100,000 documents/day (~1.2 docs/sec average).
• Peak: 5,000 documents/minute (~83 docs/sec) for short bursts.
• Average doc size: 0.5–2 MB; storage ingest ~50–200 GB/day.
• Extracted text size typically 1–10% of binary size.
• Metadata and structured records: ~1–5 KB/document => ~0.5 GB/day.

Design for autoscaling workers and queueing to absorb bursts; use batches for high efficiency in OCR/NLP workloads.

5. Data Model

Core entities (example JSON-like schema):

• Document Metadata:

  • id, source, original_filename, upload_time, size_bytes, mime_type, language, checksum, status

• Raw Storage:

  • raw_uri (S3/Blob), storage_tier, version

• Extraction Result:

  • text: string (full extracted text)
  • pages: [{page_number, width, height, image_uri, ocr_tokens:[{text, bbox, confidence}]}]
  • entities: [{type, text, start_offset, end_offset, confidence, normalized_value}]
  • tables: [{page, bbox, rows:[...] }]

• Document Classification:

  • doc_type, doc_type_confidence

• Audit / Annotation:

  • user_annotations: [{user_id, field, old_value, new_value, timestamp}]

• Indexing Fields for Search:

  • title, summary, extracted_entities, tags, timestamp

Store structured records in a transactional store (Delta Lake / relational DB) and blobs in object storage. Index copies of searchable fields into a search engine (OpenSearch/Elasticsearch).

6. API Design

Key HTTP endpoints (REST or gRPC):

• POST /ingest

  • body: file upload or URL, metadata, callback_url (optional)
  • response: {document_id, status}

• GET /documents/{id}

  • response: metadata, links to raw file, extracted text, structured payload

• GET /documents/{id}/status

  • response: current processing stage, errors, confidence

• POST /documents/{id}/annotate

  • body: corrections/annotations by human reviewer
  • response: updated record, audit entry

• GET /search?q=...

  • response: paginated documents with highlights

• POST /batch/ingest

  • body: list of document URLs or object keys for bulk processing

Design notes: use async ingestion with immediate document_id return; provide webhook/callbacks or pub/sub notifications for completion.

7. High-Level Architecture

Components and flow:

1. Ingest Layer

   • API Gateway or load-balanced service accepts uploads and connector events. Small sync validation, store raw doc in object store (S3/Blob), write message to queue (Kafka/SQS).

2. Preprocessor

   • Worker pool that normalizes files (convert to images, PDF rasterization, extract embedded text), detect language and doc type.

3. Work Queue

   • Kafka or SQS for decoupling; use topic per processing class. Include priority and retry metadata.

4. OCR/NLP Workers

   • Scalable worker fleet (Kubernetes, Lambda, or Databricks jobs) that perform OCR (for images/scans) or native extraction, then run NLP models to extract entities and classify.
   • Use GPU-backed autoscaling for heavy models; CPU workers for lighter tasks.

5. Post-Processing

   • Normalization, deduplication, validation rules, confidence thresholds, and routing to human review if below threshold.

6. Storage & Index

   • Raw files: object store (S3/Azure Blob) with lifecycle policies.
   • Structured data: Delta Lake (on Databricks) for reliable storage, ACID, and analytics.
   • Search index: OpenSearch/Elasticsearch for full-text search and faceting.

7. Human Review & Feedback

   • Web UI for reviewers to correct extractions; corrections feed back to training data store.

8. Monitoring & Model Management

   • Metrics (prometheus), logs, and model performance dashboards (use MLflow/Databricks for experiments and model registry).

Databricks fit: use Databricks to run large-scale model training, batch feature extraction, Delta Lake as the canonical structured store, and MLflow for model lifecycle.

8. Detailed Design Decisions

• Queueing: Kafka for high-throughput, SQS for simplified serverless.
• OCR: choose managed OCR (Google Document AI, AWS Textract) for higher accuracy and faster time-to-market, or open-source Tesseract + tuned models for cost control. Use hybrid approach: managed for hard cases, local for simple ones.
• NLP: use transformer-based models for entity extraction/classification (fine-tuned BERT/DistilBERT) in Databricks or hosted inference; use faster distilled models for real-time paths.
• Storage: Delta Lake as single source of truth for structured outputs; object storage for raw/processed files; OpenSearch for search.
• Autoscaling: Kubernetes Horizontal Pod Autoscaler or serverless functions with provisioned concurrency for predictable latency.
• Human-in-loop: route low-confidence items to annotation service, store annotations and incorporate into retraining pipelines.
• Security: encrypt at rest, TLS in transit, IAM roles for storage access, VPC endpoints, audit logs for access to sensitive data.

9. Bottlenecks & Scaling

• OCR/NLP compute: GPU-backed inference for heavy models; mitigate by batching documents and using fast OCR models for most cases.
• I/O: uploading and reading large raw files; use multipart upload, pre-signed URLs, and CDN for distribution.
• Queue backlog: use partitioning, multiple consumer groups, and autoscaling consumers.
• Indexing: bulk index in batches to reduce load; use incremental indexing for updates.
• Cost vs latency: provide two paths — low-latency lightweight extraction and high-accuracy batch processing.
• Monitoring: track per-stage latency, success rates, error rates, model drift metrics, and worker utilization to proactively scale or retrain.

10. Follow-up Questions / Extensions

• Support handwriting recognition and multi-language OCR.
• Add entity linking and canonicalization to external knowledge bases.
• Implement active learning: surface uncertain examples to human reviewers and incorporate into training pipeline.
• Add streaming analytics (near real-time dashboards) on extracted entities.
• Privacy features: automated PII detection and redaction, differential privacy for analytics.
• Offline/batch reprocessing: ability to re-run improved models over historical data (use Databricks jobs + Delta Lake snapshots).

11. Wrap-up

A robust document processing pipeline separates ingestion, extraction, and indexing, uses queueing to decouple stages, and provides both real-time and batch paths. Use managed services where speed is important and Databricks + Delta Lake for large-scale training, batch reprocessing, and analytics. Build monitoring and human-in-the-loop to maintain accuracy and continuously improve models.