A blueprint for building event-driven streaming pipelines that process unstructured documents (PDFs, scans, images) in real time for RAG and agentic AI. Covers the Claim Check pattern for keeping binaries out of Kafka, a three-stage topic model (raw_documents → refined_documents → curated_ai_assets), tiered routing between cheap text extractors and GPU OCR, backpressure via Kafka's pause()/resume() API, idempotency with SHA-256 content hashes and exactly-once transactions, Dead-Letter Queues for corrupt files, and fan-out to vector databases, search indexes, and data warehouses via managed sink connectors. Multimodal image processing and cost control through tiered routing are also addressed.
Table of contents
How To Process Unstructured Documents and Images in Real Time With Event-Driven Streaming PipelinesKey TakeawaysWhy Unstructured Data Processing Is Hard in Real-Time RAG PipelinesUsing Streaming as the Control Plane for Unstructured Data PipelinesA Four-Stage Streaming Pipeline for Unstructured DataWhen Layout-Aware Parsing Matters for RAG and AgentsHow To Build Multimodal Streaming Pipelines for ImagesProduction Resiliency Patterns for Unstructured Data PipelinesNext Steps: Implement a Real-Time Unstructured Data PipelineFrequently asked questions (FAQ)Sort: