Scalable Rag Ingestion Pipeline Document Ai Dataflow

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A financial enterprise is designing a scalable ingestion pipeline on Google Cloud to prepare multi-page PDF prospectuses and scanned image documents for a Retrieval-Augmented Generation (RAG) system. The documents are stored in Cloud Storage and contain multi-column text, nested tabular structures, and complex visual formatting that must be preserved to perform accurate layout-aware chunking before generating vector embeddings.

Which ingestion and extraction architecture should the data engineering team implement?

Configure Dataplex auto data quality scans to inspect the Cloud Storage files directly and extract layout trees and bounding boxes into BigLake tables.

Deploy Cloud Run functions triggered by Cloud Storage uploads to synchronously parse document layouts and insert raw extracted tables directly into Vertex AI Feature Store.

Create BigQuery Object Tables over the Cloud Storage bucket and use standard SQL regular expressions to parse the raw PDF binary streams and extract tabular structures.

Orchestrate an Apache Beam pipeline on Dataflow that reads documents from Cloud Storage, processes them with Document AI to extract layout entities and tabular structures, and outputs normalized structured records for chunking and embedding generation.

Professional Cloud Data Engineer

Scalable Rag Ingestion Pipeline Document Ai Dataflow

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