document-workflows

End-to-end document processing pipelines combine parse, classify-then-extract, and export steps into reusable workflows. You provide a folder of mixed documents, and the pipeline assembles and runs ADE operations automatically to handle batch processing.

RAG pipelines support vector DB ingestion and database export directly to Snowflake, CSV, and DataFrames. You can prepare processed document chunks for retrieval-augmented generation while simultaneously exporting structured data to your target database.

Visualization patterns display bounding-box overlays, cropped chunks, and word-level grounding results from ADE extraction. Optional Streamlit UIs can monitor pipelines to visualize how documents are parsed and where extraction boundaries are drawn.

LandingAI's ADE SDK components and compatible data tooling are required to assemble robust, scalable document-processing workflows. The pipeline relies on these SDK components to execute parse, extract, split, and grounding operations across batch and async processing.

Automating classify-then-extract patterns without manual glue code is done by composing parse, classify-then-extract, split, RAG, and export operations into reusable workflows. This eliminates manual integration by orchestrating ADE pipeline patterns natively.

Batch processing supports async operations for large-scale document workflows. End-to-end ADE pipeline patterns handle parse, extract, split, and grounding with both batch and async processing to ensure scalable document processing.