Graph Automation 1.0 Announcement

Build repeatable pipelines

Reuse established Graphwise components and ETL patterns

Move from source data to GraphDB, semantic search, and GraphRAG more efficiently and with greater operational consistency

Data ingestion

Ingesting supported datasets and documents through reusable workflows

Document processing

Parsing PDF, DOCX, and TXT files, splitting content into sections and chunks, and preparing content for downstream steps through adding of semantic tags to enrich downstream retrieval

RDF transformation

Converting structured and unstructured data into RDF; Generating triples via Text‑to‑Triples (TXT2Triples)

Semantic enrichment

Tagging, creating vectors, and preparing content for search and GraphRAG; Loading processed data into GraphDB

GraphDB integration

Loading and managing processed data using reusable components

Workflow reuse

Applying common ETL patterns without rebuilding them each time; Graph Automation 1.0 introduces pre-built workflow blocks for common knowledge‑engineering tasks:

Visual interface

Configuring, orchestrating and understanding workflows more easily; Components are grouped by function and provide guided configuration for common operations, making workflows easier to read, reuse, version, and maintain across projects and customer implementations

Security

Controlling access to Graph Automation and its workflows through integrated authentication and authorization

Reliability

Retaining workflow data and running specialized processing consistently