Hyper-ExtractKnowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export.
Why switchBoth turn documents into graphs with an LLM in the loop. hyper-extract emits hypergraphs and spatio-temporal knowledge with an MCP server for agents; docling-graph insists on validated Pydantic types and per-element provenance for high-precision domains.
Full comparison → ontocastAgentic ontology-assisted RDF extraction: co-evolves domain ontologies and fact graphs in a map/reduce pipeline with RDF 1.2 provenance, entity disambiguation and SHACL autofix.
Why switchBoth turn documents into validated graphs with an LLM in the loop; docling-graph fills Pydantic schemas you define up front, ontocast co-evolves the ontology alongside the facts as it reads. Fixed schema vs emergent one.
Full comparison → unstractLLM-driven platform turning unstructured documents into structured data: a no-code Prompt Studio to define extractions, then deploy as APIs or ETL pipelines. Self-hosted, AGPL + enterprise.
Why switchSame starting point — unstructured documents — different deliverable: Unstract gives you structured records behind an API or ETL job, docling-graph gives you a typed graph with explicit relationships you can export as Cypher.
Full comparison → knowledge_graphNotebook recipe that turns any text corpus into a concept graph with a local Mistral 7B via Ollama — chunk, extract concepts and relations, add proximity edges — for Graph RAG and KG QA.
Why switchBoth go documents → knowledge graph. docling-graph parses with Docling and fills Pydantic schemas so the graph is validated; knowledge_graph is schema-less concept co-occurrence — quick to see, hard to trust.
Full comparison → contextgemDeclarative LLM extraction from documents: describe Aspects and Concepts in plain language, get structured values back with paragraph- or sentence-level references and built-in justifications.
Why switchBoth fill typed schemas from documents with an LLM. docling-graph aims outward at a validated knowledge graph — entities and relations across a corpus; ContextGem deliberately stays inside one document, tying each extracted value back to the sentences that support it.
Full comparison →