kevOpen reproduction of TypeSafe's Jev: a LoRA + readout head on Qwen that answers many typed questions about one document in a single prefill pass, returning calibrated probabilities.
Why switchBoth turn one document into structured typed values; contextgem prompts an LLM declaratively per Aspect/Concept, kev trains a head that answers every question in one pass and returns calibrated probabilities instead of generated text.
Full comparison → receipt-ocrReceipt-to-JSON in one pip install: CLI, Python API and FastAPI service that send a receipt image to any OpenAI-compatible model and return merchant, totals and line items, plus a Tesseract module.
Why switchSame idea — describe the fields, get structured values back. ContextGem is a general declarative extraction framework with paragraph-level references and justifications; receipt-ocr hardcodes one domain so there is nothing to design before it works.
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 switchBoth turn documents into structured data with LLMs. Unstract is a platform — no-code Prompt Studio, deployable APIs and ETL around it; ContextGem is a library you compose in code, and it goes further on provenance: every value carries a paragraph- or sentence-level reference and a justification.
Full comparison → PageIndexVectorless, reasoning-based RAG — builds a hierarchical tree index from long documents so an LLM retrieves by relevance instead of similarity. No chunking, no embeddings, no vector DB.
Why switchBoth reject chunk-and-embed for long documents, then diverge on what replaces it. PageIndex builds a hierarchical tree the model navigates to reach the right section; ContextGem skips retrieval entirely and sends the whole document into a long context window. Navigate versus extract in place.
Full comparison → localjevGitHub Next's local Jev bridge: a Bun/TypeScript POST /v1/systemone that translates typed decision questions into prompts for DiffusionGemma behind any OpenAI-compatible endpoint.
Why switchSame shape of job — typed questions over one piece of text, structured values back with validation and retries; contextgem exposes it as a Python Aspect/Concept API, LocalJev as a Jev-wire HTTP service.
Full comparison → docling-graphDocuments to validated knowledge graphs: Docling parses, an LLM or VLM fills Pydantic schemas, and you get a directed NetworkX graph with provenance, Cypher/CSV export and HTML views.
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 →