contextgem vs unstract
Declarative 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. — versus — LLM-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.
Both 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.
| contextgem | unstract | |
|---|---|---|
| Stars | 2.0k | 7.2k |
| Forks | 183 | 707 |
| Language | Python | Python |
| License | Apache-2.0 | AGPL-3.0 |
| Last activity | 11 days ago | today |
| Topics | ocr, data | ocr, rag |
| Curated connections | 7 | 5 |
contextgem — the curator's take
Its thesis is explicitly anti-RAG: stop chunking and retrieving, put the whole document in a long context window and extract in place. You declare Aspects — the sections or themes worth pulling — and Concepts — the entities, booleans, numbers, ratings and JSON objects inside them — and the framework writes the prompts, builds the validation models, and returns every item with reference_paragraphs, reference_sentences and a justification. That provenance is the point: it makes contract and filing review defensible rather than merely plausible. Two limits it states about itself: no cross-document querying or corpus-wide retrieval, so reach for a real RAG framework there; and its detailed instruction style wants a model at gpt-4o-mini level or better, so 8B local models will struggle even though LiteLLM happily points it at Ollama.
unstract — the curator's take
The platform play in document extraction: where parsers hand you markdown, Unstract hands you the FIELDS — Prompt Studio lets a domain expert define and test per-document-type extraction prompts, then ships them as stable APIs or ETL pipelines into warehouses, with the connector surface (S3, databases, DMS) that ops teams actually need. Docker-compose up and it runs. NOT a parsing engine: quality rides on the LLM you wire in and per-page LLM calls at volume cost real money — for clean-layout bulk conversion a deterministic parser is cheaper; and mind the AGPL + enterprise split when embedding.