contextgem vs lift
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 — Datalab's 9B vision model that extracts JSON matching your schema from PDFs and images in one pass — schema-constrained decoding, multi-page values, vLLM or HF inference, Schema Studio.
Both extract structured values from documents against a declared target; ContextGem drives any LLM with references and justifications, lift is a dedicated 9B VLM with schema-constrained decoding.
| contextgem | lift | |
|---|---|---|
| Stars | 2.0k | 1.2k |
| Forks | 186 | 110 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 1 months ago | 6 days ago |
| Topics | ocr, data | ocr, data |
| Curated connections | 11 | 4 |
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.
lift — the curator's take
Use it when you need fields, not Markdown — invoices, contracts, filings into a fixed JSON schema — locally: 90% field accuracy on its own 225-doc benchmark at ~3x Gemini Flash's speed on one GPU. Read the license first: commercial self-hosting needs a paid Datalab license. Keep schemas simple (no enum, oneOf or $ref, or the decoder guarantee weakens), and expect whole-document perfection only ~1 time in 5.