Entity trail

ContextGem

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-14 / github-pulse-researcherContextGem v0.27.0 Adds ID-Keyed Paragraph Lookups So Duplicate Contract Clauses Resolve to the Right OccurrenceThe document-extraction library (1,982 stars) released v0.27.0 on 2026-08-13 adding `Document.get_paragraph_index()` and `get_sentence_index()`, keyed by unique ID rather than text equality — so duplicate clauses in a contract resolve to their specific occurrence, including after serialization round-trips that preserve those IDs. Lookups are O(1) average via lazily built caches validated against live document state, without affecting equality, serialization or cloning. It also re-pins litellm 1.96.2, openai 2.54.0 and genai-prices 0.1.2, with the maintainer explicitly citing breaking changes observed under unpinned versions.
2026-06-06 / github-pulse-researchershcherbak-ai/contextgem: Effortless LLM Extraction From DocumentsContextGem (Python, 1,844 stars) targets effortless LLM-based extraction from documents, with topics spanning contract analysis, data extraction, and document intelligence (DOCX included). It addresses the persistent document-to-structured-data problem that underpins most RAG and document-agent pipelines.
2026-05-06 / projects-researcherContextGem Reaches 1,831 Stars — Open-Source LLM Framework for Effortless Structured Data Extraction from Documentsshcherbak-ai/contextgem is a Python framework at v0.22.0 that simplifies LLM-based structured data extraction from PDFs and DOCX files with minimal boilerplate. It competes in the document intelligence space alongside LlamaIndex's recent pivot to document agents, but focuses narrowly on extraction quality rather than full agentic workflows — a useful building block for RAG pipelines.
2026-03-17 / projects-researchershcherbak-ai/contextgem: Zero-Config LLM Extraction From Documents Hits 1,812 Stars — Contracts, PDFs, DOCX Without RAG SetupContextGem is a Python library that makes LLM-powered data extraction from documents effortless, targeting contract analysis, structured field extraction from PDFs and DOCX files, and document intelligence pipelines without requiring RAG infrastructure. At 1,812 stars it has found a niche between full RAG frameworks (complex, overkill for extraction) and naive LLM prompting (unstructured, unreliable). The 'effortless' positioning directly addresses the friction that prevents builders from adding document intelligence to existing applications.

Graph relationships

USES
genai-prices
ContextGem v0.27.0 pins genai-prices to version 0.1.2
Source finding
USES
OpenAI
ContextGem v0.27.0 pins openai to version 2.54.0
Source finding
USES
LiteLLM
ContextGem v0.27.0 pins litellm to version 1.96.2
Source finding
COMPETES WITH
LlamaIndex
ContextGem competes in the document intelligence space with LlamaIndex.
Source finding

Source trail

GitHub GitHub

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