ContextGem
Describe, don't prompt. ContextGem is a free, open-source framework that does the work around the models for you: you describe what to extract, and it handles the prompts, the data models, the source references and the justifications, with minimal code.
Upgrading from 0.x? ContextGem 1.0 renames and removes classes and parameters, changes some defaults and drops Python 3.10, so code and serialized data written for 0.x may need changes. Follow the migration guide.
uv add contextgem
Or, with pip: pip install -U contextgem.
- Generates the prompts from your descriptions of what to extract: there are no prompts to write.
- Returns typed, validated values: text, numbers, dates, booleans, labels, ratings and JSON objects.
- Maps each extracted item back to the paragraphs or sentences of the document that it came from, and can give the reasoning behind it.
- Extracts in context: aspects, sub-aspects and concepts focus the model on the text that matters.
- Runs as one declarative pipeline, reusable and serializable, with model routing, fallbacks and cost tracking.
- Uses the right model for each question: cloud and local LLMs through LiteLLM where it takes reasoning, and decision models where a fast, low-cost answer is enough.
- Gives the probability of each possible answer of a decision model, so that you set the threshold.
- Fits into agentic workflows: extracts the metadata of documents at ingestion, runs as a tool that an agent calls, and decides the next step of a workflow, or the tools that an agent gets, with probabilities.
- Is built for in-depth analysis of individual documents, not for cross-document querying or corpus-wide retrieval.
See the documentation for the quickstart, how it works, decision models and agentic workflows, the repository for the overview and the source, and the changelog for the version history.
Licensed under Apache-2.0.
1 maintainer
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Release files for contextgem 1.1.0
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