I've just published tethered, a Python package for runtime network egress control, which is dependency-free, zero-infrastructure, and very easy to set up. It takes one function call to restrict which hosts your code can connect to.
Use cases:
- Supply chain defense. A compromised dependency can't phone home if egress is locked to your known services.
- AI agent guardrails. Constrain LLM-powered agents to only the APIs they need.
- Test isolation. Ensure your test suite never accidentally hits production endpoints.
- Least-privilege networking. Declare your app's network surface the same way you declare its dependencies.
As part of work on my open-source project ContextGem, I've built a native, zero-dependency DOCX converter that transforms Word documents into LLM-ready data.
This custom-built converter directly processes Word XML, provides comprehensive content extraction + covers what other open-source tools often miss or lack support for:
- Rich paragraph and sentence metadata for enhanced context
- Misaligned tables
- Comments, footnotes, and textboxes
- Embedded images
The converted document can then be easily used in ContextGem's LLM extraction workflows.
Perfect for developers building contract intelligence applications where precision matters. The converter preserves document structure and relationships, empowering LLMs to better understand and analyze document content.
Try it / share with your dev team today and see the difference in your document processing pipeline!
I’ve read that there are a lot of OpenXML elements that are pretty opaque. They appear to basically be XML-esque representations of binary, in-memory structs used internally by Office. (Maybe this has changed over time.)
How much OpenXML does this actually handle?
Extracts information that other open-source tools often do not capture: misaligned tables
Could you expand on what you mean by misaligned tables? Are these tables that appear as separate ‘table nodes’ in the XML, or ones that appear as a single node but have wonky formatting?
Hey! This is really awesome. Do you intend to support analysis on redlining/tracked changes? That's where it would become very useful for my use cases.
I have just released ContextGem - an open-source Python framework that offers the easiest and fastest way to build LLM extraction workflows through powerful abstractions.
Why ContextGem? Most popular LLM frameworks for extracting structured data from documents require extensive boilerplate code to extract even basic information. This significantly increases development time and complexity.
ContextGem addresses this challenge by providing a flexible, intuitive framework that extracts structured data and insights from documents with minimal effort. Complex, most time-consuming parts, - prompt engineering, data modelling and validators, grouped LLMs with role-specific tasks, neural segmentation, etc. - are handled with powerful abstractions, eliminating boilerplate code and reducing development overhead.
ContextGem leverages LLMs' long context windows to deliver superior accuracy for data extraction from individual documents. Unlike RAG approaches that often struggle with complex concepts and nuanced insights, ContextGem capitalizes on continuously expanding context capacity, evolving LLM capabilities, and decreasing costs.
If you are a Python developer, please try it! Your feedback would be much appreciated! And if you like the project, please give it a to help it grow. Let's make ContextGem the most effective tool for extracting structured information from documents!
Use cases: - Supply chain defense. A compromised dependency can't phone home if egress is locked to your known services. - AI agent guardrails. Constrain LLM-powered agents to only the APIs they need. - Test isolation. Ensure your test suite never accidentally hits production endpoints. - Least-privilege networking. Declare your app's network surface the same way you declare its dependencies.
github.com/shcherbak-ai/tethered