LangExtract: LLM-Powered Entity Extraction with One Example
Plus skip unstable packages with uv
Grab your coffee. Here are this week’s highlights.
📅 Today’s Picks
Skip Freshly Released Packages Automatically with uv
Problem
Installing updated package versions is essential to benefit from new features and bug fixes.
However, freshly released versions can introduce bugs or incompatibilities before the community has time to catch them.
Solution
uv’s exclude-newer option lets you set a cooldown period to skip packages released within a specified timeframe.
To use it, add exclude-newer = "7 days" to pyproject.toml and customize the duration as needed.
📖 View Full Article | ⭐ View GitHub
LangExtract: LLM-Powered Entity Extraction with One Example
Problem
Named entity recognition extracts entities like names, dates, and organizations from text.
But pre-trained NER models can fail on domain-specific text. They weren’t trained on medical terms, so “Metformin 500mg” gets labeled as “LAW” instead of “medication”.
Fixing this means retraining with thousands of labeled examples.
Solution
LangExtract is Google’s LLM-powered extraction library that skips retraining entirely. It works on any domain with just one example.
Plus, every extraction includes:
Exact character positions for source verification
Attribute grouping to link related entities
Interactive visualizations to review results
📖 View Full Article | 🧪 Run code | ⭐ View GitHub
☕️ Weekly Finds
pypdf [Python Utils] - Pure-Python PDF library for splitting, merging, cropping, and transforming PDF files
buzz [ML] - Transcribe and translate audio offline using OpenAI’s Whisper on your personal computer
autogluon [ML] - AWS AutoML toolkit for automating machine learning tasks with strong predictive performance
Before You Go
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