Extract structured data from local or remote LLM models
Claude Code Skill for structured information extraction from code/docs/logs. 6-step Python pipeline (source grounding, dedup, confidence scoring, entity resolution, relation inference, KG injection). Zero dependencies, no API keys. Replaces LangExtract.
news-summizr extracts structured summaries from headlines, labeling key points like announcement, products, region for quick insight.
Automated research paper analysis: PDF → JSON with evidence extraction using LLMs (DeepSeek, Gemma). Extracts methods, results, datasets, and claims with precise evidence grounding.
A new package is designed to facilitate structured, reliable extraction of key insights from user-provided texts about cultural topics. It accepts a text input, such as an article or discussion prompt
Automated prompt optimization using mentor-agent architecture. Generate and refine prompts from labeled data.
Source content for Vstorm blog posts—carefully crafted to provide both depth and clarity, with practical insights readers can apply immediately.
Evaluate local LLM accuracy on structured data extraction. Tests models' ability to extract JSON from unstructured text with ground-truth comparison, F1 scoring, and fuzzy matching. Supports MLX and Ollama backends. Generates interactive reports with charts and per-model analysis.
📰 Extract structured summaries from news articles easily. Highlight key points like announcements, products, and regions with minimal effort.
💡 Extract key insights from cultural texts easily with summaryxtract, a Python package powered by LLMs for reliable and structured summarization.
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