PDF extraction that checks its own work. #2 reading order accuracy — zero AI, zero GPU, zero cost.
Extract structured data from local or remote LLM models
Reproducible diagnostic investigation of a fine-tuned SLM that scored 99.75% on evaluation and failed silently on 10% of production inputs. Full pipeline. Every number verified.
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.
A simple llm library
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
Source content for Vstorm blog posts—carefully crafted to provide both depth and clarity, with practical insights readers can apply immediately.
Agent Zero plugin for structured document extraction — invoices, recipes, prep lists. Powered by google/langextract with source grounding.
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.
Automated prompt optimization using mentor-agent architecture. Generate and refine prompts from labeled data.
Multi-dimensional extraction engine for AI conversations.
📰 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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