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

Intelligent document OCR and structured extraction. Turn PDFs and images into typed JSON with zero vendor lock-in.

A schema-driven framework for LLM structured extraction enhanced by multi-stage RL training (SFT→DPO→GRPO), with interpretable reward design and end-to-end reproducibility.

Memtruth SDK: evidence, parse, corpus contracts, chunking, projection, and diagnostics for AI applications.

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.

Collection of purpose-built MCP servers for AI agent workflows.

A simple llm library

Send your low-confidence document extractions. A human reviews them against the PDF and returns a typed Pydantic/Zod response. Managed document verification for AI agents. PDF + handwritten OCR. Client-side fragmentation: full document never leaves your machine. $0.80/page + $5 free credit. Express 30-min SLA. Built on open source awaithumans.

news-summizr extracts structured summaries from headlines, labeling key points like announcement, products, region for quick insight.

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

Deterministic structured extraction from noisy LLM/OCR output. Zero LLM round-trips, microsecond latency, confidence score on every result. msgspec · Pydantic · dataclasses.

Structured CV extraction with strict JSON schema and anti-hallucination guarantees.

Turn tutorial videos into structured specs — Pine Script, recipes, code walkthroughs

Automated research paper analysis: PDF → JSON with evidence extraction using LLMs (DeepSeek, Gemma). Extracts methods, results, datasets, and claims with precise evidence grounding.

Accounts-payable agent: LLM extraction of messy invoices + deterministic 3-way match (invoice/PO/receipt) + human-in-the-loop review, with a safety gate that no bad invoice is ever auto-approved. Fully offline.

简历 PDF → 结构化数据 + 可读 HTML;电子版直取文字层、扫描件自动 OCR,多模态 LLM 读图 + source-span 规则校验防幻觉

Local-first eval harness for unstructured-document extraction — compare LLMs, OCR/IDP tools, and strategies (cascade/ensemble/verify) on the same cohort.

schema-driven evaluation for LLM JSON extraction, json evaluation, structured-extraction, benchmark

Parses SEC EDGAR Form 10-K annual reports into standardized JSON, automatically identifying the content and status of every Item