document-processing

Retrieval of fully structured data made easy. Use LLMs or custom models. Specialized on PDFs and HTML files. Extensive support of tabular data extraction and multimodal queries.

AI-powered platform for OSINT intelligence analysis. Features archive discovery with hypothesis-driven investigation, GLiNER entity extraction, Mapbox geospatial visualization, network analysis, and document processing. Built with FastAPI, Next.js, Weaviate, and DSPy.

A Model Context Protocol (MCP) server implementation that integrates with the Nutrient Document Web Service (DWS) Processor API, providing powerful PDF processing capabilities for AI assistants.

A privacy-first PDF processing engine that runs entirely in your browser—deconstructing documents into text, images, and tables, then reconstructing them into pristine, structured Markdown. Static React SPA with client-side AI vision via Ollama, OpenRouter, or Gemini. Your files never leave your machine.

Turn documents into structured JSON with local-first document AI. Run 100% locally by default, with API, CLI, and Web UI.

3DCF / doc2dataset: token-efficient document layer with NumGuard numeric integrity and multi-framework exports for RAG & fine-tuning.

Anthropic's Contextual Retrieval implementation with visual chunk comparison. Preview context enrichment before/after embedding.

Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language. Production-ready with multi-AI support, vector storage, and multi-database coordination.

LawBotics v2 is an AI-powered legal contract analysis platform that combines machine learning with modern web technologies to automate legal document review and clause extraction.

Extract structured data from documents, images, audio, and video using LLMs in Python

This open-source project delivers a complete pipeline for converting multi-page documents (PDFs/images) into structured JSON using Vision LLMs on Amazon SageMaker. The solution leverages the SWIFT Framework to fine-tune models specifically for document understanding tasks.

Local-first AI assistant — 9 specialized agents (code, web, debug, security…), 10M-token vector memory, multimodal (vision + local image generation), two-way voice (dictation + read-aloud), slash commands & codebase RAG, agentic VS Code extension, mobile relay via secure tunnel. 100% local with Ollama. Your data, your control.

A personal AI knowledge assistant powered by RAG, semantic search, and document understanding for context-aware answers.