2 مستودعات
Awesome GitHub RepositoriesLLM-Based Document Classifiers
Automated systems for categorizing unstructured files and documents to improve data organization and retrieval efficiency.
Distinct from Document Classification: Distinct from Document Classification: focuses on aggregating page-level classifications from an LLM into a single label set representing the entire document.
Explore 2 awesome GitHub repositories matching data & databases · LLM-Based Document Classifiers. Refine with filters or upvote what's useful.
Awesome LLM-Based Document Classifiers GitHub Repositories
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- kreuzberg-dev/kreuzberg
kreuzberg-dev/kreuzberg
8,527عرض على GitHubKreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo
Aggregates page-level classifications across a document's text to produce a combined label set for the whole document.
Rustdocument-intelligenceelixirffi - clusterzx/paperless-ai
clusterzx/paperless-ai
5,238عرض على GitHubpaperless-ai is an AI-powered assistant for Paperless-ngx that automates document classification, tagging, and natural language search. It connects directly to a Paperless-ngx instance, monitors for new or updated documents, and uses configurable AI models to assign titles, tags, types, and correspondents automatically. The tool also provides a real-time chat interface that lets users ask questions about any document and receive context-aware answers. Beyond automated classification, paperless-ai offers several distinguishing capabilities. Every AI request, raw response, and applied metadata
Automatically classifies documents by assigning titles, tags, types, and correspondents using AI models.
JavaScriptaiautomationgemma
