raglite and RAG-Anything
RAGLite is a lightweight, production-focused RAG implementation with specific database backends (DuckDB/PostgreSQL), while RAG-Anything is a comprehensive framework designed for flexibility across diverse data sources and use cases, making them complementary tools for different scale and complexity requirements rather than direct competitors.
About raglite
superlinear-ai/raglite
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
Combines DuckDB or PostgreSQL native hybrid search (full-text + vector) with advanced RAG techniques including adaptive retrieval, late chunking, and optimal semantic chunking solved via integer programming. Integrates with LiteLLM for any LLM provider, offers optional Model Context Protocol (MCP) server support, and includes specialized document processing (PDF-to-Markdown, OCR) alongside reranking and evaluation via Ragas, all without heavy dependencies like PyTorch or LangChain.
About RAG-Anything
HKUDS/RAG-Anything
"RAG-Anything: All-in-One RAG Framework"
Supports multimodal document analysis including images, tables, and equations through specialized processors and a unified knowledge graph. Built on LightRAG with adaptive parsing modes—either MinerU-based document processing or direct content injection—enabling flexible integration with external parsing pipelines. Integrates vision-language models for enhanced visual query understanding while maintaining compatibility with diverse file formats and enterprise knowledge management workflows.
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