LightRAG and raglite
Despite sharing a "RAGLite" name component, these tools are competitors; one is a research paper with an associated codebase focused on a novel, efficient RAG architecture, while the other is a Python toolkit providing practical RAG implementations using SQL databases.
About LightRAG
HKUDS/LightRAG
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
Constructs a dual-level retrieval system combining vector similarity search with knowledge graph extraction to handle both entity-centric and content-based queries. Supports multiple storage backends including Neo4j, MongoDB, and PostgreSQL, with integrated reranking, citation tracking, and multimodal document processing via RAG-Anything. Designed for Python 3.10+ with built-in evaluation (RAGAS) and tracing (Langfuse) capabilities.
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.
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