RAG framework

readme: Extensive RAG capabilities that cover everything from document ingestion to retrieval

readme: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.

readme: Connect your favorite local or cloud LLM, ingest your documents, and start chatting in minutes.

description: A no-code RAG server that keeps answers in sync with your documents.

readme: Retrieval with citations and agentic RAG

readme: LlamaIndex is a "data framework" to help you build LLM apps.

readme: Search intelligently** - Full-text and vector search across all your content

readme: Scope semantic search to a project or memory subtree instead of scanning a flat vector pool.

description: LightRAG: Simple and Fast Retrieval-Augmented Generation

readme: PageIndex is a vectorless , reasoning-based RAG engine that mirrors how humans read

readme: Answers come from retrieval-augmented generation (RAG) with hybrid search (vectors and keywords) inside PostgreSQL.

readme: hybrid retrieval (vector search and a full-corpus keyword index, merged by rank fusion) over clause-aware chunks

readme: it searches an offline library of medical, emergency, how-to and encyclopedia articles, answers from what it finds

description: Open-source enterprise RAG solution: knowledge base → automated marketing content → omnichannel publishing.

readme: 一个检索增强生成（RAG）知识库系统，**同一套前端 + 两个功能等价的后端**

readme: Search the files on your computer by what's in them, with Google DeepMind's

description: let an agent draft an ontology over it, and load it into HelixDB as one graph you can explore and ask questions of.

readme: Carrel is a self-hosted knowledge base for AI agents. It automates document parsing, indexing, and retrieval, with optional knowledge graphs.

readme: 导入剧情库后，她只回忆**自己出场过**的正式经历。

readme: What the base adds over search or RAG