RAG tooling: PageIndex vectorless + RAGAS/hybrid/agentic (CrewAI/MCP)/security + GraphRAG + n8n vec + GLM/Nemotron-OCR
Key Questions
What is PageIndex in RAG tooling?
PageIndex enables tree navigation without vector DBs, improving RAG efficiency. It supports audits and TCO evaluations alongside GraphRAG and n8n vector pipelines.
What is agentic RAG?
Agentic RAG uses CrewAI, MCP, and plan-retrieve-eval-refine cycles to supercharge retrieval with memory and tools. It addresses production failures where 90% of AI apps fail due to data quality.
How vulnerable are RAG systems to security risks?
73% of production RAG systems are vulnerable to prompt injection, data poisoning, and OWASP GenAI risks. Tools like RAGPoison and hallucinations detectors mitigate these threats.
What is GraphRAG and how does it differ from traditional RAG?
GraphRAG leverages knowledge graphs for smarter retrieval over vector-based traditional RAG. It excels in complex queries requiring relational understanding.
What role does RAGAS play in evaluations?
RAGAS provides metrics for RAG system evaluation, integrated with LangChain OpenCode. It supports hybrid strategies like pgvector and six retrieval methods.
Why do most AI apps fail in production?
90% fail due to poor data quality in RAG pipelines, despite advanced tooling. Solutions include CRUMQs, agents, and n8n for robust implementations.
How do hybrid RAG strategies work?
Hybrid RAG combines pgvector with six strategies for better retrieval accuracy. It integrates with LangChain for document loaders and output parsers.
What OCR tools enhance RAG?
GLM and Nemotron-OCR improve text extraction for RAG, supporting vectorless and agentic approaches. Weaviate Agent Skills now import PDFs for Claude Code agents.
PageIndex tree nav no vecDB; agentic RAG (CrewAI/MCP/plan-retrieve-eval-refine); prod fails (90% data qual); RAGAS eval/LangChain OpenCode; hybrid (pgvector/6-strats); security (RAGPoison/73% vuln/OWASP GenAI/hallucinations tool). TCO/audits (PageIndex/GraphRAG/RAGAS/CRUMQs/agents/n8n).