#multi-hop reasoning

8 related articles

2026年6月4日·2 min

Google Gemini for Science: A Detailed Look at the Experimental AI Toolkit Empowering Scientific Research

Google officially releases Gemini for Science, an experimental AI toolkit for researchers covering hypothesis exploration, large-scale validation, and literature interpretation to accelerate scientific discovery.

Getting Started with RAG: What Is Retrieval-Augmented Generation and How Does It Solve LLM Hallucination?

Tutorials

2026年6月3日·2 min

Getting Started with RAG: What Is Retrieval-Augmented Generation and How Does It Solve LLM Hallucination?

RAG (Retrieval-Augmented Generation) is the core solution for LLM hallucination. Learn RAG concepts, how it works, three causes of hallucination, and the complete learning path from basics to Knowledge Graph RAG.

Building a Local Knowledge Graph RAG System with Dify + Neo4j: A Hands-On Guide

Tutorials

2026年6月3日·2 min

Building a Local Knowledge Graph RAG System with Dify + Neo4j: A Hands-On Guide

A hands-on guide to building a local knowledge graph RAG system using Dify, Neo4j, and Docker for multi-hop reasoning and secure local deployment.

Deep Dives

memU Memory Framework Explained: Unify…

2026年5月29日·3 min

memU Memory Framework Explained: Unifying Multi-Modal Agent Memory with a File System

Deep dive into the memU open-source memory framework: how it organizes Agent memory as a file system with three-layer semantic abstraction, dual-loop collaboration, and two retrieval modes.

RAG Technology Full-Stack Analysis: Core Principles, Enterprise Implementation & Learning Path

Tutorials

2026年5月27日·3 min

RAG Technology Full-Stack Analysis: Core Principles, Enterprise Implementation & Learning Path

Deep analysis of RAG technology's core principles, three key values, enterprise implementation cases, common pitfalls, and a systematic learning roadmap covering vector databases, retrieval optimization, and Knowledge Graph fusion.

Enterprise RAG Implementation: Architecture Principles and Production-Grade Optimization Guide

Tutorials

2026年5月27日·3 min

Enterprise RAG Implementation: Architecture Principles and Production-Grade Optimization Guide

Complete guide to enterprise RAG architecture covering data indexing, vectorization, and retrieval optimization. Practical insights on chunking strategies, hybrid retrieval, and hallucination control for production-grade LLM applications.

Deep Dives

Getting Started with RAG: A Complete G…

2026年5月27日·3 min

Getting Started with RAG: A Complete Guide from LLM Hallucinations to Retrieval-Augmented Generation

A deep dive into RAG (Retrieval-Augmented Generation) technology, covering LLM hallucinations, data staleness, and limited expertise, plus RAG workflows, core components, and LangChain learning paths.

The Complete Guide to Agentic RAG: Principles and Code Implementation

Deep Dives

2026年5月13日·4 min

The Complete Guide to Agentic RAG: Principles and Code Implementation

Deep dive into Agentic RAG vs traditional RAG, covering tool calling, multi-step iteration, query rewriting, with LangChain and LangGraph code examples for building intelligent retrieval systems.