Building a Fast, Memory-Efficient Hash Table in Java: A SwissTable-Inspired Design
6 days ago 高效码农Building a Fast, Memory-Efficient Hash Table in Java: A Deep Dive into SwissTable-Inspired Design Have you ever wondered why some hash tables remain lightning-fast even under high load, while others slow to a crawl as they fill up? One day, I stumbled upon SwissTable—a design that made me squint, grin, and immediately regret every naive linear-probing hash table I’d ever written. This post is the story of how I tried to bring that “why is this so fast?” feeling into the Java world. It’s part technical deep dive, part engineering diary, and part cautionary tale about performance optimization work. I’ll …
Spring AI Chatbot Memory: Implementing Context Retention for Intelligent Conversations
6 months ago 高效码农Building Intelligent Chatbots with Spring AI: Implementing Conversational Memory “ Context retention capability is the defining feature separating basic Q&A tools from true conversational AI systems. This comprehensive guide explores how to implement persistent memory in chatbots using Spring AI framework for natural human-machine dialogues. 1. Environment Setup and Technology Stack Core Component Dependencies The solution leverages: Spring Boot 3.5.0: Microservice framework Spring AI 1.0.0-M6: Core AI integration library Java 17: Primary development language Ollama: Local LLM runtime environment Maven Configuration <?xml version=”1.0″ encoding=”UTF-8″?> <project xmlns=”http://maven.apache.org/POM/4.0.0″ xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” xsi:schemaLocation=”http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd”> <modelVersion>4.0.0</modelVersion> <parent> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId> <version>3.5.0</version> </parent> <groupId>com.example</groupId> <artifactId>test</artifactId> <version>0.0.1-SNAPSHOT</version> <properties> <java.version>17</java.version> …