Resources and tutorials for programmers' advanced learning journey. Advanced tracks in Java, Python, and C#. Blog: https://blog.csdn.net/badao_liumang_qizhi
This article introduces MongoDB fundamentals, walks through Windows installation, shows how to integrate Spring Boot with MongoDB via Maven, configures connection settings, demonstrates both MongoRepository and MongoTemplate usage with full code examples, and compares MongoDB to Redis for typical use cases.
This article explains a standard Spring MVC + OpenFeign design pattern for microservices, where a contract‑first interface defines routing and API specs, Controllers implement business logic, and Feign clients reuse the same interface for remote calls, with full Maven project setup, code samples, and Nacos integration.
This article introduces Neo4j as a graph database, walks through its Windows installation, and provides a complete Spring Boot integration tutorial—including Maven dependencies, YAML configuration, entity and repository definitions, REST controller implementation, and end‑to‑end testing of user and follow‑relationship APIs.
The article demonstrates how to construct a Spring Boot application that orchestrates multiple AI services using LangChain4j and Ollama, defining intent‑classification and tool‑based assistants, registering them as beans, and routing user requests through a controller to achieve multi‑step intent recognition and task dispatch in a simulated intelligent customer‑service workflow.
This guide shows how to integrate LangChain4j with Alibaba Baichuan's DashScope model in a Spring Boot application, enabling per‑user AI chat memory identified by @MemoryId, isolating conversations, and persisting the sliding‑window message history to Redis with TTL handling.
This guide walks through installing Ollama on Windows, downloading a Qwen2.5‑7B model, configuring Spring Boot with LangChain4j dependencies, setting up application.yml, defining AI service interfaces, adding conversation memory, creating REST and streaming controllers, and testing the end‑to‑end local LLM workflow.
This guide walks through setting up a local LLM with Ollama, configuring Spring Boot and LangChain4j, defining function‑calling tools for weather, order status, logistics and coupons, creating AI service beans, exposing REST controllers, and troubleshooting common integration issues.
This guide walks through why Retrieval‑Augmented Generation (RAG) is needed for large language models, explains the three‑step indexing and query workflow, details LangChain4j’s core components, and provides a complete SpringBoot example—including Maven setup, configuration, service code, and troubleshooting—to create a private document‑vector search system powered by Ollama.
This guide walks through using the SpringAIAlibaba framework to integrate Alibaba Baichuan with Spring Boot via LangChain4j, explains core concepts, compares LangChain4j to Spring AI and OpenAI, and provides step‑by‑step dependency setup, environment configuration, code examples, and a simple browser test.
This guide demonstrates how to integrate LangChain4j into a SpringBoot application to enable multi‑turn chat memory and per‑user session isolation using @MemoryId, configuring ChatMemoryProvider, updating the AI service interface, and testing with curl requests.
