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 walks through using Spring AI with Ollama to enforce JSON‑schema‑based structured output for agents, showing why structured responses matter, how Spring AI generates schemas from Java beans, and providing complete runnable code for both basic and advanced tool‑calling scenarios.
This guide walks through integrating Ollama’s local LLM with Spring AI, explaining Prompt and PromptTemplate concepts, configuring application.yml, implementing a PromptService, adding controller endpoints, using external template files, and testing the setup with curl commands.
This article walks through setting up Spring AI with Ollama, explains the tool‑calling workflow, shows two ways to define tools, provides full Maven and YAML configurations, presents runnable Java code for services, chat client, and controller, and addresses common compatibility and dependency issues.
This guide walks through replacing DashScope with a local Ollama model in a Spring AI project, showing how to configure multiple MCP servers, adjust Maven and YAML settings, run zero‑code changes, and troubleshoot common issues.
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.
