Resources and tutorials for programmers' advanced learning journey. Advanced tracks in Java, Python, and C#. Blog: https://blog.csdn.net/badao_liumang_qizhi
The article explains a structured business‑trace logging pattern that records each critical step of a workflow as a node, stores logs in a dedicated MySQL database, aggregates them by a business number, and presents them in a sortable, color‑coded front‑end for both developers and business users.
This article explains unit testing fundamentals, compares unit and integration tests, introduces Mockito's core annotations and APIs, walks through a full order‑resend example with detailed test cases, and covers best‑practice methodologies, advanced techniques, common pitfalls, naming conventions, and how to run the tests.
The article demonstrates how to use Spring Boot and MyBatis-Plus to record user-product visit logs via an atomic INSERT ON DUPLICATE KEY UPDATE upsert, while isolating any remote-call failures through layered try-catch blocks so that logging never disrupts the main product-detail response.
This article walks through constructing a lightweight YAML‑based DSL workflow engine on Spring AI 1.1.2 and Ollama, showing how to define state graphs, register tools, parse and execute nodes—including conditional edges, while loops, and parallel branches—without external orchestration tools.
This tutorial demonstrates how to integrate Spring AI with Ollama, define @Tool‑annotated weather and translation utilities, register them for automatic chaining, and let a large language model answer queries like “fetch Beijing weather and reply in English” using a concise end‑to‑end example.
This article explains the limitations of large language models, introduces Retrieval‑Augmented Generation (RAG) and its four‑step workflow, details Spring AI's RAG components and vector‑store options, and provides complete, runnable Java code—including Maven, configuration, and service classes—to build a local knowledge‑base Q&A system.
This article explains how to replace Spring AI's default in‑memory ChatMemory with a JDBC‑backed implementation that stores conversation history in a MySQL table, covering component changes, schema design, auto‑configuration, and best‑practice configuration details.
This guide explains how Spring AI abstracts LLM conversation memory using a three‑layer architecture, demonstrates configuring MessageWindowChatMemory with a sliding‑window strategy, shows two ways to register the memory advisor, and provides complete Maven, YAML, and Java code examples with test screenshots.
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.
