Focused on AI programming, sharing ready-to-use development efficiency solutions.
This article explains how Spring AI's Tool Calling lets large language models overcome their inability to access real‑time or private data by invoking annotated Java methods, showing step‑by‑step code examples, parameter handling, and the execution flow that bridges model requests with actual method results.
The article examines the limitations of a naïve QuestionAnswerAdvisor RAG implementation, introduces Spring AI's RetrievalAugmentationAdvisor for modular RAG, and demonstrates how query rewriting, multi‑turn compression, and an empty‑context augmenter improve recall and prevent model refusals.
This tutorial explains how Retrieval‑Augmented Generation (RAG) eliminates hallucinations and stale knowledge by retrieving relevant document fragments from a vector store, augmenting prompts, and generating factual answers, with step‑by‑step Spring AI code examples and parameter tuning guidance.
This article shows how to use Spring AI’s ETL pipeline to automatically read PDFs, Word files, and plain‑text documents, split them into token‑based chunks, and load the resulting embeddings into a vector store without manual copying.
This article explains why traditional keyword search fails on synonyms, introduces Spring AI's local ONNX‑based EmbeddingModel to convert text into high‑dimensional vectors, shows how to store and query those vectors with SimpleVectorStore, and compares production‑grade vector store options.
This guide explains why Claude Code lacks terminal notifications, how Warp’s official Claude Code plugin uses event‑driven hooks to deliver desktop alerts, provides step‑by‑step installation and configuration instructions, and offers fallback methods for other terminals.
This article walks through using Claude Design to generate a high‑fidelity WeChat bookkeeping mini‑program via conversational prompts, automatically apply a design system, iterate with Tweaks and inline comments, then hand off the resulting React‑style JSX bundle to Claude Code for uni‑app implementation, while highlighting token costs and common pitfalls.
This article explains Spring AI Advisors as interceptors that can modify requests and responses in the LLM call chain, demonstrates building a custom logging Advisor with code examples, shows how ordering controls execution, and reviews the built‑in Advisors for memory, RAG, logging, and safety.
This article explains how Spring AI’s Media API lets a ChatClient handle both textual prompts and image inputs, shows code examples for attaching images, discusses required visual models, and outlines practical use cases such as OCR, chart analysis, and image moderation.
The article explains how Spring AI’s .entity() method lets developers obtain structured Java objects such as POJOs, lists, enums, and response entities directly from LLM outputs, eliminating the need for manual JSON parsing and handling generic‑type issues with ParameterizedTypeReference.
