Illustrated hardcore tech: AI, agents, algorithms, databases—one picture worth a thousand words.
The article outlines a four‑layer architecture for a smart customer service agent—access, understanding, processing, and output—detailing each layer's responsibilities, handoff triggers to human agents, real‑world use cases, and key evaluation metrics.
The article breaks down Retrieval‑Augmented Generation into offline indexing (document chunking, embedding, vector storage) and online query (question embedding, similarity search, augmented generation), highlights retrieval accuracy as the main bottleneck, and shows practical use cases such as internal knowledge bases and product documentation assistants.
Recent analysis of about 30 RAG‑related job postings shows demand soaring from 35% to 60%, median salaries rising roughly 40%, and interview questions moving from basic pipeline implementation to deep trade‑off discussions on Rerank, hybrid retrieval, evaluation metrics, and compliance.
The article explains how to select an embedding model by evaluating language support, vector dimension, context length, and performance metrics, provides concrete model examples for Chinese and multilingual scenarios, compares storage costs, and recommends starting with open‑source models before considering commercial APIs.
This article walks Java developers through turning Spring AI into an enterprise‑grade AI agent that can query internal databases, access a vector‑based knowledge base, enforce role‑based permissions, persist chat sessions in Redis, add full observability, and be container‑deployed with Docker and Kubernetes.
This article explains how to equip a Spring AI Agent with real‑world capabilities by annotating Java methods with @Tool, registers those tools for the agent, demonstrates single‑ and multi‑tool orchestration, and shows how the Advisor mechanism brings AOP‑style processing such as RAG and memory management into AI workflows.
The article outlines a practical interview‑style design for a multi‑agent office assistant, detailing role specialization, task allocation flow, three communication patterns, conflict‑resolution strategies, and concrete usage scenarios such as meeting scheduling, weekly reporting, and sales data analysis.
The article outlines a four‑layer architecture for a data analysis agent—intent recognition, data acquisition via NL2SQL, analysis execution, and result presentation—while detailing practical methods to boost NL2SQL accuracy and illustrating real‑world e‑commerce, operations, and finance use cases.
The article explains how traditional React page interactions—where developers hard‑code each step—contrast with AI Agent interactions, where a language model decides which tools to call, and shows front‑end developers how to map event handlers to LangChain.js tools, choose between Chain and Agent architectures, and build a multi‑tool Agent with concrete TypeScript examples.
This tutorial walks Go developers through creating a fully functional AI Agent using Eino, covering knowledge‑base indexing with VikingDB, defining retrieval, weather, and web‑search tools, assembling a ReAct graph, running interactive queries, and adapting the demo for production environments.
