The leading WeChat public account for Elasticsearch fundamentals, advanced topics, and hands‑on practice. Join us to dive deep into the ELK Stack (Elasticsearch, Logstash, Kibana, Beats).
This article guides you through enabling Easysearch's audit log, configuring the security.audit.type parameter, verifying settings in the management UI, and using the audit records to identify external IPs, failed logins, and SSL handshake failures in a production environment.
The article analyzes why large‑language models like Claude, GPT‑4, and Gemini repeatedly generate nearly identical web designs, identifies four convergence dimensions (typography, color, motion, background), and presents a reusable "Skills" mechanism that injects expert design context to produce distinctive, high‑quality frontend outputs.
This article breaks down Andrew Ng and Anthropic’s Agent Skills course, explaining how organized skill folders give general AI agents domain expertise, repeatable workflows, and new capabilities, while using portable, composable design and progressive disclosure to make agents reliable, scalable, and industrial‑grade.
This article walks through building a practical Elasticsearch demo that lets users type a single query like “nearby clean coffee shop” and get results by combining dense‑vector semantic search, geo filtering, BM25, and a hybrid RRF‑style ranking, with both LLM‑based structuring and a fallback hash‑based embedding.
This guide walks through five systematic checks—indexing delay vs refresh, thread‑pool rejections, segment merges and I/O, bulk size and refresh settings, and mapping/_source configuration—to pinpoint and resolve Elasticsearch write‑performance bottlenecks.
This article shows how to create an Easysearch intelligent assistant that lets users manage indexes, write data, search and aggregate documents using Chinese natural language, by combining the DeepSeek large‑language model with OpenAI‑compatible function calling (Tool Use) and a lightweight Node.js executor.
This article walks through converting the Easysearch command‑line assistant into a web‑based tool by adding an Express API layer, reusing shared logic, and building a lightweight HTML/CSS/JS front‑end, while preserving the original CLI capabilities.
The article explains why Retrieval‑Augmented Generation (RAG) is needed to overcome large language models' knowledge cut‑off and hallucination issues, details the offline indexing and online retrieval‑generation workflow, compares RAG with fine‑tuning, and shows how Easysearch’s hybrid search makes an effective RAG backbone.
Elasticsearch does not store a built‑in write timestamp, so to trace when a document was indexed you must add the field during ingest, using either an Ingest Pipeline, Logstash/Beats configuration, or application‑side code, with guidance on advantages, caveats, and handling historical data.
When Elasticsearch Bulk API reports successful writes and Count shows documents, but Search and Get return empty _source, a six‑hour investigation reveals the root cause is a disabled _source mapping and provides a step‑by‑step debugging checklist and fix.
