RAG - Tag - Shengxu · Cloud Architecture & DevOps

Hands-On: From AI Semantic Search to AI Content Pipeline – How Static Blogs Continuously Evolve (Continued)

Sat, 06 Jun 2026 10:30:00 +0800

A few months ago, I wrote an article titled “Hands-on: Building Fully Automated AI Semantic Search with Cloudflare Vectorize and Gemini”. The problem it solved was clear: enabling semantic search for a static blog and capturing user queries that failed to find results as Content Gaps.

Once that architecture was running, I quickly realized: Search is just the last mile of the content lifecycle.

From the moment a Markdown article is written to when it’s actually discovered by readers, it must pass through summaries, translations, related recommendations, internal links, image optimization, search indexing, SEO, deployment, and quality checks. If these steps still rely on manual processing, even the smartest AI search is just a new entry point bolted onto a traditional publishing workflow.

Practical · Building a Memory-Enabled AI Writing Partner (Part 3): Security Architecture (RAG Protection, Fact Guard, and BYOK)

Wed, 04 Feb 2026 10:00:00 +0800

In the previous 2.5 articles, I’ve already laid out the backbone of FantasyNovelAgent:

This article dives deep into the most overlooked yet critical aspect of AI systems: Security.

If you’re thinking, “I’m just writing a novel, what security issues could there be?”, consider this:

Practical Guide: Building a Memory-Enabled AI Writing Partner (ikun) – Retrieval System (Vector Search, Hybrid Search & Cloud Deployment)

Wed, 28 Jan 2026 10:30:00 +0800

In “Practical · Building a Memory-Enabled AI Writing Partner (Part 1): Multi-Agent Architecture Evolution”, I clarified how multiple agents collaborate and how memory is chained together. In “Practical · Building a Memory-Enabled AI Writing Partner (Part 2): Database Evolution (From JSON to Single Database to Relational Tables)”, I reviewed the evolution of the “fact layer” from JSON to SQLite and then to relational tables.

However, when the text length reaches hundreds of thousands of words, what truly determines the experience is often not “whether the data exists,” but “whether I can retrieve it”: exact lookup (did it appear or not), structured filtering (who belongs to whom), and semantic association (is it similar, is it the same atmosphere) must all work simultaneously. So I added a clear “index layer” to FantasyNovelAgent and expanded retrieval from “chapters” to the “full knowledge graph.”

Hands-On: Building an Automated AI Semantic Search with Cloudflare Vectorize and Gemini

Fri, 23 Jan 2026 15:30:00 +0800

In 2026, adding AI search to a personal blog is nothing new. But achieving it with zero cost, full automation, and high performance remains a technical topic worth exploring.

This article breaks down the technical architecture behind this site’s AI Search feature, showing how to combine Cloudflare Workers, Vectorize, D1, and Google Gemini to build a closed-loop RAG (Retrieval-Augmented Generation) system.

1. Core Architecture Design

Our goal is a fully automated workflow: write and deploy. The author only needs to push Markdown articles; everything else—vector generation, index updates, frontend deployment—is automated.