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AI Architect Hub

Discuss AI and architecture; a ten-year veteran of major tech companies now transitioning to AI and continuing the journey.

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Recent Articles

Latest from AI Architect Hub

18 recent articles
AI Architect Hub
AI Architect Hub
May 3, 2026 · Artificial Intelligence

Choosing the Right Vector Database: Milvus, Chroma, Weaviate, Qdrant, FAISS Compared

This article compares five popular vector databases—Chroma, Milvus, Weaviate, Qdrant, and FAISS—detailing their positions, strengths, weaknesses, suitable scenarios, a selection‑dimension matrix, common pitfalls, code implementations for a unified RAG pipeline, best‑practice recommendations, and thought questions to guide engineers in choosing and migrating vector stores.

ChromaFAISSMilvus
0 likes · 23 min read
Choosing the Right Vector Database: Milvus, Chroma, Weaviate, Qdrant, FAISS Compared
AI Architect Hub
AI Architect Hub
Apr 30, 2026 · Artificial Intelligence

How AI Understands Your Queries: Core Techniques of Semantic Vector Search

The article explains why traditional keyword search often fails when user questions differ from knowledge‑base wording, introduces semantic search that matches queries and documents via vector similarity, details query understanding and rewriting techniques, lists common pitfalls, provides a full Python implementation, and shares best‑practice recommendations.

AIPythonRAG
0 likes · 16 min read
How AI Understands Your Queries: Core Techniques of Semantic Vector Search
AI Architect Hub
AI Architect Hub
Apr 30, 2026 · Operations

Comprehensive Guide to Installing Hermes and Its Web UI

This tutorial walks through installing Hermes with a single curl command that checks Python 3.11+, installs Node v23+, sets up a virtual environment, configures channels like WeChat, QQ and Feishu, and then adds the open‑source Hermes Web UI via npm for full model configuration and chat capabilities.

HermesInstallationNode
0 likes · 3 min read
Comprehensive Guide to Installing Hermes and Its Web UI
AI Architect Hub
AI Architect Hub
Apr 29, 2026 · Artificial Intelligence

Hermes Agent Guide: Install, Model Integration, Skills, MCP, Message Gateway

This comprehensive guide walks you through installing Hermes Agent, configuring over 20 model providers, setting up the Skill system, enabling MCP client/server modes, and connecting the agent to 17 messaging platforms, while also covering execution back‑ends, security settings, and troubleshooting tips.

AI agentHermes AgentInstallation
0 likes · 20 min read
Hermes Agent Guide: Install, Model Integration, Skills, MCP, Message Gateway
AI Architect Hub
AI Architect Hub
Apr 27, 2026 · Artificial Intelligence

Why HNSW Can Speed Up Search 50× Compared to Brute‑Force? A Hands‑On Guide to Building Vector Indexes

The article explains why brute‑force vector search is painfully slow, introduces Flat, IVF, and HNSW index structures, compares their speed, memory and accuracy, shows common pitfalls, provides production‑grade Python code, and presents benchmark results that demonstrate HNSW’s superior speed‑accuracy trade‑off.

AIFAISSHNSW
0 likes · 12 min read
Why HNSW Can Speed Up Search 50× Compared to Brute‑Force? A Hands‑On Guide to Building Vector Indexes
AI Architect Hub
AI Architect Hub
Apr 26, 2026 · Artificial Intelligence

Embedding Explained: How Vectorization Turns Text into Numbers for RAG

This article walks through why traditional keyword matching fails for RAG, explains the evolution from one‑hot encoding to Word2Vec and BERT, details sentence‑level embeddings and similarity metrics, compares leading Chinese and multilingual embedding models using the C‑MTEB benchmark, and provides practical LangChain code, deployment tips, and common pitfalls.

Chinese NLPEmbeddingLangChain
0 likes · 18 min read
Embedding Explained: How Vectorization Turns Text into Numbers for RAG
AI Architect Hub
AI Architect Hub
Apr 25, 2026 · Artificial Intelligence

How to Feed Massive Documents to an RAG System: Mastering the Art of Text Chunking

This article explains why proper text chunking is critical for Retrieval‑Augmented Generation, illustrates common pitfalls with real‑world examples, compares four chunking strategies (fixed length, recursive, structure‑aware, and code‑aware), and provides practical guidelines for chunk size, overlap, metadata handling, and a production‑ready pipeline.

AI RetrievalLangChainRAG
0 likes · 21 min read
How to Feed Massive Documents to an RAG System: Mastering the Art of Text Chunking
AI Architect Hub
AI Architect Hub
Apr 24, 2026 · Artificial Intelligence

RAG Level 1: Avoid Dirty Data Poisoning Your AI – A Data Cleaning Guide

This article explains why noisy documents cripple Retrieval‑Augmented Generation, enumerates common garbage data types, describes three typical data‑quality problems, warns against over‑cleaning, encoding, and regex pitfalls, and provides a configurable LangChain pipeline with deduplication and validation best practices.

AIData cleaningDeduplication
0 likes · 21 min read
RAG Level 1: Avoid Dirty Data Poisoning Your AI – A Data Cleaning Guide
AI Architect Hub
AI Architect Hub
Apr 21, 2026 · Artificial Intelligence

How to Choose the Right Embedding Model for RAG: A Practical Comparison

This article examines the key factors for selecting embedding models in Retrieval‑Augmented Generation, comparing dimensions, context windows, MTEB scores, pricing, and language support across major providers, and offers practical recommendations, cost estimates, and pitfalls to avoid.

AIOpen SourceRAG
0 likes · 11 min read
How to Choose the Right Embedding Model for RAG: A Practical Comparison