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Original rewrites on React, Node.js, TypeScript and AI — practical notes from the same engineering practice behind our operator software. Article bodies are in English.
Tagged: Semantic Search
How a Vector Database Works: Embeddings, Vector Search and Human Memory
A memory-based mental model for vector search: how embeddings, similarity, nearest neighbor indexes and metadata filters work inside a vector database for RAG.
9 min readWhy an LLM Call Is Not an App: Where LangChain Fits in a RAG Pipeline
Learn what LangChain actually does by tracing a document question-answering app from PDF upload to grounded answer, and see when an alternative framework fits better.
11 min readHow Vector Databases Really Work: From Embeddings to Hybrid Search
Explains how embeddings encode meaning, how similarity search and indexing scale, and when hybrid search and vector databases actually fit enterprise AI systems.
8 min readBenchmarking Vector Databases for High-Throughput Semantic Search
Learn a practical Node.js and Python methodology for benchmarking vector databases under realistic load to guide sound architectural and scaling decisions.
14 min readVector Databases Explained: How Machines Search by Meaning
How a vector database turns text into embeddings so semantic search finds results by meaning, and how it differs from a traditional row-and-index database.
7 min readRAG Explained: How AI Systems Retrieve Fresh Knowledge on Demand
How retrieval-augmented generation works, from chunking and embeddings to a vector database and semantic search, so models answer well without retraining.
13 min read
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