#Vector Search

ColBERT v2 Late Interaction: Multi-Vector Token Search vs Dense Cosine Similarity

A deep information retrieval performance guide to ColBERT v2. We analyze token-level multi-vector representations, MaxSim operator mechanics, PLAID index compression, and sub-15ms re-ranking over millions of passages.

Embeddings Explained: Addresses for Meanings & High-Dimensional Semantic Vectors (2026)

Agentic RAG with LangGraph: Hybrid Vector + BM25 Search & Self-Correction Loops

Naive RAG fails in production. Learn how to architect Agentic RAG using LangGraph, hybrid sparse-dense retrieval (BM25 + Cohere Rerank), and query rewriting loops.

From RAG to Agentic Retrieval: How Retrieval Patterns Evolved in 2026

A single embed-and-retrieve step was never going to be the final form of grounding an LLM in real data. Here's how retrieval architectures actually changed as teams ran into its limits.

On-Device Vector Search: SQLite + AI Embeddings in Flutter Apps

Build private, offline-capable semantic search in Flutter using SQLite vector extensions and local embedding models — no cloud API required.

Deploying Gemini 3.5 Flash in Web Workers for Local Vector Search

Learn how to deploy Google's Gemini 3.5 Flash inside browser Web Workers. Build offline vector embedders and local-first semantic search indices.

Local Vector Search in SQLite: Leveraging sqlite-vss for Edge AI Applications

Ditch heavy vector databases. Learn how to execute lightning-fast, local semantic search directly inside SQLite databases using the sqlite-vss extension.

Building Production RAG Evaluation: Ragas vs TruLens vs G-Eval Metrics & Triad Benchmarks (2026)

Self-Hosted Embeddings at Scale: Hugging Face TEI (Text Embeddings Inference) in Rust & CUDA (2026)

Vector-First Search Architecture in 2026: High-Performance Qdrant & Next.js Hybrid Retrieval