Vector-Database

Abstract illustration of a neural network with retrieval arrows

Building a Speculative RAG Decoder with Retrieval-Token Speculation

This tutorial shows how to combine retrieval‑augmented generation with speculative decoding to cut latency while preserving factual grounding. You’ll build a runnable system that demonstrates real‑world performance gains.

A network of connected nodes representing a knowledge graph replacing a flat vector index.

Graphify Replaces the RAG Vector Store: Why Graph-Based Retrieval Is the Next Leap

Graph-based retrieval is displacing the pure vector store in production RAG stacks. Here is how Graphify works, why it outperforms flat embeddings on multi-hop questions, and what it takes to migrate.

Diagram of a RAG pipeline with ingestion, embedding, vector store, and LLM generation stages.

Architecting Production-Ready Retrieval-Augmented Generation Systems

How to build RAG systems that survive real production traffic: chunking strategies, hybrid retrieval, vector store selection, caching, and the operational patterns teams actually ship.

Diagram of a sharded vector database cluster handling billions of embeddings.

Architecting Distributed Vector Databases: Scalability Patterns and Infrastructure for High-Volume Semantic Search

A deep dive into the architecture, scaling patterns, and operational best‑practices for distributed vector stores that enable real‑time semantic search at scale.

Architecting Distributed Vector Database Systems: Engineering Reliable Infrastructure for Scalable Semantic Search Pipelines

A deep dive into building reliable, scalable vector database backends that power modern semantic search pipelines.