Vector Search

An in-depth analysis of IBM's Chunkless RAG and Docling layout parsing versus traditional chunking, hybrid retrieval, and query expansion techniques for production enterprise AI applications.

Most RAG system failures are not caused by LLM hallucinations, but by silent breakdowns in the retrieval pipeline during document parsing, chunking, vector search, and reranking.

Building Retrieval-Augmented Generation (RAG) for voice AI demands a complete architectural shift compared to traditional enterprise search. Learn how latency budgets, offline indexing, dynamic windowing, and high-performance LLM routing redefine voice agent systems.

Learn how to build a robust hybrid search pipeline in Postgres 17 using pgvector 0.8 and tsvector. Discover how Reciprocal Rank Fusion (RRF) fixes exact-match failures in RAG applications without needing a dedicated vector database.

Retrieval-Augmented Generation (RAG) solves knowledge access, not application reliability. Learn the 9 essential system design patterns—from intent routing and deterministic validation to pre-retrieval ACLs—to build production-grade, predictable AI applications.

An in-depth technical evaluation of NeoMME, an efficient multimodal-native and multilingual encoder designed for cross-lingual image-text retrieval, visual RAG, and high-performance vector search.

Learn how to improve Retrieval-Augmented Generation (RAG) performance on tabular data by splitting tables into row-level chunks with injected headers, reducing noise and token usage.

An in-depth look at how rebuilding a knowledge base using LLM-driven distillation and 'bursting' high-signal comments can improve recall ceilings, even if initial precision metrics drop.

Learn how to move beyond simple vector search by implementing hybrid retrieval, reciprocal rank fusion (RRF), and cross-encoder reranking for production-grade RAG systems.

Learn how to build a high-performance, cost-effective semantic search system using SQLite and local embeddings, eliminating the need for expensive cloud vector databases.

Discover why AI engineers are shifting from complex vector databases to sqlite-vec for building autonomous agents. Learn how to implement high-performance, local-first RAG memory with zero external dependencies.

A deep dive into moving beyond basic semantic search to a production-grade RAG pipeline using hybrid retrieval, agentic chunking, and Bayesian hyperparameter optimization.