Vector-Search

Introduction As language models are woven into more complex systems—multi-tool agents, retrieval-augmented generation, multi-model stacks—“what should handle this request?” becomes a first-class problem. That’s what a semantic router solves. Instead of routing based on keywords or simple rules, a semantic router uses meaning (embeddings, similarity, sometimes LLMs themselves) to decide: Which tool, model, or chain to call Which knowledge base to query Which specialized agent or microservice should own the request This post is a detailed, practical guide to semantic routers: ...

FAISS (Facebook AI Similarity Search) is an open-source library developed by Meta’s AI Research team for efficient similarity search and clustering of dense vectors, supporting datasets from small sets to billions of vectors that may not fit in RAM.[1][4][5] This comprehensive guide dives deep into FAISS’s architecture, indexing methods, practical implementations, optimizations, and real-world applications, equipping you with everything needed to leverage it in your projects. What is FAISS? FAISS stands for Facebook AI Similarity Search, a powerful C++ library with Python wrappers designed for high-performance similarity search in high-dimensional vector spaces.[4] It excels at tasks like finding nearest neighbors, clustering, and quantization, making it ideal for recommendation systems, image retrieval, natural language processing, and more.[5][8] ...

Vector search in PostgreSQL has gone from “experimental hack” to a serious production option, largely thanks to the pgvector extension. But as teams push from thousands to tens or hundreds of millions of embeddings, a natural question emerges: How do you keep vector search fast and cost‑effective as the dataset grows, without adding yet another external database? This is exactly the problem pgvectorscale is designed to address. In this article, we’ll take a detailed look at pgvectorscale: what it is, how it fits into the Postgres ecosystem, how it scales vector search, and what trade‑offs you should understand before using it. ...

Table of contents Introduction What is RAG (Retrieval-Augmented Generation)? Why RAG matters: strengths and limitations Core RAG components and pipeline Retriever types Vector stores and embeddings Indexing and metadata Reader / generator models Orchestration and caching Chunking strategies (text segmentation) Fixed-size chunking Overlap and stride Semantic chunking Structure-aware and LLM-based chunking Practical guidelines Embeddings: models, training, and best practices Off-the-shelf vs. fine-tuned embeddings Dimensionality, normalization, and distance metrics Handling multilingual and multimodal data Vector search and hybrid retrieval ANN algorithms and trade-offs Hybrid (BM25 + vector) search patterns Scoring, normalization, and retrieval thresholds Reranking and cross-encoders First-stage vs. second-stage retrieval Cross-encoder rerankers: when and how to use them Efficiency tips (distillation, negative sampling) Query rewriting and query engineering User intent detection and canonicalization Query expansion, paraphrasing, and reciprocal-rank fusion Multi-query strategies for coverage Context management and hallucination reduction Context window budgeting and token economics Autocut / context trimming strategies Source attribution and provenance Multi-hop, iterative retrieval, and reasoning Decomposition and stepwise retrieval GraphRAG and retrieval over knowledge graphs Chaining retrievers with reasoning agents Context distillation and chunk selection strategies Condensing retrieved documents Evidence aggregation patterns Using LLMs to produce distilled context Fine-tuning and retrieval-aware training Fine-tuning LLMs for RAG (instruction, RLHF considerations) Training retrieval models end-to-end (RAG-style training) Retrieval-augmented pretraining approaches Memory and long-term context Short-term vs. long-term memories Vector memories and episodic memory patterns Freshness, TTL, and incremental updates Evaluation: metrics and test frameworks Precision / Recall / MRR / nDCG for retrieval Factuality, hallucination rate, and human evaluation for generation Establishing gold-standard evidence sets and benchmarks Operational concerns: scaling, monitoring, and safety Latency and throughput optimization Cost control (compute, storage, embedding calls) Access control, data privacy, and redaction Explainability and user-facing citations Advanced topics and research directions Multimodal RAG (images, audio, tables) Graph-based retrieval and retrieval-aware LLM architectures Retrieval for agents and tool-use workflows Recipes: end-to-end examples and code sketches Minimal RAG pipeline (conceptual) Practical LangChain / LlamaIndex style pattern (pseudo-code) Reranker integration example (pseudo-code) Troubleshooting: common failure modes and fixes Checklist: production-readiness before launch Conclusion Resources and further reading Introduction This post is a practical, end-to-end guide to Retrieval-Augmented Generation (RAG). It’s aimed at engineers, ML practitioners, product managers, and technical writers who want to go from RAG basics to advanced production patterns. The goal is to provide both conceptual clarity and hands-on tactics so you can design, build, evaluate, and operate robust RAG systems. ...

Introduction Retrieval-Augmented Generation (RAG) pairs a retriever (to fetch relevant context) with a generator (an LLM) to produce accurate, grounded answers. This pattern reduces hallucinations, lowers inference costs by offloading knowledge into a searchable store, and makes updating knowledge as simple as adding or editing documents. In this guide, we’ll move from beginner-friendly RAG to advanced techniques, with practical code examples along the way. We’ll cover chunking, embeddings, vector stores, hybrid retrieval, reranking, query rewriting, multi-hop reasoning, GraphRAG, production considerations, and evaluation. A final resources chapter includes links to papers, libraries, and tools. ...