Every Rust ANN library I found had the same problem. None of them would let me change the distance function.I’m building OmniPulse, a content fingerprinting system that uses Wavelet Scattering Transform to identify audio. I needed to search a large ind…
Vector Search Done Right: Best Practices, Qwen3 Dimension Control, and Why Reranking Is Non-NegotiableThree things your RAG pipeline on Databricks needs to get right — and why most pipelines get at least one of them wrong.The Problem With “Good Enough”…
Adding content staleness tracking, CDC-based updates, and recency-weighted retrieval to a Databricks RAG pipelineYou built a RAG system. It parses PDFs, chunks them, embeds them, retrieves relevant context. It even remembers conversations across turns …
Your RAG Agent Forgets Everything After One Message – Here’s How I Fixed It with Databricks LakebaseBuilding a context-aware RAG system end-to-end: from PDF parsing to multi-turn conversations that actually rememberMost RAG tutorials show you how to bu…
source: OpenAI GPT Image 2 modelYou have built a RAG pipeline. You chunked your documents, picked an embedding model, and wired up a vector database. You ask a question, the retriever pulls back 50 chunks, and you stuff the top 5 into your LLM prompt. …
Two similarity score formulas. One silent assumption. A ~270× discrepancy waiting to bite you.If you’ve read the Databricks Vector Search documentation, you’ve probably come across two similarity score formulas:Cosine-form: score = 1 / (3 − 2·cosθ)Eucl…
The Day My AI Stopped Guessing and Started Finding Answers (Vector Search)Continue reading on Medium »
Table of Contents Semantic Caching for LLMs: FastAPI, Redis, and Embeddings Introduction: Why Semantic Caching Matters for LLM Systems How Semantic Caching Works for LLMs: Embeddings and Similarity Search Explained Semantic Caching Architecture and Request Flow Configuring Your Environment for…
The post Semantic Caching for LLMs: FastAPI, Redis, and Embeddings appeared first on PyImageSearch.
If you’ve been following the “long-context” wave in AI, you’ve probably heard the same story: bigger context windows feel magical… until…Continue reading on Towards AI »
Moving from CNN’s Low-Level Visual Features to Deep Semantic Embeddings with SigLIP.Image by the author.Convolutional Neural Networks (CNNs) have important semantic limitations: while they capture low and mid-level visual features (such as edges, textu…