If you have built a RAG (Retrieval-Augmented Generation) system, you have likely faced the “Lost in the Middle” problem. You ask your AI a question. It retrieves 10 documents. But the one document that actually contains the answer is buried at position #7. The LLM gets distracted by the noise in documents #1–6 and gives […]
The Accuracy Booster: Demystifying Cross-Encoder Reranking Read More »
Vector search is the technology powering the most intelligent applications we use daily—from product recommendations and image lookups to smart chatbots. But what happens when you need to search millions, or even billions, of these data points instantly?
The answer lies in two powerful techniques: Approximate Nearest Neighbor (ANN) and the revolutionary HNSW (Hierarchical Navigable Small-World Graph) algorithm.
ANN is a fast shortcut method that finds the “closest matches” to your query based on meaning (vectors), not just exact keywords. HNSW takes this a step further, acting like a multi-level navigation system in a massive data supermarket.
Instead of scanning every item, HNSW quickly travels from the top-level categories (sports, electronics) down to the exact shelf (running shoes) in milliseconds.
Dive into the full post to understand exactly how ANN and HNSW make lightning-fast, highly accurate AI search possible.
Vector search – ANN & HNSW Read More »
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat
The biggest shifts and trends driving short- and long-term growth in the future Read More »
Introduction: The “Roadblock” Problem In vector search, we often talk about speed and accuracy. But there is a major flaw in the most popular algorithm (HNSW) that most people don’t realize until they go into production. HNSW is great at finding “Similar items.” But it is terrible at finding “Similar items… that also match a
Why Filtering Breaks HNSW — and How ACORN Fixes It Read More »
