Recent posts tagged vector-search

In Part 2https://coderlegion.com/21936/ai-fundamentals-part-2-why-ai-gets-things-right-and-wrong, we learned why AI sometimes hallucinates. One of the biggest reasons is that an LLM can only answer based on what it learned during training and the inf...

Intro: We need to talk about your "Modern Stack" Let’s be real for a second. We’ve all been there: You have a "simple" project idea. Two hours later, you’re staring at a Docker Compose file with PostgreSQL, Redis for caching, Neo4j because "graphs a...

Oracle Database 23ai's AI Vector Search provides multiple strategies for finding similar vectors, each with different trade-offs between accuracy, speed, and resource usage. Understanding when to use exact search, approximate search, or multi-vector ...

Oracle Database 23ai introduces native vector capabilities that enable semantic search directly within SQL. Understanding how to create vectors, calculate distances, and choose appropriate metrics is fundamental to building effective AI-powered appli...

Oracle Database 23ai introduces the VECTOR data type, enabling you to store AI embeddings alongside traditional business data. Understanding how to perform Data Manipulation Language DML and Data Definition Language DDL operations on vector columns i...

Oracle Database 23ai's AI Vector Search enables powerful semantic search capabilities, but to truly harness its potential, you need to understand how to query vector data efficiently and when to use vector indexes. This guide explores querying vector...

Oracle AI Vector Search Workflow: From Data to Semantic Search Oracle Database 23ai introduces AI Vector Search, enabling semantic search capabilities directly within the database. Unlike traditional keyword-based searches, vector search understand...

Semantic search represents a fundamental shift in how we retrieve information from databases and search engines. Unlike traditional keyword-based search that relies on exact text matches, semantic search understands the meaning and context behind que...

Oracle Database 23ai formerly 23c represents a significant leap forward in integrating artificial intelligence capabilities directly into the database. One of its most groundbreaking features is AI Vector Search, which enables the database to underst...

Retrieval-Augmented Generation RAG has become the default approach for providing context and memory to AI applications. Understanding the RAG pipeline—from document ingestion to final response generation—is essential for building high-performance AI ...

AI is only useful when it answers with the right information. A vector database makes that possible. Instead of searching by keywords, it searches by meaning. That’s how AI finds the most relevant data from your docs, pages, chats, and files. With...

Oracle Cloud Infrastructure OCI is revolutionizing how enterprises approach artificial intelligence, providing comprehensive AI services that integrate seamlessly with existing business processes. This introduction explores the fundamental concepts o...

Have you ever wondered how search engines like Google are changing with the rise of artificial intelligence AI? Platforms like ChatGPT and Perplexity are leading the way in this new era of search. For SEO experts, this shift presents a huge opportuni...

This post was written by James Luan1, VP of Engineering at Zilliz2 and key maintainer of the Milvus open source project. The Evolution of Modern Search Architecture You're debugging a critical production issue, and you know the solution exists in...