What is vector search? We explain everything developers should know about vector indexes, embeddings, and how to use them effectively with Pinecone. For many developers, the present problem is vector similarity search. The solution is Pinecone.| www.pinecone.io
Vector similarity search is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles — with incredible accuracy in sub-second timescales for billion+ size datasets.| www.pinecone.io
Learn the essentials of vector search and how to apply them in Faiss.| www.pinecone.io
Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together.| www.pinecone.io
Locality sensitive hashing (LSH) is a widely popular technique used in approximate nearest neighbor (ANN) search. The solution to efficient similarity search is a profitable one — it is at the core of several billion (and even trillion) dollar companies.| www.pinecone.io
Vector embeddings have proven to be an effective tool in a variety of fields, including natural language processing and computer vision. Comparing vector embeddings and determining their similarity is an essential part of semantic search, recommendation systems, anomaly detection, and much more.| www.pinecone.io
Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.| www.pinecone.io
Vector embeddings are one of the most fascinating and useful concepts in machine learning. They are central to many NLP, recommendation, and search algorithms. If you’ve ever used things like recommendation engines, voice assistants, language translators, you’ve come across systems that rely on embeddings.| www.pinecone.io
Learn how to build better retrieval augmented generation (RAG) pipelines for LLMs, search, and recommendation. In this chapter we explore two-stage retrieval and the incredible accuracy of reranker models.| www.pinecone.io
We are announcing Pinecone serverless, a completely reinvented vector database that lets you easily build fast and accurate GenAI applications.| www.pinecone.io
Generative AI sparked several “wow” moments in 2022. From generative art tools like OpenAI’s DALL-E 2, Midjourney, and Stable Diffusion, to the next generation of Large Language Models like OpenAI’s GPT-3.5 generation models, BLOOM, and chatbots like LaMDA and ChatGPT.| www.pinecone.io
Struggling with Large Language Models' hallucinations? Discover how Retrieval Augmented Generation (RAG) can enhance their performance!| www.pinecone.io
With similarity search, we can work with semantic representations of our data and find similar items fast. And in the sections below we will discuss how exactly it works.| www.pinecone.io
Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search[1]. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super fast search speeds and fantastic recall.| www.pinecone.io
Discover Vector Databases: How They Work, Examples, Use Cases, Pros & Cons, Selection and Implementation. They have combined capabilities of traditional databases and standalone vector indexes while specializing for vector embeddings.| www.pinecone.io