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
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
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