Titan Text Embeddings V2

Titan Text Embeddings V2 converts words, sentences, and documents into fixed-length numeric vectors that capture meaning rather than keywords. Those vectors make it easy to build accurate semantic search, retrieval-augmented generation, deduplication, reranking, topic discovery, and personalization. In a typical RAG pipeline you chunk content, embed it with Titan, store the vectors in a database such as OpenSearch Serverless or pgvector, and retrieve the nearest neighbors at query time using cosine similarity; the model is trained to keep related ideas close in vector space, even across languages and phrasing. It’s engineered for production on Bedrock—fast inference, batching for throughput, private VPC networking, encryption, and enterprise monitoring—so you can scale indexing jobs and real-time queries without custom infrastructure. Good hygiene still matters: keep chunk sizes consistent, normalize text, and store the embedding norm (or L2-normalize) to ensure stable similarity scores. When content changes, re-embed the affected chunks rather than whole corpora to control costs. If you need robust, multilingual embeddings that slot neatly into AWS services and common vector stores, Titan Text Embeddings V2 is the straightforward choice.