Tag
embeddings
Embeddings turn text, speech, or code into vectors that systems can compare for semantic search, retrieval-augmented generation, similarity matching, and vector databases. They also shape tasks like token initialization, ASR evaluation, and context retrieval in tools such as Redis Vector Search.
7 articles
Build a code-aware RAG pipeline with LangChain
Set up a code-aware retrieval augmented generation pipeline with LangChain.
Build semantic search with OpenSearch vectors
A step-by-step guide to set up OpenSearch vector search for semantic retrieval.
Why RAG Beats Prompting for Private Data
RAG is the right architecture for answering questions over private, changing data.
Vector Databases: How AWS Explains Them
AWS explains how vector databases store embeddings, power similarity search, and support Bedrock apps with OpenSearch Service.
How to Build a RAG Pipeline in 5 Steps
Build a retrieval-augmented generation pipeline that grounds AI answers in your own data.
Redis Vector Search: Quick Start Guide Explained
Redis can store vectors, metadata, and search them with semantic queries. This guide shows the setup, indexing, and KNN search path.
A Better Way to Seed New LM Tokens
GTI grounds new vocabulary tokens before fine-tuning, aiming to preserve distinctions that mean initialization tends to collapse.