The Sequence Engineering #488: Txtai, Maybe the Simplest Way to do Embeddings
Was this email forwarded to you? Sign up here The Sequence Engineering #488: Txtai, Maybe the Simplest Way to do EmbeddingsA simple and developer friendly framework for building embeddings into LLM apps.
Embeddings are a critical component of any generative AI applications. The market has been floated with many vector databases and other platforms. There is an entire argument about whether that market can survive as a standalone ecosystem but that’s a debate for another day. Simplicity and developer friendliness and two of the main characteristics that I look for in embedding frameworks when building generative AI apps. And today, I would like to cover a new framework that really stands out in both of those areas. txtai is an open-source embeddings database that integrates semantic search, LLM orchestration, and language model workflows1. It's designed to be an all-in-one solution, combining vector indexes (both sparse and dense), graph networks, and relational databases. This foundation allows for both vector search and serves as a knowledge base for large language model (LLM) applications2. The goal is to provide a platform for building autonomous agents, retrieval augmented generation (RAG) systems, and multi-model workflows... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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