Introducing txtai, the all-in-one embeddings database

This is an updated version of the <a href="https://medium.com/neuml/introducing-txtai-an-ai-powered-search-engine-built-on-transformers-37674be252ec" rel="noopener">original article</a>. Search is the base of many applications. Once data starts to pile up, users want to be able to find it. It’s the foundation of the internet and an ever-growing challenge that is never solved or done. The field of Natural Language Processing (NLP) is rapidly evolving with a number of new developments. Large-scale general language models are an exciting new capability allowing us to add amazing functionality. Innovation continues with new models and advancements coming in at what seems a weekly basis. This article introduces txtai, an all-in-one embeddings database that enables Natural Language Understanding (NLU) based search in any application. <h1>Introducing txtai</h1> <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*cBxmgtJEZsgk2sbUAYyMOw.png" style="height:147px; width:700px" /> <a href="https://github.com/neuml/txtai" rel="noopener ugc nofollow" target="_blank">txtai</a> is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases. This enables vector search with SQL, topic modeling, retrieval augmented generation and more. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. The following is a summary of key features: <a href="https://medium.com/neuml/introducing-txtai-the-all-in-one-embeddings-database-c721f4ff91ad">Read More</a>