Best Embedding Models for Kubernetes

txtai, an open-source embeddings database, is designed for semantic search and large language model orchestration. It also supports language model workflows. It unifies vector indices (both dense and sparse), graph networks, relational databases and provides a robust foundation to vector search. Users can create autonomous agents, implement retrieval augmented creation processes, and develop multimodal workflows with txtai. The key features include vector searching with SQL support, object-storage integration, topic modeling and graph analysis, as well as multimodal indexing capabilities. It allows the creation of embeddings from various data types including text, audio, images and video. txtai also offers pipelines powered with language models to handle tasks like LLM prompting and question-answering. It can also be used for labeling, transcriptions, translations, and summaries.