Deployment and inference for open source text embedding models

Text embedding models transform text into vectors that represent its semantic meaning, enabling various use cases such as search, retrieval-augmented generation with LLMs, recommendations, classification, and clustering. These models encode chunks of text into vectors using tokenization, context windows, dimensionality, and similarity functions. Choosing the right model depends on the use case and compute resources, with popular open-source models like all-MiniLM-L6-v2, all-mpnet-base-v2, jina-embeddings-v2-base-en, LEALLA-base, and instructor-xl available for different applications. Packaging these models into Truss allows for easy deployment and inference, making it possible to create embeddings from a corpus of text and compare their similarity using various methods.

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