Description

Text embeddings are a key component of production-ready applications using large language models. They transform chunks of text into vectors of floating-point numbers that represent their semantic meaning, allowing for quantitative comparison of strings for similarity. Creating embeddings on a large corpus of text enables the building of applications like search and recommendation engines, as well as providing additional context to LLMs for Retrieval-Augmented Generation (RAG) on custom documents. This tutorial by Jason Liu explains how to embed the entire English Wikipedia in under 15 minutes using Hugging Face’s Text Embedding Inference service on Modal. The process involves setting up a serverless solution that can handle large-scale workloads and generate embeddings for a massive text dataset at lightning speed, which is crucial for continuous model fine-tuning in production use cases.