Serving Online Inference with Text-Embeddings-Inference on Vast.ai | June 2024

Text-Embeddings-Inference is an open-source framework developed by Hugging Face for low latency, high throughput serving of embedding and reranker models, particularly useful in applications like retrieval-augmented generation (RAG), information retrieval, and search. The guide details the setup process for deploying an embedding model for online inference on Vast.ai, emphasizing the importance of selecting machines with specific capabilities such as a static IP, available ports, and a modern GPU, as well as requiring Cuda version 12.2 or higher. Users are instructed to deploy the model using the command line and can connect to their instance to test the setup by sending requests to the model via a specified IP address and port. Additionally, the guide demonstrates integrating embeddings into applications using the OpenAI SDK, which can generate embeddings by modifying the API key and base to work with the instance's API server. The guide concludes by highlighting the foundational role of embeddings and re-ranking in constructing AI applications and promises further exploration of these topics.

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