使用 Hugging Face Inference Endpoints 輕鬆部署高效能嵌入模型
Original: Deploy Embedding Models with Hugging Face Inference Endpoints
As large language models (LLMs) and Retrieval-Augmented Generation (RAG) technology become increasingly widespread, embedding models have…
Hugging Face 宣布其 Inference Endpoints 正式支援高效部署嵌入模型（Embedding Models）。此服務整合了 Text Embeddings Inference (TEI) 技術，提供極低的延遲、動態批處理與高吞吐量。開發者只需幾鍵即可在專屬雲端基礎設施（如 AWS 或 Azure）上部署開源嵌入模型，極大簡化了 RAG（檢索增強生成）與向量搜尋系統的建置流程。
As large language models (LLMs) and Retrieval-Augmented Generation (RAG) technology become increasingly widespread, embedding models have become an indispensable foundation of modern AI application architectures. To simplify the challenge of deploying and scaling these models, Hugging Face has announced the official launch of an optimized deployment solution for embedding models within its hosting service, Inference Endpoints.
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