使用 Hugging Face Transformers 與 Ray 實現大規模檢索增強生成 (RAG)
Original: Retrieval Augmented Generation with Huggingface Transformers and Ray
Retrieval-Augmented Generation (RAG) is a powerful architecture that combines a "retriever" with a "generator." It enables language models…
Hugging Face 與 Anyscale 合作，展示如何利用 Ray 框架來擴展檢索增強生成（RAG）模型。 透過將 Ray 的分散式運算能力與 Hugging Face 的 NLP 模型結合，開發者可以高效地在海量知識庫中進行向量檢索與文本生成。 此方案解決了 RAG 在處理大規模知識庫（如完整維基百科）時的記憶體限制與運算瓶頸，顯著提升查詢吞吐量。
Retrieval-Augmented Generation (RAG) is a powerful architecture that combines a "retriever" with a "generator." It enables language models to dynamically retrieve relevant information from an external knowledge base (such as Wikipedia or internal corporate documents) when generating responses, resulting in answers that are more accurate, up-to-date, and less prone to hallucination. However, when the knowledge base is extremely large, efficiently loading a vector index (such as FAISS) and performing real-time retrieval becomes a significant engineering challenge.
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