Deploying and Using the Rerank Model

Author(s): zhaozhiming

Originally published on Towards AI.

In the Retrieval-Augmented Generation (RAG) process, the Rerank model plays a critical role. A typical RAG might retrieve a plethora of documents, not all of which are necessarily relevant to the query. Rerank steps in to reorganize and filter these documents, ensuring the most relevant ones are prioritized, thereby enhancing the effectiveness of RAG. This piece will detail deploying the Rerank model using HuggingFace’s Text Embedding Inherence tool and showcase how to integrate Rerank functionality into LlamaIndex’s RAG.

RAG is a language model technique that combines information retrieval with text generation. In essence, when you pose a question to a large language model (LLM), RAG first searches for relevant information in a vast collection of documents and then generates an answer based on this information.

Rerank acts like an intelligent filter. When RAG retrieves multiple documents from its collection, these documents may vary in their relevance to your question. Some might be highly pertinent, while others might be only marginally related or even irrelevant. Rerank’s job is to assess the relevance of these documents and reorder them accordingly. It prioritizes those most likely to provide accurate, relevant responses. In layman’s terms, Rerank is like a librarian who helps you pick out the… Read the full blog for free on Medium.

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Published via Towards AI