ure the query-document interaction as effectively.Lightweight Models: Lightweight rerankers, such as distilled models or small transformer variants, aim to strike a balance between effectiveness and efficiency. They are faster and have a smaller footprint compared to large cross-encoders, making them suitable for real-time applications.

Benefits of Reranking in RAG

Reranking offers several benefits in the context of RAG:

Improved Relevance: By considering additional features and scores, rerankers can identify the most relevant documents for a given query, ensuring that the LLM has access to the most informative context for generating accurate responses.

Semantic Understanding: Rerankers can capture the semantic meaning and context of the query and documents, going beyond simple keyword matching. This enables the retrieval of documents that are semantically similar to the query, even if they don't contain exact keyword matches.

Domain Adaptation: Rerankers can be fine-tuned on domain-specific data to incorporate domain knowledge and improve performance in specific verticals or industries.

Personalization: Rerankers can be personalized based on user preferences, historical interactions, or user profiles, enabling the retrieval of documents that are more tailored to individual users' needs.

In the next section, we'll dive into how to implement reranking in ZenML and integrate it into your RAG inference pipeline.

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Last updated 15 days ago