Reranking in Information Retrieval: Enhancing Search Results with RAG

Reranking is a transformative technique in information retrieval (IR) that enables more accurate sorting of retrieved documents by evaluating their relevance to the query, adding significant value over initial retrieval results. In this article, we’ll explore the purpose and functionality of rerankers, the limitations of embeddings, and walk through code examples using Python and APIs from OpenAI, CohereRerank reranker and FlashRank reranker. Flashrank does not require a third party API and is very convenient to implement.

Why Reranking is Needed in Information Retrieval

Embeddings, commonly used in initial document retrieval, represent documents in a low-dimensional vector space to capture semantic similarity. Despite their utility, embeddings are constrained by limited dimensionality and can struggle with nuanced context. Rerankers address these limitations by reordering documents retrieved in the first pass based on refined query-document relevance.

Limitations of Embeddings in IR

1. Limited Semantic Distinction: Embeddings may not capture fine-grained differences. For instance, “I love apples” and “I used to love apples” may appear similar due to overlapping terms.
2. Dimensional Constraints: Embedding spaces have fixed dimensions (e.g., 1024), which limits the context they can encode, especially with long queries or documents.