🦹 Re-rank retrieved results

Re-rankers are specialized models that are trained to calculate the relevance between query-document pairs. Without re-ranking the order of retrieved results is governed by the embedding model, which isn't optimized for relevance and can lead to poor LLM recall in RAG applications.

Fill in any <CODE_BLOCK_N> placeholders and run the cells under the 🦹‍♀️ Re-rank retrieved results section in the notebook to add a re-ranking stage to the RAG application.

The answers for code blocks in this section are as follows:

CODE_BLOCK_19

Answer

rerank_model.rank(
    user_query, documents, return_documents=True, top_k=5
)