Welcome back! In our previous lesson, we explored hybrid retrieval, which combines metadata and vector search to enhance the accuracy and relevance of search results. Today, we will focus on reranking, a technique that further refines search results by ordering them based on relevance. This lesson will build on your existing knowledge of vector search and introduce you to the practical implementation of reranking using cosine similarity.

Reranking is an essential step in semantic search systems. While initial search results may be relevant, reranking ensures that the most pertinent results appear at the top. This is particularly important in scenarios where users expect quick and accurate answers. By leveraging cosine similarity, we can measure the relevance of each document to the query and reorder the results accordingly. Let's dive into how this works.

As you already know, cosine similarity is a metric that measures the similarity between two vectors, with scores ranging from -1 (completely opposite) to 1 (identical). It helps us assess the relevance of documents to a query.

Now, let's implement reranking using cosine similarity. We'll use the rerank_results function to reorder search results based on their relevance to a query. Here's the code provided in src/main.py:

In this function, we first compute the cosine similarity scores between the query embedding and each document embedding. These scores indicate how relevant each document is to the query. We then sort the documents based on these scores in descending order, ensuring that the most relevant documents appear first.

Let's walk through a practical example using the provided code. Suppose we have a query about "Latest AI trends" and a set of retrieved documents. We want to rerank these documents to ensure the most relevant ones are at the top.

Output:

In this example, we first load the embedding model and encode the query and documents into embeddings. We then call the rerank_results function to reorder the documents based on their cosine similarity scores. The output displays each document along with its relevance score, allowing us to see which documents are most pertinent to the query.

In this lesson, we explored the concept of reranking and its role in enhancing search accuracy by ordering results based on relevance. We implemented a reranking function using cosine similarity and demonstrated its application with a practical example. This technique ensures that users receive the most relevant search results, improving their overall experience.

As you move on to the practice exercises, focus on applying what you've learned about reranking. Experiment with different queries and document sets to see how they affect the search results. Mastering reranking will provide a strong foundation for more advanced search techniques in future lessons.