Re-ranking

Many search systems are built on two-stage retrieval pipelines.

The first stage uses cheap, fast algorithms to find a broad set of possible matches.

The second stage uses a more powerful model, often machine learning-based, to reorder the documents. This second step is called re-ranking. Because the resource-intensive model is only applied to the smaller set of pre-filtered results, this approach returns more relevant results while still optimizing for search performance and computational costs.

Elasticsearch supports various ranking and re-ranking techniques to optimize search relevance and performance.

Elasticsearch ranks documents based on term frequency and inverse document frequency, adjusted for document length. BM25 is the default statistical scoring algorithm in Elasticsearch.

Vector search involves transforming data into dense or sparse vector embeddings to capture semantic meanings, and computing similarity scores for query vectors. Store vectors using semantic_text fields for automatic inference and vectorization or dense_vector and sparse_vector fields when you need more control over the underlying embedding model. Query vector fields with semantic, knn or sparse_vector queries to compute similarity scores. Refer to semantic search for more information.

Hybrid search techniques combine results from full-text and vector search pipelines. Elasticsearch enables combining lexical matching (BM25) and vector search scores using the Reciprocal Rank Fusion (RRF) algorithm.

When using the following advanced re-ranking pipelines, first-stage retrieval mechanisms effectively generate a set of candidates. These candidates are funneled into the re-ranker to perform more computationally expensive re-ranking tasks.

Semantic re-ranking uses machine learning models to reorder search results based on their semantic similarity to a query. Models can be hosted directly in your Elasticsearch cluster, or you can use inference endpoints to call models provided by third-party services. Semantic re-ranking enables out-of-the-box semantic search capabilities on existing full-text search indices.

Learning To Rank is for advanced users. Learning To Rank involves training a machine learning model to build a ranking function for your search experience that updates over time. LTR is best suited for when you have ample training data and need highly customized relevance tuning.