In this series of guides we will go through typical cases of using Metarank to improve the relevance of your search engine.

There are two main approaches to search reranking:

  • Zero-shot: using generic approaches not fine-tuned on your dataset and visitor behavior. This does not require any telemetry collection and is a good starting point.

  • Learn-to-Rank: adapt ranking to the dataset and visitor behavior. Can yield better quality, with the drawback of requiring proper visitor analytics.

Zero-shot re-ranking

If you're not familiar with concepts of re-ranking and Metarank, start with these intro guides to get better understanding about how things work:

  • Search re-ranking with cross-encoder LLMs: How to use a general-purpose cross-encoder, pre-trained on MS-MARCO dataset to improve your Elasticsearch search relevance.

  • TODO: Semantic search with sentence-transformers and Qdrant: setting up Metarank as an inference server for bi-encoders for semantic retrieval with vector search.


  • TODO: Setting up data collection

    • TODO: explicit and implicit relevance labels

  • TODO: Configuring ranking factors

    • TODO: Automatic config generation based on your existing data

    • TODO: Personalization and tracking visitor profile

Last updated