Core Concepts #

First, let's walk through a few of the core concepts in Quepid.

Book: A book refers to a set of queries and documents and their ratings independent of the search engine. A single book can provide the source information for multiple cases.

Case: A case refers to all of the queries and relevance tuning settings for a single search engine. If you want to work with multiple instances of Solr or Elasticsearch (or any other search engine or API), you must create a separate case for each one.

Query: Within a case, queries are the keywords or other search criteria and their corresponding set of results that will be rated to determine the overall score of a case.

Rating: Ratings are the numerical values given to a result that indicates how relevant a particular search result is for the query. How each rating is interpreted depends on the scorer used for the query (or case), but usually the higher the number the more relevant the result is.

Result: Within a query are the individual results, which are rated to determine the cumulative score of a query. Sometimes results are also referred to as documents (or docs).

Scorer: A scorer refers to the scale used to rate query results. Quepid ships with several classical relevance scorers such as AP, RR, CG, DCG, and NDCG, as well as the ability to create custom scorers.

Search Endpoint: A search endpoint refers to the search engine, API, or even static dataset that you are interacting with. A search endpoint has all of the information required for Quepid to interact with it.

Snapshot: A snapshot is a capture of all your queries and ratings at a point in time. It is important to take regular snapshots of your case to use as benchmarks and ensure that your search relevancy is improving.

Team: A team refers to a group of individual users who can view and share cases and custom scorers.

Try: A try is a saved iteration of a case's settings. Quepid is a developer tool and we expect developers to constantly tweak the settings, and sometimes you would want to go back to a previous iteration that had better results, so we've made that an integral part of Quepid.