# Concept search before vectors were the default

> A books-and-journals search that let people explore by concepts, built in 2018 from phrases and similarity, not from a vector database.

## The problem

Researchers and students did not want another keyword box over books and journals. They wanted to move through a subject.

## The approach

I extracted noun phrases and adjective-noun phrases, exposed them as filters, and trained a word2vec model so a section could be tagged with related concepts. Custom Solr pieces covered the request path, spelling, autosuggest, and filters.

## Subjects

- [Apache Solr](https://oppority.com/topics/apache-solr.md)
- [Relevance](https://oppority.com/topics/relevance.md)
- [Hybrid search](https://oppority.com/topics/hybrid-search.md)

## The conclusion

A concept a person can read beats a similarity score they cannot. The same idea shows up now in hybrid retrieval: use the dense signal where meaning is fuzzy, and keep a precise handle where it is not.