Skip to main content
Text search in ParadeDB, like Elasticsearch and most search engines, is centered around the concept of token matching. Token matching consists of two steps. First, at indexing time, text is processed by a tokenizer, which breaks input into discrete units called tokens or terms. For example, the default tokenizer splits the text Sleek running shoes into the tokens sleek, running, and shoes. Second, at query time, the query engine looks for token matches based on the specified query and query type. Some common query types include:
  • Match: Matches documents containing any or all query tokens
  • Phrase: Matches documents where all tokens appear in the same order as the query
  • Term: Matches documents containing an exact token
  • …and many more advanced query types

​
Not Substring Matching

While ParadeDB supports substring matching via regex queries, it’s important to note that token matching is not the same as substring matching. Token matching is a much more versatile and powerful technique. It enables relevance scoring, language-specific analysis, typo tolerance, and more expressive query types — capabilities that go far beyond simply looking for a sequence of characters. Text search is different from similarity search, also known as vector search. Whereas text search matches based on token matches, similarity search matches based on semantic meaning. Today, most ParadeDB users install pgvector alongside ParadeDB for vector search and hybrid search. That remains our recommended setup when you need embeddings in Postgres right now. We are also actively working on a native vector search experience inside ParadeDB indexes that is intended to improve on the current pgvector workflow, especially for filtered and hybrid search. You can follow that work in our roadmap or reach out if it is important for your use case.