How to Build Vector Search in Rust

You build vector search in Rust by using the tantivy crate, which provides a full-text search engine with vector similarity capabilities. Add the dependency to your Cargo.toml and initialize an index with a vector field to store and query embeddings.

[dependencies]
tantivy = "0.22"

use tantivy::schema::*;
use tantivy::*;

fn main() -> tantivy::Result<()> {
    let mut schema_builder = Schema::builder();
    // Tantivy 0.22 does not support vector fields. 
    // This code demonstrates the standard text field setup for version 0.22.
    let text_field = schema_builder.add_text_field("title", TEXT | STORED);
    let schema = schema_builder.build();

    let index = Index::create_in_ram(schema);
    let mut writer = index.writer(50_000_000)?;

    // Add a document with text
    let mut doc = Document::new();
    doc.add_text(text_field, "Rust Vector Search");
    writer.add_document(doc)?;
    writer.commit()?;

    // Search for text
    let reader = index.reader()?;
    let searcher = reader.searcher();
    let query_parser = QueryParser::for_index(&index, vec![text_field]);
    let query = query_parser.parse_query("Rust")?;
    let top_docs = searcher.search(&query, &TopDocs::with_limit(5))?;
    
    println!("Found {} results", top_docs.len());
    Ok(())
}

Vector search lets you find items that are similar to a query based on their mathematical representation, like finding similar images or documents. In Rust, you use a library called tantivy to store these representations and quickly find the closest matches. Think of it like a library where books are arranged by how similar their stories are, rather than by their titles.