Adding `vectorbase` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add vectorbase

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

vectorbase = "0.0.1"

The vectorbase library will be automatically available globally.

Back to the crate overview.

Readme

VectorBase

A vector-based search engine, also supports ordinary text search

Feature：

Provide a low-level search library
Support vector similarity search
Support keyword search
Simd optimizes similarity calculation
Support index merging
Support memory index
Index persisted to disk and loaded

Getting started

simple example

use galois::Shape;
use galois::Tensor;
use searchlite::ann::AnnType;
use searchlite::query::Term;
use searchlite::schema::*;
use searchlite::Collection;
use searchlite::IndexConfigBuilder;
use std::path::PathBuf;
fn main() {
    let mut schema = Schema::with_vector(VectorEntry::new(
        "vector",
        AnnType::HNSW,
        TensorEntry::new(1, [4], VectorType::F32),
    ));
    schema.add_field(FieldEntry::str("color"));
    let field_id_color = schema.get_field("color").unwrap();
    let config = IndexConfigBuilder::default()
        .data_path(PathBuf::from("./data"))
        .build();
    let mut collect = Collection::new(schema, config).unwrap();
    {
        let mut d5 = Document::new();
        d5.add_text(field_id_color.clone(), "red");
        let v5 = Vector::from_array([0.0f32, 0.0, 1.0, 1.0], d5);
        collect.add(v5).unwrap();

        let mut d6 = Document::new();
        d6.add_text(field_id_color.clone(), "blue");
        let v6 = Vector::from_array([0.0f32, 1.0, 1.0, 0.0], d6);
        collect.add(v6).unwrap();

        let mut d7 = Document::new();
        d7.add_text(field_id_color.clone(), "red");
        let v7 = Vector::from_array([1.0f32, 0.0, 0.0, 1.0], d7);
        collect.add(v7).unwrap();

        let mut d8 = Document::new();
        d8.add_text(field_id_color.clone(), "yellow");
        let d8 = Vector::from_array([1.0f32, 1.0, 0.0, 0.0], d8);
        collect.add(d8).unwrap();
    }

    let reader = collect.reader();
    let p = reader
        .query(
            &Tensor::from_vec(vec![0.0f32, 0.0, 1.0, 1.0], 1, Shape::from_array([4])),
            2,
        )
        .unwrap();

    for n in p.iter() {
        let doc = reader.vector(n.doc_id()).unwrap();
        println!(
            "id:{},vector:{:?},doc:{:?}",
            n.doc_id(),
            doc.vector().to_vec::<f32>(),
            doc.doc(),
        );
    }

    let p = reader
        .search(Term::from_field_text(field_id_color, "red"))
        .unwrap();

    for n in p.iter() {
        let doc = reader.vector(n.doc_id()).unwrap();
        println!("id:{},doc:{:?}", n.doc_id(), doc.doc(),);
    }
}

Adding vectorbase library as a dependency

Adding `vectorbase` library as a dependency