Struct VectorDB 

pub struct VectorDB { /* private fields */ }

High-performance vector database with HNSW indexing

Implementations

impl VectorDB

pub fn instance_of<V: NapiRaw>(env: Env, value: V) -> Result<bool>

impl VectorDB

pub fn into_reference(val: VectorDB, env: Env) -> Result<Reference<VectorDB>>

pub fn into_instance(self, env: Env) -> Result<ClassInstance<VectorDB>>

impl VectorDB

pub fn new(options: JsDbOptions) -> Result<Self>

Create a new vector database with the given options

Example

const db = new VectorDB({
  dimensions: 384,
  distanceMetric: 'Cosine',
  storagePath: './vectors.db',
  hnswConfig: {
    m: 32,
    efConstruction: 200,
    efSearch: 100
  }
});

pub fn with_dimensions(dimensions: u32) -> Result<Self>

Create a vector database with default options

Example

const db = VectorDB.withDimensions(384);

pub async fn insert(&self, entry: JsVectorEntry) -> Result<String>

Insert a vector entry into the database

Returns the ID of the inserted vector (auto-generated if not provided)

Example

const id = await db.insert({
  vector: new Float32Array([1.0, 2.0, 3.0]),
  metadata: { text: 'example' }
});

pub async fn insert_batch( &self, entries: Vec<JsVectorEntry>, ) -> Result<Vec<String>>

Insert multiple vectors in a batch

Returns an array of IDs for the inserted vectors

Example

const ids = await db.insertBatch([
  { vector: new Float32Array([1, 2, 3]) },
  { vector: new Float32Array([4, 5, 6]) }
]);

pub async fn search(&self, query: JsSearchQuery) -> Result<Vec<JsSearchResult>>

Search for similar vectors

Returns an array of search results sorted by similarity

Example

const results = await db.search({
  vector: new Float32Array([1, 2, 3]),
  k: 10,
  filter: { category: 'example' }
});

pub async fn delete(&self, id: String) -> Result<bool>

Delete a vector by ID

Returns true if the vector was deleted, false if not found

Example

const deleted = await db.delete('vector-id');

pub async fn get(&self, id: String) -> Result<Option<JsVectorEntry>>

Get a vector by ID

Returns the vector entry if found, null otherwise

Example

const entry = await db.get('vector-id');
if (entry) {
  console.log('Found:', entry.metadata);
}

pub async fn len(&self) -> Result<u32>

Get the number of vectors in the database

Example

const count = await db.len();
console.log(`Database contains ${count} vectors`);

pub async fn is_empty(&self) -> Result<bool>

Check if the database is empty

Example

if (await db.isEmpty()) {
  console.log('Database is empty');
}

Trait Implementations

impl FromNapiMutRef for VectorDB

unsafe fn from_napi_mut_ref( env: napi_env, napi_val: napi_value, ) -> Result<&'static mut Self>

Safety

impl FromNapiRef for VectorDB

unsafe fn from_napi_ref( env: napi_env, napi_val: napi_value, ) -> Result<&'static Self>

Safety

impl FromNapiValue for &VectorDB

unsafe fn from_napi_value(env: napi_env, napi_val: napi_value) -> Result<Self>

Safety

fn from_unknown(value: JsUnknown) -> Result<Self, Error>

impl FromNapiValue for &mut VectorDB

unsafe fn from_napi_value(env: napi_env, napi_val: napi_value) -> Result<Self>

Safety

fn from_unknown(value: JsUnknown) -> Result<Self, Error>

impl ObjectFinalize for VectorDB

fn finalize(self, env: Env) -> Result<(), Error>

impl ToNapiValue for VectorDB

unsafe fn to_napi_value(env: napi_env, val: VectorDB) -> Result<napi_value>

Safety

impl TypeName for &VectorDB

fn type_name() -> &'static str

fn value_type() -> ValueType

impl TypeName for &mut VectorDB

fn type_name() -> &'static str

fn value_type() -> ValueType

impl TypeName for VectorDB

fn type_name() -> &'static str

fn value_type() -> ValueType

impl ValidateNapiValue for &VectorDB

unsafe fn validate(env: napi_env, napi_val: napi_value) -> Result<napi_value>

Safety

impl ValidateNapiValue for &mut VectorDB

unsafe fn validate(env: napi_env, napi_val: napi_value) -> Result<napi_value>

Safety