Struct Client 

pub struct Client { /* private fields */ }

The top-level KeraDB client.

Example

use keradb_sdk::{connect, Client};
use serde_json::json;

let mut client = connect("mydb.ndb").unwrap();
let coll = client.database().collection("users");
let res = coll.insert_one(json!({"name": "Alice"})).unwrap();
println!("inserted: {}", res.inserted_id);
client.close();

Or use the Drop-based RAII via the with / scope pattern – the database is flushed and closed when Client is dropped.

Implementations

impl Client

pub fn database(&self) -> Database

Return the Database associated with this client.

The optional name argument is accepted for MongoDB API compatibility but is ignored (KeraDB is single-database per file).

pub fn sync(&self) -> Result<()>

Sync all pending writes to disk.

pub fn close(&mut self)

Close the database. Subsequent calls are safe (no-ops).

pub fn create_vector_collection( &self, name: &str, config: &VectorConfig, ) -> Result<()>

Create a new vector collection with the given configuration.

pub fn list_vector_collections(&self) -> Result<Vec<VectorCollectionInfo>>

Return a list of all vector collections.

pub fn drop_vector_collection(&self, name: &str) -> Result<bool>

Drop a vector collection, returning true on success.

pub fn insert_vector( &self, collection: &str, embedding: &[f32], metadata: Option<&Value>, ) -> Result<u64>

Insert a vector embedding with optional JSON metadata.

Returns the numeric ID assigned by KeraDB.

pub fn insert_text( &self, collection: &str, text: &str, metadata: Option<&Value>, ) -> Result<u64>

Insert text (requires a lazy-embedding collection) and return its ID.

pub fn vector_search( &self, collection: &str, query: &[f32], k: usize, ) -> Result<Vec<VectorSearchResult>>

Perform a k-nearest-neighbour vector search.

pub fn vector_search_text( &self, collection: &str, query: &str, k: usize, ) -> Result<Vec<VectorSearchResult>>

Perform a text-based similarity search (requires lazy-embedding collection).

pub fn vector_search_filtered( &self, collection: &str, query: &[f32], k: usize, filter: &MetadataFilter, ) -> Result<Vec<VectorSearchResult>>

Perform a filtered k-NN search.

pub fn get_vector( &self, collection: &str, id: u64, ) -> Result<Option<VectorDocument>>

Retrieve a single vector document by its numeric ID.

pub fn delete_vector(&self, collection: &str, id: u64) -> Result<bool>

Delete a vector document by its numeric ID. Returns true if deleted.

pub fn vector_stats(&self, collection: &str) -> Result<VectorCollectionStats>

Return statistics for a vector collection.

pub fn has_vector_support(&self) -> bool

Returns true when the loaded native library includes vector support.

Trait Implementations

impl Drop for Client

fn drop(&mut self)

Executes the destructor for this type.

impl Send for Client