vector_xlite 1.4.0

The embedded mode allows you to use VectorXLite as a direct dependency in your Rust application, providing high-performance vector search with zero network overhead.

Overview

The embedded library combines SQLite's reliability with HNSW-based vector search, enabling:

• Sub-millisecond similarity search on millions of vectors
• SQL-powered payload filtering for hybrid queries
• ACID transactions with atomic operations
• In-memory or file-backed storage options

Installation

Add to your Cargo.toml:

[dependencies]
vector_xlite = { path = "../embedded/core" }  # Or from crates.io: "1.2"
r2d2 = "0.8"
r2d2_sqlite = "0.31"
rusqlite = { version = "0.37", features = ["load_extension"] }

Quick Start

use vector_xlite::{VectorXLite, customizer::SqliteConnectionCustomizer, types::*};
use r2d2::Pool;
use r2d2_sqlite::SqliteConnectionManager;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // 1. Create connection pool
    let manager = SqliteConnectionManager::memory();
    let pool = Pool::builder()
        .max_size(10)
        .connection_customizer(SqliteConnectionCustomizer::new())
        .build(manager)?;

    let db = VectorXLite::new(pool)?;

    // 2. Create a collection
    let config = CollectionConfigBuilder::default()
        .collection_name("products")
        .vector_dimension(384)
        .distance(DistanceFunction::Cosine)
        .payload_table_schema(
            "CREATE TABLE products (
                rowid INTEGER PRIMARY KEY,
                name TEXT NOT NULL,
                category TEXT,
                price REAL
            )"
        )
        .build()?;

    db.create_collection(config)?;

    // 2.5. Check if collection exists
    if db.collection_exists("products")? {
        println!("Collection 'products' already exists!");
    }

    // 3. Insert vectors
    let embedding = vec![0.1; 384];
    let point = InsertPoint::builder()
        .collection_name("products")
        .id(1)
        .vector(embedding)
        .payload_insert_query(
            "INSERT INTO products(rowid, name, category, price)
             VALUES (?1, 'Wireless Headphones', 'Electronics', 99.99)"
        )
        .build()?;

    db.insert(point)?;

    // 4. Search with filtering
    let query = vec![0.15; 384];
    let search = SearchPoint::builder()
        .collection_name("products")
        .vector(query)
        .top_k(10)
        .payload_search_query(
            "SELECT rowid, name, category, price
             FROM products
             WHERE category = 'Electronics' AND price < 150"
        )
        .build()?;

    let results = db.search(search)?;

    for result in results {
        println!("Found: {}", result["name"]);
    }

    Ok(())
}

Core Concepts

Collections

A collection combines a vector index with a payload table.

Creating Collections

let config = CollectionConfigBuilder::default()
    .collection_name("documents")
    .vector_dimension(768)           // Dimension of your embeddings
    .distance(DistanceFunction::Cosine)  // Similarity metric
    .max_elements(1_000_000)         // Capacity
    .payload_table_schema(           // SQL schema for metadata
        "CREATE TABLE documents (
            rowid INTEGER PRIMARY KEY,
            title TEXT,
            content TEXT,
            tags JSON,
            created_at TIMESTAMP
        )"
    )
    .index_file_path("/data/docs.idx")  // Optional: persist HNSW index
    .build()?;

Checking Collection Existence

Before creating or using a collection, you can check if it exists:

// Check if a collection exists
if db.collection_exists("documents")? {
    println!("Collection exists!");
} else {
    println!("Collection does not exist, creating...");
    db.create_collection(config)?;
}

// Prevent duplicate collection creation
let collection_name = "products";
if !db.collection_exists(collection_name)? {
    let config = CollectionConfigBuilder::default()
        .collection_name(collection_name)
        .vector_dimension(384)
        .build()?;

    db.create_collection(config)?;
    println!("Collection created successfully");
} else {
    println!("Collection already exists, skipping creation");
}

Important Notes:

• Collection names are case-sensitive
• Empty collection names return an error
• This check is atomic and thread-safe
• Useful for idempotent initialization code

Distance Functions

Storage Modes

In-Memory (Development/Testing)

let manager = SqliteConnectionManager::memory();

File-Backed (Production)

let manager = SqliteConnectionManager::file("vectors.db");
let config = CollectionConfigBuilder::default()
    .collection_name("prod")
    .index_file_path("/data/prod.idx")  // Persistent HNSW index
    .build()?;

Advanced Usage

JSON Payloads

let config = CollectionConfigBuilder::default()
    .collection_name("products")
    .payload_table_schema(
        "CREATE TABLE products (
            rowid INTEGER PRIMARY KEY,
            metadata JSON
        )"
    )
    .build()?;

// Insert
let point = InsertPoint::builder()
    .collection_name("products")
    .id(1)
    .vector(embedding)
    .payload_insert_query(
        r#"INSERT INTO products(rowid, metadata)
           VALUES (?1, '{"tags": ["sale"], "stock": 100}')"#
    )
    .build()?;

// Query JSON fields
let search = SearchPoint::builder()
    .collection_name("products")
    .vector(query)
    .payload_search_query(
        "SELECT * FROM products
         WHERE json_extract(metadata, '$.stock') > 0"
    )
    .build()?;

Atomic Operations

// Atomic batch insert using transactions
let manager = SqliteConnectionManager::file("vectors.db");
let pool = Pool::builder()
    .connection_customizer(SqliteConnectionCustomizer::new())
    .build(manager)?;

let db = VectorXLite::new(pool.clone())?;

// All inserts are atomic - either all succeed or all fail
for (id, vector) in vectors.iter().enumerate() {
    db.insert(InsertPoint::builder()
        .collection_name("batch")
        .id(id as u64)
        .vector(vector.clone())
        .payload_insert_query(&format!(
            "INSERT INTO batch(rowid, data) VALUES (?1, 'Item {}')", id
        ))
        .build()?
    )?;
}

Connection Pooling

use vector_xlite::customizer::SqliteConnectionCustomizer;

// Default timeout: 15 seconds
let customizer = SqliteConnectionCustomizer::new();

// Custom timeout: 30 seconds
let customizer = SqliteConnectionCustomizer::with_busy_timeout(30000);

let pool = Pool::builder()
    .max_size(15)  // Max concurrent connections
    .connection_customizer(customizer)
    .build(manager)?;

Examples

Run the included examples:

# Run all examples
cargo run -p embedded-examples --release

# Run specific example
cd embedded/examples/rust
cargo run --release

Performance Tips

1. Use file-backed storage for datasets larger than RAM
2. Tune max_elements to your expected dataset size
3. Index payload columns that you frequently filter on:

   CREATE INDEX idx_category ON products(category);
   

4. Batch operations for better throughput
5. Persistent HNSW index with index_file_path for faster restarts

API Reference

Main Types

• VectorXLite - Main database interface
  • create_collection(config) - Create a new collection
  • collection_exists(name) - Check if a collection exists
  • insert(point) - Insert a vector with payload
  • delete(point) - Delete a vector from a collection
  • delete_collection(collection) - Delete an entire collection
  • search(query) - Search for similar vectors
• CollectionConfigBuilder - Configure collections
• InsertPoint - Insert operations
• SearchPoint - Search operations
• DistanceFunction - Similarity metrics (Cosine, L2, IP)

Full documentation

See the main README for comprehensive API documentation.

Architecture

┌─────────────────────────────────────────┐
│          Your Application               │
├─────────────────────────────────────────┤
│         VectorXLite API                 │
├──────────────────┬──────────────────────┤
│   HNSW Index     │      SQLite          │
│ (Vector Search)  │  (Payload Storage)   │
├──────────────────┴──────────────────────┤
│         Connection Pool (r2d2)          │
└─────────────────────────────────────────┘

Use Cases

• RAG Applications - Embed documents for LLM context retrieval
• Semantic Search - Find similar content by meaning
• Recommendation Systems - Similar item suggestions
• Image Search - Visual similarity using CNN embeddings
• Anomaly Detection - Outlier detection in high-dimensional data
• Deduplication - Find near-duplicate content

Testing

Run the comprehensive test suite:

# Run all tests
cargo test -p vector_xlite_tests --release

# Run specific test file
cargo test -p vector_xlite_tests --test snapshot_tests --release

Next Steps

• Standalone Mode: Need multi-language clients? See ../standalone/
• Distributed Mode: Need high availability? See ../distributed/
• Documentation: Full API docs at docs.rs/vector_xlite

License

MIT OR Apache-2.0