# VectorLite
[](https://crates.io/crates/vectorlite)
[](https://docs.rs/vectorlite)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.rust-lang.org)
A high-performance, in-memory vector database optimized for AI agent workloads with HTTP API and thread-safe concurrency.
## Overview
VectorLite is designed for **single-instance, low-latency vector operations** in AI agent environments. It prioritizes **sub-millisecond search performance** over distributed scalability, making it ideal for:
- **AI Agent Sessions**: Session-scoped vector storage with fast retrieval
- **Real-time Search**: Sub-millisecond response requirements
- **Prototype Development**: Rapid iteration without infrastructure complexity
- **Single-tenant Applications**: No multi-tenancy isolation requirements
### Key Features
- **In-memory storage** for zero-latency access patterns
- **Native Rust ML models** using Candle framework with pluggable architecture. Bring your own embedding model (default to all-MiniLM-L6-v2)
- **Thread-safe concurrency** with RwLock per collection and atomic ID generation
- **HNSW indexing** for approximate nearest neighbor search with configurable accuracy
## HTTP API
RESTful interface optimized for AI agent integration:
```bash
# Health check
GET /health
# Collection management
GET /collections
POST /collections {"name": "docs", "index_type": "hnsw"}
DELETE /collections/{name}
# Vector operations
POST /collections/{name}/text {"text": "Hello world"}
POST /collections/{name}/vector {"id": 1, "values": [0.1, 0.2, ...]}
POST /collections/{name}/search/text {"query": "hello", "k": 10}
POST /collections/{name}/search/vector {"query": [0.1, 0.2, ...], "k": 10}
GET /collections/{name}/vectors/{id}
DELETE /collections/{name}/vectors/{id}
```
## Index Types
### FlatIndex
- **Complexity**: O(n) search, O(1) insert
- **Memory**: Linear with dataset size
- **Use Case**: Small datasets (< 10K vectors) or exact search requirements
### HNSWIndex
- **Complexity**: O(log n) search, O(log n) insert
- **Memory**: ~2-3x vector size due to graph structure
- **Use Case**: Large datasets with approximate search tolerance
## ML Model Integration
### Built-in Embedding Models
- **all-MiniLM-L6-v2**: Default 384-dimensional model for general-purpose text
- **Candle Framework**: Native Rust ML inference with CPU/GPU acceleration
- **Pluggable Architecture**: Easy integration of custom embedding models
- **Memory Efficient**: Models loaded once and shared across requests
### Similarity Metrics
- **Cosine**: Default for normalized embeddings, scale-invariant
- **Euclidean**: Geometric distance, sensitive to vector magnitude
- **Manhattan**: L1 norm, robust to outliers
- **Dot Product**: Raw similarity, requires consistent vector scaling
## Configuration Profiles
```bash
# Balanced (default)
cargo build
# Memory-constrained environments
cargo build --features memory-optimized
# High-precision search
cargo build --features high-accuracy
```
## Getting Started
```rust
use vectorlite::{VectorLiteClient, EmbeddingGenerator, IndexType, SimilarityMetric};
// Create client with embedding function
let client = VectorLiteClient::new(Box::new(EmbeddingGenerator::new()?));
// Create collection
client.create_collection("documents", IndexType::HNSW)?;
// Add text (auto-generates embedding and ID)
let id = client.add_text_to_collection("documents", "Hello world")?;
// Search
let results = client.search_text_in_collection(
"documents",
"hello",
5,
SimilarityMetric::Cosine
)?;
```
## HTTP Server Example
```rust
use vectorlite::{VectorLiteClient, EmbeddingGenerator, start_server};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let client = VectorLiteClient::new(Box::new(EmbeddingGenerator::new()?));
start_server(client, "127.0.0.1", 3000).await?;
Ok(())
}
```
## License
Apache 2.0 License - see [LICENSE](LICENSE) for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.