hyperspace-sdk (Rust)

Official Rust client for HyperspaceDB gRPC data plane.

This crate provides:

• authenticated gRPC client
• collection management
• insert/search APIs
• high-throughput search_batch
• f32 helper methods for Euclidean workloads (insert_f32, search_f32, search_batch_f32)

Installation

[dependencies]
hyperspace-sdk = "3.0.0-alpha.2"
tokio = { version = "1", features = ["macros", "rt-multi-thread"] }

Quick Start

use hyperspace_sdk::Client;
use std::collections::HashMap;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut client = Client::connect(
        "http://localhost:50051".to_string(),
        Some("I_LOVE_HYPERSPACEDB".to_string()),
        None,
    ).await?;

    let collection = "docs_rust".to_string();
    let _ = client.delete_collection(collection.clone()).await;
    client.create_collection(collection.clone(), 3, "cosine".to_string()).await?;

    client.insert(
        1,
        vec![0.1, 0.2, 0.3],
        HashMap::new(),
        Some(collection.clone()),
    ).await?;

    let results = client.search(
        vec![0.1, 0.2, 0.3],
        10,
        Some(collection.clone()),
    ).await?;

    println!("results: {}", results.len());
    Ok(())
}

Batch Search

Use search_batch to reduce RPC overhead:

let responses = client.search_batch(
    vec![
        vec![0.1, 0.2, 0.3],
        vec![0.3, 0.1, 0.4],
    ],
    10,
    Some("docs_rust".to_string()),
).await?;

Each entry in responses corresponds to one query vector.

f32 Helpers

When your app keeps Euclidean vectors in f32, use conversion helpers:

• insert_f32
• search_f32
• search_batch_f32

The crate converts to protocol f64 once per call.

API Surface (Core)

• Client::connect
• create_collection, delete_collection, list_collections
• insert, insert_f32
• insert_text (server-side vectorization and storage)
• vectorize (convert text to vector on server)
• search, search_f32, search_advanced
• search_text (search with text input, vectorized on server)
• search_batch, search_batch_f32, search_wasserstein, search_multi_collection
• delete
• configure
• get_collection_stats, get_digest
• trigger_vacuum, trigger_reconsolidation, rebuild_index, rebuild_index_with_filter
• get_neighbors_with_weights (graph edges with distances)
• subscribe_to_events (CDC stream)

Rebuild with Pruning

client
    .rebuild_index_with_filter(
        "docs_rust".to_string(),
        "energy".to_string(),
        "lt".to_string(),
        0.1,
    )
    .await?;

Hyperbolic Math Utilities

use hyperspace_sdk::math::{
    mobius_add, exp_map, log_map, parallel_transport, riemannian_gradient, frechet_mean
};

Graph Diagnostics (Gromov Delta)

Analyze your dataset structure directly on the client to select the correct metric:

use hyperspace_sdk::gromov::analyze_delta_hyperbolicity;

// Returns the delta value and recommended metric (lorentz, poincare, cosine, l2)
let (delta, metric) = analyze_delta_hyperbolicity(&vectors, 100);

AI Sleep Mode / Memory Reconsolidation

Trigger the database to run Riemannian SGD via Flow Matching natively:

client.trigger_reconsolidation("my_collection".to_string(), target_vector, 0.01).await?;

Cognitive Math SDK (Spatial AI Engine)

Provides advanced tools for Agentic AI, running entirely on the client side:

use hyperspace_sdk::math::{
    local_entropy, lyapunov_convergence, koopman_extrapolate, context_resonance
};

// 1. Detect Hallucinations (Entropy approaches 1.0)
let entropy = local_entropy(&thought_vector, &neighbors, curvature)?;

// 2. Proof of Convergence (Negative derivative = convergence)
let stability = lyapunov_convergence(&chain_of_thought, curvature)?;

// 3. Extrapolate next thought (Koopman linearization)
let next_thought = koopman_extrapolate(&past, &current, 1.0, curvature)?;

// 4. Phase-Locked Loop for topic tracking
let synced_thought = context_resonance(&thought, &global_context, 0.5, curvature)?;

Embedding Pipeline (Optional)

HyperspaceDB supports per-geometry embeddings — each of the 4 distance types (l2, cosine, poincare, lorentz) can have its own embedding backend configured independently.

Available Backends

Usage

# Cargo.toml
[dependencies]
# API providers only
hyperspace-sdk = { version = "3.0.0", features = ["embedders"] }

# Local ONNX files (no network required at inference time)
hyperspace-sdk = { version = "3.0.0", features = ["local-onnx"] }

# Download from HuggingFace Hub (includes local-onnx)
hyperspace-sdk = { version = "3.0.0", features = ["huggingface"] }

EmbedGeometry

Every embedder requires specifying the target geometry, which controls post-processing:

use hyperspace_sdk::embedder::EmbedGeometry;

// Cosine / dot-product: vectors are unit-normalized
let geom = EmbedGeometry::Cosine;

// L2 / Euclidean: vectors are unit-normalized
let geom = EmbedGeometry::L2;

// Poincaré ball: vectors are clamped inside the unit ball (||x|| < 1)
let geom = EmbedGeometry::Poincare;

// Lorentz hyperboloid: no post-processing (model head handles constraint)
let geom = EmbedGeometry::Lorentz;

// Parse from collection metric string
let geom = EmbedGeometry::from_str("poincare");

Local ONNX Embedder

use hyperspace_sdk::embedder::{LocalOnnxEmbedder, EmbedGeometry, Embedder};

let embedder = LocalOnnxEmbedder::new(
    "./models/bge-small-en-v1.5.onnx",
    "./models/bge-small-en-v1.5-tokenizer.json",
    EmbedGeometry::Cosine,
)?;

let vector = embedder.encode("Hello, hyperbolic world!").await?;

HuggingFace Hub Embedder

Downloads model.onnx and tokenizer.json automatically from the Hub on first use. Files are cached locally (~/.cache/huggingface/hub).

use hyperspace_sdk::embedder::{HuggingFaceEmbedder, EmbedGeometry, Embedder};

// Public model — no token needed
let embedder = HuggingFaceEmbedder::new(
    "BAAI/bge-small-en-v1.5",
    None, // HF token (None for public models)
    EmbedGeometry::Cosine,
)?;

// Private or gated model — provide HF_TOKEN
let embedder = HuggingFaceEmbedder::new(
    "your-org/cde-spatial-lorentz-128d",
    std::env::var("HF_TOKEN").ok(),
    EmbedGeometry::Lorentz,
)?;

let vector = embedder.encode("Retrieve context").await?;

OpenAI / Remote API Embedder

use hyperspace_sdk::embedder::{OpenAIEmbedder, Embedder};

let embedder = OpenAIEmbedder::new(
    std::env::var("OPENAI_API_KEY").unwrap(),
    "text-embedding-3-small".to_string(),
);

let vector = embedder.encode("my document text").await?;

Server-Side Embedding (InsertText / SearchText)

The server can embed text automatically. Configure in .env (see server docs):

HYPERSPACE_EMBED=true

# Cosine geometry via HuggingFace
HS_EMBED_COSINE_PROVIDER=huggingface
HS_EMBED_COSINE_HF_MODEL_ID=BAAI/bge-small-en-v1.5
HS_EMBED_COSINE_DIM=384
HF_TOKEN=hf_your_token_here  # Optional: for gated models

# Lorentz geometry via local ONNX
HS_EMBED_LORENTZ_PROVIDER=local
HS_EMBED_LORENTZ_MODEL_PATH=./models/lorentz_128d.onnx
HS_EMBED_LORENTZ_TOKENIZER_PATH=./models/lorentz_128d_tokenizer.json
HS_EMBED_LORENTZ_DIM=129

Production Notes

• Reuse long-lived clients instead of reconnecting per request.
• Prefer search_batch on concurrency-heavy paths.
• Keep collection metric/dimension consistent with your vector source.
• For huggingface provider, models are cached; first startup incurs download time.
• For lorentz geometry, dimension is typically spatial_dim + 1 (the time component).