zvec-rust

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Safe, idiomatic Rust bindings for the zvec vector database.

Features

• RAII Resource Management — All C resources are automatically freed via Drop
• Builder Pattern — Fluent APIs for schema, query, and configuration
• Type Safety — Rust enums for all C constants with compile-time checks
• Comprehensive Error Handling — All FFI calls return Result<T> with detailed error codes
• Zero-Copy Where Possible — Minimizes data copying across the FFI boundary
• Prebuilt Libraries — Automatically downloads prebuilt libzvec_c_api from GitHub Releases; advanced users can override with ZVEC_LIB_DIR
• FTS with jieba Out of the Box — Prebuilt packages ship the cppjieba dictionary (data/jieba_dict); initialize auto-discovers it and registers the default jieba dict dir for the jieba FTS tokenizer

Supported Platforms

Linux gnu prebuilts target glibc 2.28 (manylinux_2_28, same as upstream zvec), so they work on glibc distros as old as CentOS 8 / Ubuntu 20.04. musl prebuilts work on musl distros such as Alpine (musl 1.2+).

The dynamic library name varies by platform: libzvec_c_api.dylib (macOS), libzvec_c_api.so (Linux), zvec_c_api.dll (Windows).

Architecture

zvec-rust/
├── zvec-sys/    # Low-level FFI bindings to libzvec_c_api
├── zvec/        # Safe, high-level Rust wrapper
└── fuzz/        # Fuzz testing targets

• zvec-rust-sys — Raw extern "C" declarations, opaque pointer types, and constants
• zvec-rust — Safe wrappers with RAII, builders, iterators, and idiomatic Rust APIs

Prerequisites

The Rust SDK depends on the zvec C library (libzvec_c_api). Choose one of the following ways to provide it:

Option 1: Bundled Prebuilt Library (Zero Setup)

Add zvec-rust to your Cargo.toml. The default bundled feature automatically downloads the prebuilt libzvec_c_api for your platform from GitHub Releases:

[dependencies]
zvec-rust = "0.7.2"

cargo run / cargo test work out of the box because Cargo passes the resolved library directory to the linker for you. A directly executed or deployed binary, however, needs a runtime search path (rpath) pointing at the shared library — Cargo does not add one automatically. Use the zvec-rust-build build-script helper from your binary crate to emit the rpath and stage the library beside the executable:

[dependencies]
zvec-rust = "0.7.2"

[build-dependencies]
zvec-rust-build = "0.7.2"

// build.rs
fn main() {
    zvec_rust_build::configure();
}

Without the helper you must instead set DYLD_LIBRARY_PATH (macOS) / LD_LIBRARY_PATH (Linux) at runtime.

Option 2: Custom Build

If you want to build the zvec C library yourself (e.g., for a custom configuration or unsupported platform), set the ZVEC_LIB_DIR environment variable:

# Build zvec from source
git clone https://github.com/alibaba/zvec.git && cd zvec
mkdir -p build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_C_BINDINGS=ON
make -j$(nproc)

# Point zvec-rust to your custom build
export ZVEC_LIB_DIR=/path/to/zvec/build/lib

Or use the built-in Makefile for local development:

make setup        # Install dev tools + init git submodule
make zvec-build   # Build the zvec C library from submodule
make test-all     # Run all tests

Library Resolution Order

The build script resolves the C library in this order:

1. ZVEC_LIB_DIR environment variable (highest priority)
2. Sibling checkout: ../zvec/build/lib
3. Git submodule: vendor/zvec/build/lib
4. Vendor directory: vendor/lib/
5. Prebuilt download: from GitHub Releases (automatic)
6. Auto-build: clone and build from source (requires git, cmake, C++17 compiler)

Set ZVEC_AUTO_BUILD=0 to disable steps 5 and 6.

Quick Start

use zvec_rust::*;

fn main() -> zvec_rust::Result<()> {
    // 1. Initialize the engine
    initialize(None)?;

    // 2. Define schema
    let schema = CollectionSchema::builder("my_collection")
        // Note 1: use `?` to unwrap the Result returned by FieldSchema::new
        .add_field(FieldSchema::new("id", DataType::String, false, 0)?)
        // Note 2: use `?` to unwrap the Result returned by IndexParams::hnsw
        .add_vector_field(
            "embedding",
            DataType::VectorFp32,
            128,
            IndexParams::hnsw(MetricType::Cosine, 16, 200)?
        )
        .build()?;

    println!("Schema built successfully.");

    // 3. Create and open the collection (the directory will be created if it does not exist)
    // "./data" is the local storage path
    let collection = Collection::create_and_open("./data", &schema, None)?;

    println!(" Collection opened.");

    // 4. Insert data
    let mut doc = Doc::new()?;
    doc.set_pk("doc1");
    doc.add_string("id", "doc1")?;
    // Build a 128-dim vector filled with 0.1
    let vec_data = vec![0.1_f32; 128];
    doc.add_vector_f32("embedding", &vec_data)?;

    // `insert` accepts a slice of &[&Doc]
    collection.insert(&[&doc])?;

    println!("Document inserted.");

    // 5. Vector similarity search
    // Query vector: filled with 0.2
    let query_vec = vec![0.2_f32; 128];
    let query = SearchQuery::new("embedding", &query_vec, 10)?;

    let results = collection.query(&query)?;

    println!("Search Results:");
    for result in &results {
        let pk = result.get_pk().unwrap_or("unknown");

        let score = result.get_score();
        println!("   PK: {}, Score: {:.4}", pk, score);
    }

    // 6. Shutdown the engine
    shutdown()?;

    println!("Test Finished!");
    Ok(())
}

Examples

Run any example with cargo run --example <name>:

# Using cargo directly (requires ZVEC_LIB_DIR / DYLD_LIBRARY_PATH)
export DYLD_LIBRARY_PATH=vendor/zvec/build/lib

cargo run --example basic
cargo run --example vector_search

API Overview

Initialization

Use ConfigBuilder to customize memory limits, thread counts, and logging:

let config = ConfigBuilder::new()
    .memory_limit(1024 * 1024 * 1024)
    .num_threads(4)
    .enable_console_log(true)
    .build();
initialize(Some(&config))?;

jieba FTS tokenizer dictionary

The jieba FTS tokenizer needs cppjieba's dictionary files (jieba.dict.utf8 and hmm_model.utf8). The prebuilt libraries ship them under data/jieba_dict next to libzvec_c_api, and initialize automatically discovers that directory and registers it via zvec_set_default_jieba_dict_dir — Chinese full-text search works with no extra configuration:

initialize(None)?; // jieba dict auto-discovered and registered

let schema = CollectionSchema::builder("articles")
    .add_field(FieldSchema::new("id", DataType::String, false, 0)?)
    .add_indexed_field("content", DataType::String,
        IndexParams::fts(Some("jieba"), None, None)?)
    .build()?;

To override the location (e.g. a custom dictionary), use one of:

• ConfigBuilder::new().jieba_dict_dir("/path/to/jieba_dict") — per initialize call (highest priority)
• set_default_jieba_dict_dir("/path/to/jieba_dict") — process-wide default
• ZVEC_JIEBA_DICT_DIR environment variable — read by the zvec library at query time
• per-field extra_params JSON with "jieba_dict_dir" — per index

Schema Definition

let schema = CollectionSchema::builder("name")
    .add_field(FieldSchema::new("field", DataType::String, false, 0)?)
    .add_vector_field("vec", DataType::VectorFp32, 128,
        IndexParams::hnsw(MetricType::Cosine, 16, 200)?)
    .build()?;

Collection Operations

Document Operations

let mut doc = Doc::new()?;
doc.set_pk("my_pk");
doc.add_string("name", "value")?;
doc.add_i64("count", 42)?;
doc.add_vector_f32("embedding", &[0.1, 0.2, 0.3])?;

// Getters return `Result<Option<T>>` — `?` only unwraps the Result.
// Use `unwrap_or_default()` / `expect(..)` etc. to handle the Option.
let name: Option<String> = doc.get_string("name")?;
let count: Option<i64> = doc.get_i64("count")?;

Scalar Query

Use SearchQuery::scalar(topk) to filter scalar fields without a query vector, FTS payload, or target field. It also works on collections with no vector fields:

let mut query = SearchQuery::scalar(10)?;
query.set_filter("relative_path = 'src/main.rs'")?;
query.set_output_fields(&["relative_path"])?;
let results = collection.query(&query)?;

topk limits the number of returned documents, and their order is unspecified. A query without a filter returns up to topk documents; it does not enumerate all matches when there are more than that limit.

SearchQuery::builder() produces the same query when neither vector(..) nor an FTS clause is set; field_name(..) must then be omitted.

Vector Query

// Simple query
let query = SearchQuery::new("embedding", &query_vec, 10)?;

// Builder pattern with filters
let query = SearchQuery::builder()
    .field_name("embedding")
    .vector(&query_vec)
    .topk(10)
    .filter("category = 'tech'")
    .output_fields(&["id", "name"])
    .build()?;

The builder infers the query kind from what was set: vector(..) builds a dense vector query (adding an FTS clause makes it a hybrid search), an FTS clause without a vector builds a keyword-only query, and neither builds a scalar query. SearchQuery::fts(field, fts, topk) is the direct keyword-only constructor.

Multi-Query (Hybrid Search)

MultiQuery combines multiple sub-queries (dense vector, sparse vector, or FTS) with RRF or weighted reranking:

// FTS + vector hybrid search with RRF reranking
let mut sub_vec = SubQuery::new()?;
sub_vec.set_field_name("embedding")?;
sub_vec.set_query_vector(&query_vec)?;
sub_vec.set_num_candidates(50)?;

let mut fts = Fts::new()?;
fts.set_match_string("Rust vector database")?;

let mut sub_fts = SubQuery::new()?;
sub_fts.set_field_name("content")?;
sub_fts.set_fts(&fts)?;
sub_fts.set_num_candidates(50)?;

let mut mq = MultiQuery::new()?;
mq.set_topk(10)?;
mq.set_rerank_rrf(60)?;   // rank constant for RRF
mq.add_sub_query(&sub_vec)?;
mq.add_sub_query(&sub_fts)?;

let results = collection.multi_query(&mq)?;

// Weighted reranking
mq.set_rerank_weighted(&[0.7, 0.3])?;  // weights per sub-query

Supported Types

Index Types

Available distance metrics: L2, Ip, Cosine, MipsL2.

With Int8 / Int4 quantization, params.set_quantizer_enable_rotate(true) randomly rotates vectors before quantization to reduce the quantization error. The rotation matrix is stored with the index and applied to query vectors at search time.

Testing

# Using Makefile (recommended — auto-detects library paths)
make test-unit         # Unit tests (no C library required)
make test-integration  # Integration tests (requires C library)
make test-all          # All tests (unit + integration + doc)

# Using cargo directly (requires ZVEC_LIB_DIR / DYLD_LIBRARY_PATH)
cargo test --lib
cargo test --test integration_test

# Fuzz tests (requires nightly)
cargo install cargo-fuzz
cargo +nightly fuzz run fuzz_types -- -max_total_time=60

# Benchmarks
make bench

# Code coverage
cargo install cargo-llvm-cov
./scripts/coverage.sh --html

Keeping in Sync with zvec Core

This SDK tracks the zvec C-API. When the upstream C-API changes:

1. Update zvec-sys/src/lib.rs with new FFI declarations
2. Add safe wrappers in the zvec crate
3. Update integration tests to cover new functionality
4. Run the full test suite to verify compatibility

The CI pipeline automatically clones the latest zvec and builds the C library, ensuring FFI compatibility on every PR.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Ensure all tests pass (cargo test)
4. Ensure code is formatted (cargo fmt --all -- --check)
5. Ensure clippy is clean (cargo clippy --workspace --all-targets -- -D warnings)
6. Submit a pull request

License

Apache-2.0 — see LICENSE.