This library is not recommended for use and is currently under development.

omni_search

omni_search is a Rust SDK for multimodal embedding and similarity search over local ONNX model directories.

Current scope:

• load a flat local model directory with root-level model_config.json;
• compute text embeddings;
• compute image embeddings;
• compare embeddings with cosine similarity;
• expose runtime snapshots with requested device, planned providers, registered providers, and effective provider;
• manually unload ONNX Runtime sessions.

Supported families:

• chinese_clip
• fg_clip
• open_clip

The published crate does not bundle ONNX models or sample images. Point it at your own local assets with OMNI_BUNDLE_DIR and OMNI_SAMPLES_DIR.

The CLI binaries automatically load a repo-root .env file when present. Existing shell environment variables still win, so .env works as a local default layer.

Quickstart:

• create a .env from .env.example or edit the existing local .env defaults when you want to pin a bundle, sample directory, or test fixtures;
• set OMNI_BUNDLE_DIR to a local model directory that contains model_config.json plus flat root-level assets;
• set OMNI_SAMPLES_DIR to a directory containing one or more .jpg, .jpeg, .png, .webp, or .bmp images;
• build with cargo build --features avif when you need .avif inputs; the avif feature is opt-in and enables AVIF decode support;
• on Windows, the avif feature additionally requires a native dav1d library; provide it through pkg-config or set SYSTEM_DEPS_DAV1D_NO_PKG_CONFIG=1, SYSTEM_DEPS_DAV1D_SEARCH_NATIVE, and SYSTEM_DEPS_DAV1D_LIB;
• see docs/avif-setup.md for platform-specific avif feature setup on Windows, Linux, and macOS;
• build SDK instances with OmniSearch::builder() when you only want to override part of the runtime config and keep the rest at defaults;
• run cargo run --bin omni_search --release to scan all images in OMNI_SAMPLES_DIR with the default query "山";
• run cargo run --bin omni_search --release -- "海边" to scan all images with a custom query;
• run cargo run --bin omni_search --release -- "海边" 20 to print a different top-k;
• run cargo run --bin omni_search --release -- "海边" "/absolute/path/to/query.jpg" to run image_to_image with a specific query image;
• set OMNI_DEVICE to auto, cpu, or gpu to control execution provider selection; default is auto;
• set OMNI_PROVIDER_POLICY to auto, interactive, or service when you want to change GPU provider priority without pinning a single provider;
• call sdk.runtime_snapshot() when you need to inspect or display the current execution provider state;
• set OMNI_ORT_DYLIB_PATH when you build with runtime-dynamic and want to point omni_search at a specific onnxruntime.dll / .so / .dylib;
• set OMNI_ORT_PROVIDER_DIR, OMNI_CUDA_BIN_DIR, OMNI_CUDNN_BIN_DIR, and OMNI_TENSORRT_LIB_DIR when you need omni_search to preload or register NVIDIA provider libraries from explicit directories;
• set OMNI_PRELOAD_RUNTIME_LIBRARIES=false when you want to disable eager runtime DLL preloading while still keeping the path hints in config;
• the default RuntimeConfig::intra_threads value also resolves to the host physical core count;
• set OMNI_INTRA_THREADS to auto or a positive integer to override the ONNX Runtime intra-op thread count; auto resolves to the host physical core count;
• set OMNI_INTER_THREADS to a positive integer when you need to override the ONNX Runtime inter-op thread count while benchmarking or tuning;
• set OMNI_FGCLIP_MAX_PATCHES to cap FG-CLIP2 image preprocessing at a smaller bucket without changing the exported model directory;
• recommended OMNI_FGCLIP_MAX_PATCHES values are 128, 256, 576, 784, or 1024;
• run cargo test --test quickstart -- --ignored --nocapture to execute the smoke test after setting OMNI_TEST_BUNDLE_DIR and OMNI_TEST_SAMPLE_IMAGE.

Example .env:

OMNI_DEVICE=auto
OMNI_PROVIDER_POLICY=auto
OMNI_BUNDLE_DIR=models/fgclip2_flat
OMNI_SAMPLES_DIR=samples

Device selection notes:

• all current model families load standard ONNX graphs, so GPU support is determined by the ONNX Runtime execution provider rather than by a model-specific code path;
• the default build is runtime-bundled + directml + coreml, which keeps Windows DirectML and Apple CoreML enabled while still using the bundled ONNX Runtime loading mode;
• enable cargo build --features nvidia when you want TensorRT -> CUDA ahead of the platform fallback provider on supported Windows/Linux x64 targets;
• build with cargo build --no-default-features --features runtime-dynamic,directml,nvidia when you want a Windows/NVIDIA variant that uses system-provided ONNX Runtime, CUDA, cuDNN, and TensorRT libraries instead of bundling them into the application;
• OMNI_PROVIDER_POLICY=service prefers steady-state throughput and currently tries TensorRT -> CUDA -> DirectML -> CPU on Windows with --features nvidia;
• OMNI_PROVIDER_POLICY=interactive prefers lower warmup cost and currently tries CUDA -> DirectML -> TensorRT -> CPU on Windows with --features nvidia;
• OMNI_FORCE_PROVIDER remains available as a diagnostics-only override when you need to pin one execution provider for benchmarking or debugging;
• on Apple Silicon/macOS, gpu is wired to the CoreML execution provider;
• on Linux, the current crate build does not yet wire a GPU provider; AMD GPU support would require a dedicated ROCm or WebGPU path;
• auto first tries the configured GPU chain and falls back to CPU if acceleration is unavailable;
• gpu requires at least one GPU execution provider to register successfully; it does not silently fall back to CPU;
• runtime_snapshot() now separates compiled_providers, planned_providers, registered_providers, and issues, so upper layers can distinguish feature-gated providers from missing runtime libraries or dependency-chain failures;
• the Windows build output includes DirectML.dll; application packaging should ship that file with the executable.

Legacy migration:

python .\scripts\flatten_bundle_to_flat.py --input .\models\chinese_clip_bundle --mode hardlink
python .\scripts\flatten_bundle_to_flat.py --input .\models\fgclip2_bundle --mode hardlink

Direct exporters:

The exporter scripts live in D:\code\vl-embedding-test and default to writing flat model directories into D:\code\omni_search\models.

uv run D:\code\vl-embedding-test\export_openclip_flat.py --id timm/MobileCLIP2-S2-OpenCLIP --output D:\code\omni_search\models\mobileclip2 --force
uv run D:\code\vl-embedding-test\export_chinese_clip_flat.py --model-dir D:\models\chinese-clip-vit-base-patch16 --output D:\code\omni_search\models\chinese_clip_flat --force
uv run D:\code\vl-embedding-test\export_fgclip2_flat.py --model-dir D:\models\fg-clip2-base --output D:\code\omni_search\models\fgclip2_flat --force

Builder example:

use omni_search::{
    GraphOptimizationLevel, OmniSearch, ProviderPolicy, RuntimeDevice, SessionPolicy,
};

let sdk = OmniSearch::builder()
    .from_local_model_dir("D:/models/fgclip2_flat")
    .device(RuntimeDevice::Auto)
    .provider_policy(ProviderPolicy::Service)
    .provider_dir("D:/onnxruntime/lib")
    .cuda_bin_dir("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.6/bin")
    .cudnn_bin_dir("D:/runtime/cudnn/bin")
    .tensorrt_lib_dir("D:/runtime/tensorrt/lib")
    .intra_threads(4)
    .fgclip_max_patches(256)
    .session_policy(SessionPolicy::SingleActive)
    .graph_optimization_level(GraphOptimizationLevel::All)
    .build()?;

let snapshot = sdk.runtime_snapshot();
println!("mode: {:?}", snapshot.summary.mode);
println!("provider: {:?}", snapshot.summary.effective_provider);
println!("compiled: {:?}", snapshot.text_session.compiled_providers);
println!("issues: {:?}", snapshot.text_session.issues);