license: mit library_name: custom pipeline_tag: feature-extraction tags:

• sentence-embeddings
• text-embeddings
• rust

auto-g-embed

Local semantic embedding pipeline with a Rust-native runtime.

What this repo provides

• Contrastive dataset preparation (prepare_contrastive)
• Rust-native embedder training (train_rust_embedder)
• Runtime embedding APIs and examples
• Optional ONNX/SentenceTransformer path in training/

Quick start

cargo test

./training/run_pipeline.sh \
  --profile kaggle_questions_million \
  --source-csv data/kaggle/one-million-reddit-questions.csv

Run the Rust embedding example:

cargo run --example rust_embed -- \
  artifacts/model/rust-embedder \
  "A quick test sentence for semantic embeddings."

Model artifacts

Published model artifacts are available on Hugging Face:

• https://huggingface.co/geoffsee/auto-g-embed

Project layout

• src/: library modules and binaries
• examples/: runnable embedding demos
• tests/: integration/performance tests
• training/: pipeline scripts and dataset adapters

Development checks

cargo fmt --all -- --check
cargo clippy --all-targets --all-features -- -D warnings
cargo test

Community Benchmark

Run the reproducible benchmark CLI:

cargo run --release --bin community_benchmark -- \
  --output artifacts/benchmarks/latest.json

The output includes throughput, latency percentiles (p50/p95/p99), retrieval quality metrics, and environment metadata for publishing. Methodology and reporting guidance: BENCHMARKS.md.

Latest Benchmark (M4 Max) (February 8, 2026):

cargo run --release --bin community_benchmark -- \
  --eval-count 500 --warmup-count 100 --query-count 32 \
  --output artifacts/benchmarks/smoke.json

• embeds_per_second: 219595.18
• p50_us: 3.88
• p95_us: 6.54
• p99_us: 6.71
• top1_accuracy: 0.9375
• separation: 0.2886

Comparison Chart

Notes:

• N/R means not reported in the source.
• External numbers are from different hardware and workloads, so this is directional and not an apples-to-apples benchmark.
• Source links:
  • NVIDIA NIM performance tables: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/performance.html
  • SwiftEmbed paper: https://arxiv.org/abs/2510.24793

Additional docs

• Training and pipeline details: training/README.md
• Test data notes: test-data/README.md