RaBitQ-Library is a research-backed library for vector quantization and approximate nearest-neighbor search. See the research papers.
News
- v0.7.0: Modular C++ components, stable core API v1, optional OpenMP for numerical components, and unified index counts. This documentation targets 0.7.0; see the upgrade notes.
Vector indexes
Use the included indexes when you want construction, search, and save/load in one library. All three support L2 and inner product; normalize vectors for cosine search. Their structures, storage costs, and search parameters offer different QPS–recall tradeoffs.
| Index | Search approach | Storage and updates |
|---|---|---|
| IVF-RaBitQ | Partitioned search with batch scoring. | 1–9-bit codes or raw-vector reranking; add/remove. |
| HNSW-RaBitQ | Graph traversal over quantized vectors. | 1–9-bit codes plus graph edges; add/remove and resize. |
| SymphonyQG | Graph search with batched distance estimation. | Raw or 4/8-bit storage; updates require rebuilding. |
Native clustering methods prepare centroids and assignments for IVF-RaBitQ and HNSW-RaBitQ.
Search throughput and index size for three quantized configurations. QPS uses the fastest measured setting reaching Recall@10 ≥ 90%; actual recall and storage budgets differ.
GIST1M
1,000,000 vectors · 960 dimensions · L2
Detailed results, settings, and measurement conditions.
Quick start
Python
python -m pip install --upgrade "rabitqlib>=0.7.0"
Wheels support CPython 3.11–3.14 on Linux x86-64/ARM64, Windows x86-64, and macOS 14+ ARM64. x86-64 requires AVX2/FMA and optionally uses AVX-512; ARM64 uses NEON. See platform requirements and source installation.
Build and search a small IVF-RaBitQ index with synthetic data; no dataset download is required. This example demonstrates the API, not performance.
import numpy as np
from rabitqlib import FinalAssignmentMode, IvfIndex, RaBitQKMeans
rng = np.random.default_rng(42)
data = rng.standard_normal((500, 64)).astype(np.float32)
queries = rng.standard_normal((5, 64)).astype(np.float32)
clustering = RaBitQKMeans(
64, 5, num_threads=2, final_assignment=FinalAssignmentMode.Exact
)
clustering.train(data)
index = IvfIndex(dim=64, max_elements=len(data), num_clusters=5, nbits=4, metric="l2")
index.build(data, clustering.centroids, clustering.assignments)
ids, distances = index.search(queries, k=10, nprobe=5)
print(ids.shape, distances.shape) # (5, 10) (5, 10)
IDs identify input rows; distances here estimate squared L2 distance, with smaller
values first. Quantization remains approximate even when all clusters are probed.
The complete quick start explains result handling,
measures recall while varying nprobe, and shows save/load.
For other indexes, see the HNSW-RaBitQ and SymphonyQG examples.
C++
Use the C++ integration guide for source builds, component linking, and installation. Start with the C++ index examples, or use the quantization components below in your own engine.
Quantization components
Integrate encoding and distance estimation into your own storage and search engine, independently of the included indexes.
| Component | What it provides | Start here |
|---|---|---|
rabitqlib::core |
Rotation, per-vector encoding, query preparation, and distance estimation. | Core example · Stable v1 API |
rabitqlib::fastscan |
Batch encoding and lookup-table scoring over 32-vector layouts, built on core. | FastScan example |
See component integration for build options and compatibility, the quantizer guide for encoding choices, and the core benchmark for component throughput and estimation error.
Adoption and resources
Built with RaBitQ-Library
Two examples of direct library integration:
| Project | Integration |
|---|---|
| txtai | Uses the Python IVF-RaBitQ and HNSW-RaBitQ indexes as an embedding-search backend. |
| zvec | Uses core quantization in its own IVF-RaBitQ and HNSW-RaBitQ indexes, with FastScan batch scoring in IVF-RaBitQ. |
The linked walkthroughs describe integration boundaries and provide versioned source references.
RaBitQ across the vector-search ecosystem
The projects below illustrate adoption of RaBitQ techniques across vector search. They do not necessarily depend on RaBitQ-Library; direct library integrations are listed above.
 Milvus |
 Faiss |
 NVIDIA cuVS |
 Microsoft DiskANN |
 VSAG |
 VectorChord |
 Volcengine OpenSearch |
 CockroachDB |
 Elasticsearch |
 Apache Lucene |
 turbopuffer |
 Zvec |
 LanceDB |
 Databricks |
 ClickHouse |
 Qdrant |
 Weaviate |
Further reading
- Documentation and algorithm overview.
- Papers and citations for the research behind the library.
- Release notes and 0.7 migration guide.
- GPU implementation in cuVS.
Contributing
Start with the contribution guide or starter tasks. Report bugs and request features through GitHub Issues. See maintenance and feedback for maintainer information.
Acknowledgements
RaBitQ Library is developed by Yutong Gou, Jianyang Gao, Yuexuan Xu, Jifan Shi, and Zhonghao Yang. We thank Alexandr Guzhva, Li Liu, Chao Gao, Silu Huang, Jiabao Jin, Xiaoyao Zhong, and Jinjing Zhou for their valuable feedback.
License
RaBitQ Library is available under the Apache License 2.0.
1 maintainer
Metadata
1 maintainer
Release files for rabitqlib 0.7.1
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