A low-latency, billion-scale, and updatable graph-based vector store on SSD.

✨ Key Features

Feature	Description
⚡ Ultra-Low Latency	<1ms for 1 billion vectors (top-10, 90% recall), only 1.14x-2.02x of in-memory index
📈 High Throughput	20K QPS for 1 billion vectors, outperforming DiskANN and SPANN
🔄 Efficient Updates	Insert/delete with minimal search interference (1.07x fluctuation)
🎯 Speculative Filtering	3K QPS & 6ms latency for attribute-filtered ANNS on 100 million vectors
💾 Memory Efficient	>10x less memory than in-memory indexes (~40GB for 1B vectors)
🐍 Easy-to-Use	Both Python (faiss-like) and C++ interfaces supported
🔌 Seamless Integration	LangChain- and Qdrant-compatible APIs for easy integration
🗄️ Multi-SSD Scaling	Scales to 70K QPS & 2ms tail latency on 1B vectors (4 SSDs, SPDK backend)

📊 Performance Comparison

PipeANN is suitable for both large-scale and memory-constraint scenarios.

Dataset	Dimension	Memory	Latency	QPS	PipeANN	HNSW	DiskANN
1B (SPACEV)	100	40GB	2ms	5K	✅	❌ 1TB mem	❌ 6ms
80M (Wiki)	768	10GB	1.5ms	5K	✅	❌ 300GB mem	❌ 4ms
10M (SIFT)	128	550MB	<1ms	10K	✅	❌ 4GB mem	❌ 3ms

Recall@10 = 0.99, Samsung PM9A3 SSD, 32B PQ-compressed vectors (128B for Wiki).

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🚀 Quick Start

For best performance, we recommend Linux with io_uring support (e.g., Ubuntu 22.04 with Kernel 6.8).

🏗️ Build

Install dependencies:

# Ubuntu >= 22.04
# libmkl could be replaced by other BLAS libraries (e.g., openblas).
sudo apt install make cmake g++ libaio-dev libgoogle-perftools-dev \
                 clang-format libmkl-full-dev libeigen3-dev

# For Python interface
pip install "pybind11[global]"

# Build liburing
cd third_party/liburing
./configure && make -j
cd ../..

Build PipeANN:

# For C++ users: build C++ binaries under build/
bash ./build.sh

# For Python users: build and install the Python interface
pip install -e .

⚡ C++

# Search an existing on-disk index with PipeANN pipelined search
build/tests/search_disk_index uint8 index_prefix 1 32 query.bin gt.bin 10 l2 pq 2 10 10 20 30 40

See C++ Interface for index building, index updates, and filtered / OOD search, and SPDK Backend to use SPDK as I/O engine (fastest).

🐍 Python

from pipeann import IndexPipeANN, Metric

idx = IndexPipeANN(data_dim=128, data_type='float32', metric=Metric.L2)
idx.omp_set_num_threads(32)
idx.set_index_prefix(index_prefix)
idx.add(vectors, tags)                          # insert (auto disk-convert at 100K)
ids, dists = idx.search(queries, topk=10, L=50) # search
idx.save(index_prefix)                          # persist

See Python Interface for the full API, including filtered / OOD search and example output.

See Application Integrations for integration with LangChain, Qdrant, and Open WebUI.

📰 Updates

• May 18, 2026: SPDK backend supported, stable tail latency with better multi-SSD scalability
• May 18, 2026: Filtered Search (Speculative Filtering), OOD search (NGFix) & range search supported
• May 18, 2026: PipeANN is integrated into OdinANN (search + insert), higher performance with less threads
• Mar 27, 2026: PiPNN indexing algorithm supported
• Dec 4, 2025: Inner product and filtered ANNS (arbitrary filter) supported
• Oct 14, 2025: RaBitQ (1-bit and multi-bit quantization) supported
• Sep 29, 2025: Python interface released
• Jul 16, 2025: Vector update (insert/delete) supported

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📖 Citation

If you use PipeANN in your research, please cite our papers:

@misc{arxiv26pipeannfilter,
      title={PipeANN-Filter: An Efficient Filtered Vector Search System on SSD}, 
      author={Hao Guo and Jiwu Shu and Youyou Lu},
      year={2026},
      eprint={2605.17992},
      archivePrefix={arXiv},
      primaryClass={cs.OS},
      url={https://arxiv.org/abs/2605.17992}, 
}

@inproceedings{fast26odinann,
  author    = {Hao Guo and Youyou Lu},
  title     = {OdinANN: Direct Insert for Consistently Stable Performance 
               in Billion-Scale Graph-Based Vector Search},
  booktitle = {24th USENIX Conference on File and Storage Technologies (FAST 26)},
  year      = {2026},
  address   = {Santa Clara, CA},
  pages     = {133--147},
  publisher = {USENIX Association}
}

@inproceedings{osdi25pipeann,
  author    = {Hao Guo and Youyou Lu},
  title     = {Achieving Low-Latency Graph-Based Vector Search via 
               Aligning Best-First Search Algorithm with SSD},
  booktitle = {19th USENIX Symposium on Operating Systems Design and Implementation (OSDI 25)},
  year      = {2025},
  address   = {Boston, MA},
  pages     = {171--186},
  publisher = {USENIX Association}
}

See Repository Layout for code layout and scripts.