PipeANN

PipeANN is 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).

🚀 Quick Start¶

Requirements¶

Component	Requirement
CPU	x86 or ARM with SIMD (AVX2/AVX512 recommended)
DRAM	~40GB (search) or ~90GB (search+update) per billion vectors
SSD	~700GB for 1B SIFT, ~900GB for 1.4B SPACEV

OS: Linux with io_uring (fast) or libaio (compatible) support, Ubuntu 22.04 with Kernel 6.8 recommended
Compiler: C++17 support required
Constraints: <2B vectors to avoid integer overflow
RAID users: Use Kernel > 6.1 for better io_uring performance (liburing#792), or use SPDK backend.

Build¶

Install dependencies:

# Ubuntu >= 22.04
sudo apt install make cmake g++ libaio-dev libgoogle-perftools-dev \
                 clang-format libmkl-full-dev libeigen3-dev

# For Python interface
pip3 install "pybind11[global]"

libmkl could be replaced by other BLAS libraries (e.g., openblas).

Build liburing:

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

Build PipeANN:

# For Python users: editable install of the Python package
pip install -e .

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

C++ Interface¶

# 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, updates, and filtered / OOD search, and SPDK Backend to use SPDK as I/O engine (fastest).

Python Interface¶

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.

Application Integrations¶

PipeANN provides LangChain-compatible vector store and Qdrant-compatible HTTP API for seamless integration with existing applications (e.g., Open WebUI).

See Application Integrations for details.

📰 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

📖 Citation¶

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

@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}
}

🙏 Acknowledgments¶

PipeANN is based on DiskANN and FreshDiskANN. We sincerely appreciate their excellent work.