PipeANN

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

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<h3 align="center">
A low-latency, billion-scale, and updatable graph-based vector store on SSD.
</h3>

## ✨ 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** | Milvus-compatible API (drop-in MilvusClient + gRPC server) |
| 🗄️ **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).

## 🔌 Already on Milvus? Switch in one line

PipeANN speaks the **Milvus API**. Point your existing code at PipeANN and keep everything else the same — no rewrite, no new SDK.

```python
# In-process, drop-in for pymilvus.MilvusClient (URI is a directory):
from pipeann import MilvusClient
client = MilvusClient(uri="./pipeann-data")

# Or talk to the PipeANN gRPC server with the stock pymilvus client:
from pymilvus import MilvusClient
client = MilvusClient(uri="http://localhost:19530")   # PipeANN server, same wire protocol

client.create_collection("demo", dimension=128, metric_type="L2")
client.insert("demo", [{"id": 1, "vector": [0.1] * 128, "color": "red"}])
client.create_index("demo")   # builds the SSD graph; lazy/no-op for the in-process client
client.search("demo", data=[[0.1] * 128], filter="color == 'red'", limit=5)
```

You get PipeANN's on-disk, larger-than-RAM index and speculative filtering behind the API you already use. See [Application Integrations](docs/application-integrations.md) for the full guide and Milvus-vs-PipeANN benchmarks.

---

## 🚀 Quick Start

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

### 🏗️ Build

Install dependencies:

```bash
# 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

# gRPC, Protobuf, and RocksDB — required by the Milvus-compatible layer
# (the gRPC server *and* the in-process MilvusClient, which share the same
# RocksDB-backed collection engine). The Python module links RocksDB, so
# these are needed even if you only use the Python interface.
sudo apt install libgrpc++-dev protobuf-compiler-grpc libprotobuf-dev \
                 protobuf-compiler librocksdb-dev

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

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

Build PipeANN:

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

# For Python users: build and install the Python interface.
# This also builds and bundles the Milvus-compatible gRPC server binary,
# so `pipeann-server` is available straight after install.
pip install -e .
```

#### Milvus-compatible gRPC server

`pip install -e .` already builds and bundles the `pipeann_milvus_server`
binary into the package (`pipeann/_bin/`), so you can launch it directly:

```bash
pipeann-server --data_dir ./data --port 19530 --threads 8
```

To build just the C++ server target on its own (without the Python interface):

```bash
cmake -B build -DBUILD_MILVUS_SERVER=ON . && cmake --build build --target pipeann_milvus_server
```

See [Application Integrations](docs/application-integrations.md) for running and connecting to the server.

### ⚡ C++

```bash
# 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](docs/cpp-interface.md) for index building, index updates, and filtered / OOD search, 
and [SPDK Backend](docs/cpp-interface-spdk.md) to use SPDK as I/O engine (fastest).

### 🐍 Python

```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](docs/python-interface.md#python-interface) for the full API, including filtered / OOD search and example output.

See [Application Integrations](docs/application-integrations.md) for the Milvus-compatible API (in-process and gRPC server).

## 📰 Updates

- **May 26, 2026**: Filter config unified around SQL-like expressions with `$$var` placeholders for batch binding; legacy selector-config loader removed
- **May 18, 2026**: SPDK backend supported, stable tail latency with better multi-SSD scalability
- **May 18, 2026**: Filtered Search (Speculative Filtering), OOD search ([NGFix](https://dl.acm.org/doi/abs/10.1145/3769783)) & range search supported
- **May 18, 2026**: PipeANN is integrated into OdinANN (search + insert), higher performance with less threads
- **Mar 27, 2026**: [PiPNN](http://arxiv.org/abs/2602.21247) indexing algorithm supported
- **Dec 4, 2025**: Inner product and filtered ANNS (*arbitrary filter*) supported
- **Oct 14, 2025**: [RaBitQ](https://github.com/VectorDB-NTU/RaBitQ-Library) (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:

```bibtex
@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](docs/repository-layout.md) for code layout and scripts.

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