brinicle

brinicle is a C++ vector index engine (ANN library) optimized for disk-first, low-RAM similarity search.

pip install brinicle

What is brinicle?

Disk-optimized vector search engine for high-performance similarity search

brinicle is a high-performance C++ vector index engine (ANN library) optimized for disk-first, low-RAM similarity search. It provides fast build + query operations, supports inserts/upserts/deletes, and targets predictable latency at high recall with minimal memory overhead on constrained environments.

Designed for production workloads, brinicle excels in scenarios where memory is limited but disk storage is abundant. Whether you're building recommendation systems, semantic search applications, or similarity matching at scale, brinicle offers the perfect balance between performance, resource efficiency, and operational simplicity.

Learn More About BicardinalView Benchmark Results

How to Use brinicle

Get started with brinicle using these code examples. Build, insert, update, and delete vectors in your index.

Pythonusage.py
import numpy as npimport brinicleD = 2n = 5X = np.random.randn(n, D).astype(np.float32)Q = np.random.randn(D).astype(np.float32)engine = brinicle.VectorEngine("test_index", dim=D, delta_ratio=0.1)engine.init(mode="build")for eid in range(n):    engine.ingest(str(eid), X[eid])engine.finalize()print(engine.search(Q, k=10)) # returns a list of ids

Benchmark Results

See how brinicle compares to vector databases and ANN libraries in real-world scenarios

Memory Efficiency
Optimized for resource-constrained environments
256MB
Minimum RAM Required
224.95 MB
Search Peak
982.94 MB
1M Vectors

Runs efficiently in tight containers and edge devices

Performance Metrics
Best-in-class latency and throughput
Average Latency0.927ms
P95 Latency1.705ms
P99 Latency2.266ms
Throughput1,086 QPS

Fashion-MNIST (60K vectors), 512MB RAM, 2 CPU

View Complete Benchmark Results

Compare with Qdrant, Weaviate, Milvus, Chroma, FAISS, and hnswlib

About Bicardinal

Building production-oriented vector search solutions for resource-constrained environments

Index Engine
brinicle is an index engine, not a full vector database. You embed it in your service or pair it with your own metadata store.
Disk-First Design
Optimized for disk-first operation with low memory overhead. Perfect for tight containers, edge machines, and low-cost instances.
Production Ready
Designed for datasets under 10M vectors with core lifecycle operations: build/load, search, insert/upsert/delete, and rebuild.
Learn More About Bicardinal