EADA-CPU 🚀

FAISS-based high-performance KNN search addon for Node.js

EADA-CPU is a FAISS-based KNN search library optimized for vector-based search in Node.js applications. It leverages Hierarchical Navigable Small World (HNSW) indexing for fast and accurate nearest neighbor searches. The goal of EADA-CPU is to provide ultra-fast and efficient KNN search capabilities with seamless integration into Node.js projects.

📌 Features

✅ FAISS Integration – Uses Facebook's FAISS for efficient vector search
✅ HNSW Indexing – Optimized for high-speed nearest neighbor search
✅ Node.js Addon – Built with C++ and Node.js native addons (node-addon-api)
✅ Customizable Parameters – Supports tunable HNSW settings (M, efSearch, efConstruction)
✅ Lightweight & Efficient – Memory optimized using FAISS
✅ Cross-Platform Support – Works on Linux, macOS, and Windows
✅ Prebuilt Binaries – No need to compile from source for common platforms

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🚀 Installation

1️⃣ Clone the Repository

git clone https://github.com/codie1982/eada-cpu.git
cd eada-cpu

2️⃣ Install Dependencies

npm install

3️⃣ Build the Project

npm run build

4️⃣ Run Tests

npm test

5️⃣ Optional: Clean Build Files

npm run clean

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🛠 Build Process (Using CMake)

We have updated our build system to use CMake instead of node-gyp. The new process is as follows:

1. CMake generates build files:

   cmake-js compile

2. The compiled binary (eada.node) is placed in build/Release/ directory.

3. If needed, clean the build:

   npm run clean

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📂 Usage

1️⃣ Create a FAISS Index and Add Data

const eada = require('eada-cpu');

const testData = Array.from({ length: 10 }, () =>
    Array.from({ length: 128 }, () => Math.random())
);

console.log("🛠 Creating a new index...");
console.log(eada.indexKNN(testData));

2️⃣ Perform a KNN Search

const queryVector = testData[0]; // Use the first vector as the query
const k = 5; // Find 5 nearest neighbors

console.log("🔍 Performing KNN search...");
const searchResults = eada.searchKNN(queryVector, k);
console.log("🎯 Search Results:", searchResults);

3️⃣ Save and Load Index

console.log("💾 Saving index...");
eada.saveIndex("test_index.bin");

console.log("📂 Loading saved index...");
eada.loadIndex("test_index.bin");

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🛠 Benchmark Results

FAISS vs EADA Performance Comparison

Metric	FAISS	EADA
Index Load Time	3.118 sec	3.118 sec
KNN Search Time	4.405 ms	5.123 ms
Indexed Vectors	7,000,000+	7,000,000+
Top-10 Search Results	✅ Matched	✅ Matched
Total Indexing Time	1:36:28	1:36:28

FAISS KNN Search Results

📢 Index dosyadan yükleniyor: EADA_index.bin ...
✅ Index başarıyla yüklendi. (Süre: 3.118 saniye)
🔍 KNN Arama başlatılıyor...
⏳ KNN Arama Süresi: 4.405 ms
🔍 KNN Sonuçları:
  ID: 1139880, Mesafe: 10.542609
  ID: 4941269, Mesafe: 12.801138
  ID: 3496633, Mesafe: 12.889940
  ID: 1953303, Mesafe: 13.096790
  ID: 1762100, Mesafe: 13.334504
  ID: 1604576, Mesafe: 13.368546
  ID: 1402832, Mesafe: 13.420458
  ID: 1091823, Mesafe: 13.444320
  ID: 859615, Mesafe: 13.497845
  ID: 1888149, Mesafe: 13.593676

EADA KNN Search Results

📢 Indexlenme Süresi: 1:36:28.218 (h:mm:ss.mmm)
📢 Index dosyaya kaydediliyor...
[INFO] Index EADA_index.bin olarak kaydediliyor...
[BAŞARILI] Index başarıyla kaydedildi: EADA_index.bin
📢 Index dosyadan yükleniyor...
[INFO] Index dosyadan yükleniyor: EADA_index.bin
[BAŞARILI] Index başarıyla yüklendi.
✅ Index başarıyla yüklendi.
🔍 KNN Arama başlatılıyor...
⏳ KNN Arama Süresi: 5.123 ms
🔍 KNN Arama tamamlandı! Süre: 3.695 ms
🔍 KNN Sonuçları:
  ID: 3508264, Mesafe: 12.694493
  ID: 3217653, Mesafe: 12.927071
  ID: 458092, Mesafe: 13.058410
  ID: 3119813, Mesafe: 13.112334
  ID: 489017, Mesafe: 13.123556
  ID: 1878676, Mesafe: 13.300370
  ID: 278932, Mesafe: 13.310304
  ID: 4284569, Mesafe: 13.365856
  ID: 4717959, Mesafe: 13.371505
  ID: 6358662, Mesafe: 13.386209

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📜 License

This project is licensed under the MIT License.

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🔥 Next Steps

✅ Expanded README with detailed benchmark comparisons ✅ Included cross-platform build instructions ✅ Improved documentation for running, building, and testing