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README.md

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 # ACORN
-state-of-the-art search over vector embeddings and structured data (SIGMOD '24)
+ACORN is an index for state-of-the-art search over vector embeddings and structured data (SIGMOD '24)
+
+You can read more about our work in the paper:
+[**ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data**](https://dl.acm.org/doi/10.1145/3654923)
+
+This implementation of the ACORN index is built on [**The FAISS Library**](https://github.com/facebookresearch/faiss) in C++.
+
+## Installation
+```
+git clone https://github.com/stanford-futuredata/ACORN.git
+cmake -DFAISS_ENABLE_GPU=OFF -DFAISS_ENABLE_PYTHON=OFF -DBUILD_TESTING=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -B build
+make -C build -j faiss
+```
+
+
+## Example Usage
+1) Initialize the index
+```
+d=128;
+M=32; 
+gamma=12;
+M_beta=32;
+
+// ACORN-gamma
+faiss::IndexACORNFlat acorn_gamma(d, M, gamma, M_beta);
+
+// ACORN-1
+faiss::IndexACORNFlat acorn_1(d, M, 1, M*2);
+```
+2) Construct the index
+```
+size_t nb, d2;
+std::string filename = // your fvec file
+float* xb = fvecs_read(filename.c_str(), &d2, &nb);
+assert(d == d2 || !"dataset dimension is not as expected");
+acorn_gamma.add(nb, xb);
+```
+
+3) Search the index
+```
+// ... load nq queries, xb
+// ... load attribute filters as array aq
+
+std::vector<faiss::idx_t> nns2(k * nq);
+std::vector<float> dis2(k * nq);
+
+// create filter_ids_map to specify the passing entities for each predicate
+std::vector<char> filter_ids_map(nq * N);
+for (int xq = 0; xq < nq; xq++) {
+    for (int xb = 0; xb < N; xb++) {
+        filter_ids_map[xq * N + xb] = (bool) (metadata[xb] == aq[xq]);
+    }
+}
+
+// perform efficient hybrid search
+acorn_gamma.search(nq, xq, k, dis2.data(), nns2.data(), filter_ids_map.data());
+```